Patent Publication Number: US-11399040-B1

Title: Subscription-based malware detection

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 15/940,410 filed Mar. 29, 2018, now U.S. Pat. No. 10,791,138 issued on Sep. 29, 2020 which claims the benefit of priority on U.S. Provisional Application No. 62/479,208 filed Mar. 30, 2017 and U.S. Provisional Application No. 62/523,121 filed Jun. 21, 2017, the entire contents of which are incorporated by reference. 
    
    
     FIELD 
     Embodiments of the disclosure relate to the field of cybersecurity. More specifically, one embodiment of the disclosure relates to a scalable, subscription-based malware detection system. 
     GENERAL BACKGROUND 
     Cybersecurity attacks have become a pervasive problem for organizations as many networked devices and other resources have been subjected to attack and compromised. An attack constitutes a threat to security of stored or in-transit data that may involve the infiltration of malicious software (i.e., “malware”) onto a network device with the intent to perpetrate malicious or criminal activity or even a nation-state attack. 
     Recently, malware detection has undertaken many approaches involving network-based, malware protection services. One approach involves “on-site” placement of dedicated malware detection appliances at various ingress points throughout a network or subnetwork. Each of the malware detection appliances is configured to extract information propagating over the network at an ingress point, analyze the information to determine a level of suspiciousness, and conduct an analysis of the suspicious information internally within the appliance itself. While successful in detecting advanced malware that is attempting to infect network devices connected to the network (or subnetwork), as network traffic increases, an appliance-based approach may exhibit a decrease in performance due to resource constraints. 
     In particular, a malware detection appliance has a prescribed (and finite) amount of resources (for example, processing power) that, as resource use is exceeded, requires either the malware detection appliance to resort to more selective traffic inspection or additional malware detection appliances to be installed. The installation of additional malware detection appliances requires a large outlay of capital and network downtime, as information technology (IT) personnel are needed for installation of these appliances. Also, dedicated, malware detection appliances provide limited scalability and flexibility in deployment. 
     An improved approach that provides scalability, reliability, and efficient and efficacious malware detection at lower capital outlay is desirable. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the invention are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which: 
         FIG. 1  is a block diagram of a first exemplary embodiment of a scalable, malware detection system. 
         FIG. 2  is a block diagram of an exemplary embodiment of logic implemented within a sensor deployed within the malware detection system of  FIG. 1 . 
         FIG. 3  is an exemplary embodiment of a cluster implemented within the object evaluation service hosted by the second subsystem of the malware detection system of  FIG. 1 . 
         FIG. 4  is an exemplary embodiment of a compute node being part of the cluster of  FIG. 3 . 
         FIGS. 5A-5B  are an exemplary flowchart of the general operations performed by the malware detection system of  FIG. 1 . 
         FIG. 6A  is an embodiment of the operational flow conducted by the malware detection system of  FIG. 1  in establishing communications with on-site sensors. 
         FIG. 6B  is an embodiment of the operational flow between the sensors and the subscription review service of  FIG. 1 . 
         FIG. 7  is an exemplary embodiment of the analysis selection service of  FIG. 1 , including the cloud broker and the system monitoring logic. 
         FIG. 8  is a block diagram of a second exemplary embodiment of a scalable, malware detection system. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present disclosure generally relate to a subscription-based malware detection system, which includes a first subsystem and a second subsystem. Herein, the first subsystem may provide multi-tenancy through a cloud-based service that connects any number of subscribers to an object evaluation service, which is hosted by the second subsystem that is remotely located from the first subsystem. This multi-tenant, cloud-based service allows multiple subscribers to concurrently provide objects to the object evaluation service for malware detection. 
     Herein, a “subscriber” may be interpreted as a customer (e.g., an individual or an organization being a group of individuals operating within the same or different company, governmental agency, department or division, etc.) with authorized access to the malware detection system. According to embodiments of the invention, the subscriber deploys one or more devices (e.g., sensor), which, after credential checks, may gain authorized access to the malware detection system via the first subsystem. “Multi-tenancy” refers to a system architecture in which a single IT resource (in this case, the malware detection system), can serve multiple tenants (customers), each with shared access with specific privileges to the resource. Implementations of the invention are designed with multi-tenancy controls to enforce per-tenant segregation of sensitive data and metadata to avoid its access by other tenants, while achieving statistical data aggregation benefits, as well as scalability, reliability, and efficient and efficacious malware detection at lower capital outlay. Also, in embodiments of the invention, per-tenant controls of the malware detection system and its object evaluation service are achieved based on subscription information, which may include subscription attributes, customer-configured attributes, factory set attributes, and/or operationally dynamically generated attributes, as described below. 
     In general, the object evaluation service includes one or more clusters (referred to as “cluster(s)”) for use in analyzing objects provided by one or more sensors for malware and a cluster management system that monitors the operations of each cluster and controls its configuration. The cluster includes at least a broker compute node, as described below. Deployed as a physical logic unit (e.g. a network device) or as a virtual logic unit (e.g., software operating on a network device), each sensor is configured to capture network traffic, including objects, perform a preliminary analysis on the objects, and provide objects deemed “suspicious” (e.g., meet or exceed a first probability of the object under analysis being malicious) to a selected cluster for in-depth analysis of the objects. A customer may subscribe to the malware detection system in order to utilize the object evaluation services through data submissions via one or more sensors as described below. 
     The second subsystem of the malware detection system may further include a subscription review service, which is configured to store a portion of the subscription information (referred to as “service policy level information”) for use in selection of a cluster to analyze submitted objects and monitor operability of the selected cluster to confirm compliance with certain performance-based attributes. The performance-based attributes may pertain to any category of attribute such as certain subscription attributes (e.g., number or rate of object submissions, Quality of Service “QoS” levels guaranteed by a subscription tier, cluster availability, etc.) or certain customer-configured attributes (e.g., geographic location permissions or restrictions for compute nodes with the selected cluster in processing objects, type of remediation scheme, type of notification “alert” scheme, etc.). 
     According to one embodiment of the disclosure, the service policy level information includes an identifier to a customer (referred to as “Customer_ID”), received in response to a granted request to subscribe to the object evaluation service. Some or all of the service policy level information may be provided to (i) a sensor, (ii) management system or web portal associated with the sensor, and/or (iii) a data store (e.g., one or more databases) that is accessible by one or more cloud brokers, as described below. During operation, the sensor communicates with the subscription review service to enroll and gain authorized access to the malware detection system. The subscription review service further coordinates an exchange of information with the cluster management system for updating software or other logic operating within one or more compute nodes (referred to as “compute node(s)”) within the cluster(s) of the object evaluation service and/or within one or more sensors (referred to as “sensor(s)”) in communication with the malware detection system. 
     According to one embodiment of the disclosure, the first (cloud-based) subsystem of the malware detection system features (i) an analysis selection service and (ii) an analysis monitoring service. The analysis selection service includes logic, referred to as a “cloud broker,” which is responsible for both selecting a cluster to analyze objects that are submitted by a particular customer via a sensor and monitoring operability of the selected cluster to ensure compliance with the performance-based attributes e.g., associated with the subscription level selected by the customer. In particular, the analysis monitoring service is configured to communicate with the cluster management system to receive metadata associated with the cluster(s) operating as part of the second subsystem and/or metadata associated with compute nodes within the cluster(s). The metadata may include performance-based information (e.g., capacity, rate of analyses, number of analyses conducted, guest images utilized, etc.), derived from historical operational statistics and current status of the clusters. Based on this metadata, the analysis monitoring service generates information (referred to as “cluster selection values”) for use, at least in part, by the cloud broker in selecting a cluster to process objects from a specific sensor and determining compliance with performance and/or operation thresholds for the tier of subscription selected. 
     More specifically, the analysis monitoring service includes logic, referred to as a “system monitoring logic,” which is responsible for collecting metadata from the cluster management system that pertains to the operating state of (a) sensor(s) at a subscriber site, (b) cluster(s) that are part of the second subsystem, and/or (c) compute node(s) of a particular cluster or clusters. According to one embodiment of the disclosure, this metadata (referred to as “operational metadata”) may include, but is not limited or restricted to, any or all of the following: cluster-based metadata, subscriber-based metadata, and/or compute node (CN)-based metadata (when the cluster management system is monitoring cluster specific activity), as described below. The receipt of the operational metadata may occur periodically or aperiodically. Also, the operational metadata may be received in response to a query message initiated by the system monitoring logic of the analysis monitoring service (“pull” method) or may be received without any prompting by the system monitoring logic (“push” method). 
     Responsive to receipt of operational metadata from the cluster management system (and optionally subscription information from the subscription review service), the system monitoring logic may generate and provide cluster selection values to the cloud broker. According to one embodiment of the disclosure, a rules engine within the cloud broker includes policy and routing rules that are designed to determine cluster and/or compute node availability based, at least in part, on the cluster selection values. Hence, the cluster selection values may influence which cluster is selected by the cloud broker to handle malware analysis of an object determined to be suspicious by a sensor of a customer who subscribes to services provided by the malware detection system. 
     Also, the policy and routing rules may be designed to confirm compliance by the malware detection system with respect to customer requirements specified by performance-based attributes associated with the selected subscription level and/or the customer-configurable attributes contained within the service policy level information for the customer. This confirmation may be accomplished by comparing values associated with certain operational metadata to values associated with certain attributes within the service policy level information. 
     In response to determining that the operability of the selected cluster is not compliant with the performance-based attributes and/or customer-configurable attributes for the selected subscription level (e.g., operability falls below a prescribed number of performance thresholds, falls below any performance threshold by a certain amount or percentage, etc.), the cloud broker may issue one or more alerts in efforts to remedy non-compliance. 
     A first alert may include a message sent to an on-premises management system or an endpoint controlled by an administrator of the customer&#39;s network. The message may identify one or more attributes that have not been satisfied in accordance with the service policy level information, e.g., associated with the current subscription level. In some cases, non-compliance may be remedied by increasing the current subscription level to increase entitled object processing capacity. In other cases, non-compliance may be remedied by reducing the current subscription level to save money with reduced protection being provided. Where the subscription level qualifies or permits the customer to submit a number or rate of objects for analysis, the first alert may notify the administrator that the number or rate has been exceeded, and the customer is notified to increase the subscription level accordingly to address non-compliance. 
     A second alert may include a message directed to an original equipment manufacturer (OEM) or third party hosting the object evaluation service identifying the performance issues causing non-compliance. In response to the second alert, the OEM or third party may provide a remedy by augmenting the selected cluster with more compute nodes or re-balancing workloads on the existing clusters/compute nodes (e.g., by re-enrolling the impacted sensor and/or other sensors contributing to the workload). Of course, the selected remedy may depend on what attributes have not been satisfied in accordance with the service policy level information associated with the current subscription level. 
     The cloud broker of the analysis selection service is configured to establish communication sessions between a sensor and a cluster, which may involve selection of a cluster (or selection of a particular compute node within that cluster) to handle analyses of suspicious objects detected by a specific sensor. The cloud broker relies on the policy and routing rules to select the pairing between the cluster and the specific sensor, where the selection of the cluster may be influenced by the cluster selection values from the system monitoring logic and/or service policy level information. The service policy level information may be provided from the specific sensor or accessible from one or more databases located within the first subsystem and/or the second subsystem using the Customer_ID or an identifier of the sensor (“Sensor_ID”). 
     The cloud broker may also be responsible for reporting statistical information associated with analyses of suspicious objects and/or operability of particular cluster(s), particular compute node(s) or particular sensor(s). For example, the statistical information may be provided from the cluster management system within the second subsystem. Responsive to a request by the sensor for statistical information, the cloud broker may return a first type of aggregated statistical information to the sensor. The first type of aggregated statistical information allows network administrators for the subscriber to monitor performance of the malware detection system and/or compliance with service policy level guarantees based on the paid subscription. The cloud broker may return a second type of aggregated statistical information to a destination other than the sensor (e.g., the original equipment manufacturer “OEM” of the malware detection system, a third party entity, or another entity), which may be monitoring system performance. 
     It is contemplated that the cloud broker may include logic that returns analysis results for a previously analyzed object when a hash value of the previously analyzed object is highly correlated (e.g., identical or substantially corresponds) to a hash value of an object requested for analysis by a subscriber. This is a “cache” concept to avoid re-analyzing previously analyzed content and mitigate a likelihood of false positives or false negatives. 
     According to one embodiment of the disclosure, as previously described, the second subsystem includes the subscription review service, which includes licensing logic, enrollment logic and/or security content update logic. Collectively, the subscription review service confirms sensor enrollment as well as coordinates an exchange of information for use in updating operability of the malware detection system and/or the sensors. Prior to the malware detection system analyzing a data submission (e.g., object) from or returning statistical information to a sensor, the subscription review service may be requested to authenticate the sensor to confirm that the sensor (and/or the customer associated with the sensor) is authorized to use object analysis services provided by the malware detection system. 
     Hence, as described below in detail, a customer may submit a license request message for a subscription with a certain tier of service (subscription level) in consideration of desired attributes (e.g., desired network traffic capacity level, number of endpoints to be protected, etc.). Some of the attributes may be configured by the customer via an interface or portal (e.g., customer selected guest image “GI” preferences based on current OS/application types, notification (alert) scheme, remediation setting preference, etc.) while other attributes may be provided implicitly from the submission of the license request message (e.g., geographic location of the sensor by Internet Protocol (IP) or Media Access Control (MAC) address, GI preferences through analysis of installed software on the endpoint, etc.). This information associated with the attributes may be stored in one or more databases directly by or via the licensing/enrollment logic, and thereafter, maintained in or accessible by the cloud broker. The information may be accessed in the database via the Customer_ID, or, since the Customer_ID may be associated with one or more enrolled sensors (and their corresponding IDs), in some embodiments, a Sensor_ID may be used to access the information. 
     As described herein, a “cluster” is a scalable architecture that includes at least one compute node and allows additional compute nodes to be added as increased object analysis capacity is needed. A “compute node” includes logic that is configured to analyze suspicious objects that are detected by one or more sensors deployed at a subscriber site and submitted to the compute node via the analysis selection service located within the first subsystem of the malware detection system. The level (or threshold) for suspiciousness may be customer configurable (i.e., customer can adjust the sensitivity of the analysis based on available capacity or subscription level, the number or rate of false positives/negatives, or the like) or may depend on the type of object under analysis. 
     For example, according to one embodiment of the disclosure, the cluster may include a plurality of compute nodes, including at least (i) a compute node that operates as an “analytic” compute node by performing a malware detection analysis on a suspicious object and (ii) a compute node that operates as a “broker” compute node to select a particular analytic compute node within the cluster to analyze the suspicious object. The above-identified compute nodes may be the same compute node (e.g., different logic in same electronic device) or different compute nodes (e.g., different electronic devices or different logic within different electronic devices). For this embodiment, an analytic compute node of the cluster obtains metadata associated with a suspicious object via a broker compute node, and the metadata is used in the retrieval of the suspicious object for threat analysis by the analytic compute node. The classification of the suspicious object, in which a determination is made whether the object is associated with a cyber-attack or not, may take into account the analyses by the sensor (sensor analysis) and/or by the compute node (cluster analysis). 
     According to this embodiment, as described below, a sensor may be deployed at a subscriber site to monitor and evaluate information at that site. In particular, according to this embodiment, the sensor may be configured to capture incoming information, which is copied or intercepted during transit over a network (e.g., enterprise network or a public network where the intercepted information may include, for example, webpages downloaded by a user at the subscriber site or electronic messages transmitted to an email service provider over the Internet), conduct a preliminary analysis of incoming information, and route data submissions associated with suspicious objects (e.g., the suspicious objects themselves and/or metadata of the suspicious object) to a cluster within the second subsystem for more in-depth analysis of the object. During the preliminary analysis, the sensor may monitor, track or even intelligently control the number or frequency of data submissions to the cluster. Cluster selection prompted by a sensor requesting access to the malware detection system, along with maintenance of communications between the cluster and the sensor, are handled by the analysis selection service based, at least in part, on operational metadata received from the cluster management system via the analysis monitoring service. 
     Physically separate from the sensor, the cluster is responsible for analyzing suspicious objects that are part of the intercepted or copied information for malicious characteristics, namely the likelihood of a suspicious object being associated with malware. Prior to this analysis, logic within the sensor and/or the analysis selection service may conduct an initial determination as to whether (i) the object has already been submitted for malware analysis and/or (ii) a malware analysis has been completed for this object. In some situations, the results of a prior malware analysis may be returned to the sensor via the first subsystem. 
     I. Terminology 
     In the following description, certain terminology is used to describe features of the invention. In certain situations, each of the terms “logic”, “logic unit,” “engine,” or “system” are representative of hardware, firmware, and/or software that is configured to perform one or more functions. As hardware, the logic (or engine or system) may include circuitry having data processing or storage functionality. Examples of such circuitry may include, but are not limited or restricted to a microprocessor, one or more processor cores, a programmable gate array, a microcontroller, an application specific integrated circuit, wireless receiver, transmitter and/or transceiver circuitry, semiconductor memory, or combinatorial logic. 
     Alternatively, or in combination with the hardware circuitry described above, the logic (or logic unit or engine or system) may be software in the form of one or more software modules. The software modules may include an executable application, an application programming interface (API), a subroutine, a function, a procedure, an applet, a servlet, a routine, source code, a shared library/dynamic load library, or one or more instructions. The software module(s) may be stored in any type of a suitable non-transitory storage medium, or transitory storage medium (e.g., electrical, optical, acoustical or other form of propagated signals such as carrier waves, infrared signals, or digital signals). Examples of non-transitory storage medium may include, but are not limited or restricted to a programmable circuit; a semiconductor memory; non-persistent storage such as volatile memory (e.g., any type of random access memory “RAM”); persistent storage such as non-volatile memory (e.g., read-only memory “ROM”, power-backed RAM, flash memory, phase-change memory, etc.), a solid-state drive, hard disk drive, an optical disc drive, or a portable memory device. As firmware, the executable code may be stored in persistent storage. 
     The term “computerized” generally represents that any corresponding operations are conducted by hardware in combination with software and/or firmware. 
     The term “message” generally refers to information in a prescribed format and transmitted in accordance with a suitable delivery protocol such as HTTP (Hypertext Transfer Protocol); HTTPS (HTTP Secure); SSH (Secure Shell); SSH over SSL (SSH over Secure Socket Layer); Simple Mail Transfer Protocol (SMTP), File Transfer Protocol (FTP), iMESSAGE, Instant Message Access Protocol (IMAP), or another delivery protocol. Hence, each message may be in the form of one or more packets, frames, or any other series of bits having the prescribed format. 
     The term “cloud-based” generally refers to a hosted service that is remotely located from a data source and configured to receive, store and process data delivered by the data source over a network. Cloud-based systems may be configured to operate as a public cloud-based service, a private cloud-based service or a hybrid cloud-based service. A “public cloud-based service” may include a third-party provider that supplies one or more servers to host multi-tenant services. Examples of a public cloud-based service include Amazon Web Services® (AWS®), Microsoft® Azure™, and Google® Compute Engine™ as examples. In contrast, a “private” cloud-based service may include one or more servers that host services provided to a single subscriber (enterprise) and a hybrid cloud-based service may be a combination of certain functionality from a public cloud-based service and a private cloud-based service. 
     As briefly described above, the term “malware” may be broadly construed as any code, communication or activity that initiates or furthers a malicious attack (hereinafter, “cyber-attack”). Malware may prompt or cause unauthorized, anomalous, unintended and/or unwanted behaviors or operations constituting a security compromise of information infrastructure. For instance, malware may correspond to a type of malicious computer code that, as an illustrative example, executes an exploit to take advantage of a vulnerability in a network, network device or software, for example, to gain unauthorized access, harm or co-opt operation of a network device or misappropriate, modify or delete data. Alternatively, as another illustrative example, malware may correspond to information (e.g., executable code, script(s), data, command(s), etc.) that is designed to cause a network device to experience anomalous (unexpected or undesirable) behaviors. The anomalous behaviors may include a communication-based anomaly or an execution-based anomaly, which, for example, could (1) alter the functionality of a network device executing application software in an atypical manner; (2) alter the functionality of the network device executing that application software without any malicious intent; and/or (3) provide unwanted functionality which may be generally acceptable in another context. 
     In certain instances, the terms “compare,” comparing,” “comparison,” or other tenses thereof generally mean determining if a match (e.g., a certain level of correlation) is achieved between two items where one of the items may include a particular pattern. 
     Both the term “node” and the term “network device” may be construed as an electronic device or software with at least data processing functionality and perhaps connectivity to a network. The network may be a public network such as the Internet or a private network such as a wireless data telecommunication network, wide area network, a type of local area network (LAN), or a combination of networks. Examples of a node or network device may include, but are not limited or restricted to any type of computer (e.g., desktop, laptop, tablet, netbook, server, mainframe, etc.), a mobile phone, a data transfer device (e.g., router, repeater, portable mobile hotspot, etc.), a wireless interface (e.g., radio transceiver or tuner, a firewall, etc.), or software or other logic type. Illustrative examples of a node or network device may include a sensor or a compute node (e.g., hardware and/or software that operates to receive information, and when applicable, perform malware analysis on that information). Also, an “endpoint” is a network device deployed at a subscriber site with access to a network to which a sensor may be communicatively coupled to monitor network traffic as well as incoming traffic (e.g., email) destined for the endpoint. 
     The term “transmission medium” may be construed as a physical or logical communication path between two or more nodes. For instance, as a physical communication path, wired and/or wireless interconnects in the form of electrical wiring, optical fiber, cable, bus trace, or a wireless channel using infrared, radio frequency (RF), may be used. 
     The term “data submission” includes metadata associated with an object that is determined to be suspicious and may be subjected to additional malware analysis. In addition to the metadata, the data submission may include one or more objects provided concurrently with or subsequent to the metadata. The term “object” generally relates to content (or a reference for accessing such content) having a logical structure or organization that enables it to be classified for purposes of malware analysis. The content may include an executable (e.g., an application, program, code segment, a script, dynamic link library “dll” or any file in a format that can be directly executed by a computer such as a file with an “.exe” extension, etc.), a non-executable (e.g., a storage file; any document such as a Portable Document Format “PDF” document; a word processing document such as Word® document; an electronic mail “email” message, web page, etc.), or simply a collection of related data. 
     The object and/or metadata may be acquired from information in transit (e.g., a plurality of packets), such as information being transmitted over a network or copied from the transmitted information for example, or may be acquired from information at rest (e.g., data bytes from a storage medium). Examples of different types of objects may include a data element, one or more flows, or a data element within a flow itself. A “flow” generally refers to related packets that are received, transmitted, or exchanged within a communication session while a “data element” generally refers to a plurality of packets carrying related payloads (e.g., a single webpage provided as multiple packet payloads received over a network). The data element may be an executable or a non-executable, as described above. 
     Finally, the terms “or” and “and/or” as used herein are to be interpreted as inclusive or meaning any one or any combination. As an example, “A, B or C” or “A, B and/or C” mean “any of the following: A; B; C; A and B; A and C; B and C; A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive. 
     As this invention is susceptible to embodiments of many different forms, it is intended that the present disclosure is to be considered as an example of the principles of the invention and not intended to limit the invention to the specific embodiments shown and described. 
     II. General Architecture 
     Referring to  FIG. 1 , an exemplary block diagram of an illustrative embodiment of a subscription-based, malware detection system  100  is shown. Herein, the malware detection system  100  is communicatively coupled to one or more sensors  110   1 - 110   M  (M≥1). The sensors  110   1 - 110   M  may be located at a subscriber site  112  (e.g., located at any part of an enterprise network infrastructure at a single facility or at a plurality of facilities), or as shown, the sensors  110   1 - 110   M  may be located at different subscriber sites  112  and  114 . As illustrated, the malware detection system  100  may be separated geographically from any of the subscriber sites  112  and  114 . 
     According to one embodiment of the disclosure, the malware detection system  100  includes a first subsystem  130  and a second subsystem  160 . As shown in  FIG. 1 , the first subsystem  130  of the malware detection system  100  may be hosted as part of a public cloud-based service. The second subsystem  160  of the malware detection system  100  may be a private cloud-based object evaluation service operating as an analysis system, which is hosted by a cybersecurity provider or another entity different than the subscriber. Having a high degree of deployment flexibility, in the alternative, the malware detection system  100  can also be deployed as a fully public cloud-based service, as a fully private cloud-based service, or as a hybrid cloud-based service. This flexibility provides optimal scaling with controlled capital expense as well as the ability to control location(s) of deployments to satisfy governmental requirements, e.g., as to sensitive information (e.g., Personally Identifiable Information). 
     In  FIG. 1 , a sensor  110   1  may be deployed as a physical logic unit or as a virtual logic unit (software) installed on a network device. When deployed as a physical logic unit, the sensor  110   1  is identified by a sensor identifier (“Sensor_ID), which may be based on the media access control (MAC) address or another unique identifier (e.g., serial number) assigned to the sensor  110   1 . However, when deployed as a virtual logic unit, the sensor  110   1  may be preloaded with an activation code, which includes the Sensor_ID along with other credentials for communications with the malware detection system  100 . 
     As further shown in  FIG. 1 , the sensors  110   1 - 110   2  may be positioned at separate ingress points along the subscribing customer&#39;s network or subnetwork, or may be positioned in close proximity to one another, perhaps sharing the same hardware (e.g., power source, memory, hardware processor, etc.). For certain deployments, where the sensor  110   1 - 110   2  are used as edge network devices for subnetworks, sensors may be used to monitor lateral infection between the subnetworks at the subscriber site  112 . The sensors may serve as email proxies to receive email traffic being sent to computing assets protected by the customer in order to perform a security analysis. 
     When authenticated to access an object evaluation service  180  provided by the malware detection system  100  and a communication session to a selected cluster within the second subsystem  160  has been established, as described below, a sensor (e.g., sensor  110   1 ) may conduct a preliminary analysis of data within an object  120  (e.g., data within a header or body of one or more packets or frames within monitored network traffic) to determine whether that object  120  is suspicious. The object  120  may include a portion of information (content) that is intercepted or copied from information being routed over a network. The object  120  may be “suspicious” upon detecting (i) the object  120  is sourced by or directed to a particular network device identified in a “blacklist” or (ii) the data within the object  120  features a suspicious data pattern. Hence, the preliminary analysis, in effect, controls the number and/or frequency of suspicious objects made available by the sensor  110   1  for in-depth malware analysis by a selected cluster within the second subsystem  160 . In some embodiments, all objects of a specific type of object (e.g., emails) are regarded as suspicious and sent for in-depth malware analysis, with the results of the preliminary analysis being available for used in the final determination of whether the object is associated with a cyber-attack. 
     Referring still to  FIG. 1 , with respect to the malware detection system  100 , an analysis selection service  140  hosted within the first subsystem  130  is responsible for selecting a particular cluster (e.g., cluster  185   1 ) of one of more clusters  185   1 - 185   N  (N≥1), which is deployed within the second subsystem  160 , to perform malware analysis of objects provided by a specific sensor (e.g., sensor  110   1 ). The analysis selection service  140  selects the cluster  185   1  based on an analysis of the service policy level information  127  and/or a portion of the operational metadata  150  (referred to as “cluster selection values  157 ”) operating as inputs. 
     For example, according to one embodiment of the disclosure, upon receiving the cluster selection values  157  and/or the service policy level information  127 , a rules engine  142  operates in accordance with policy and routing rules to select the cluster  185   1 , where the operational metadata associated with the selected cluster  185   1  indicates that the cluster  185   1  is able to satisfy performance or operation criterion set forth by subscription attributes and/or customer-configured attributes within the service policy level information  127 . The policy and routing rules utilized by the rules engine  142  may be static, dynamic (modifiable and updateable) or a hybrid where some of the policy/routing rules are static while others are dynamic. For instance, the policy and routing rules of the rules engine  142  may be preloaded, but some of its rules may be modified or replaced over time. The frequency of the rule modifications may depend, at least in part, on results of prior malware detection by cybersecurity providers, changes in the cyber-threat landscape, and/or the types, targets, and techniques used in recent or potential cyber-attacks. 
     Hence, the analysis selection service  140  is configured to select the cluster  185   1  to perform malware analyses on suspicious objects submitted by a sensor (e.g., sensor  110   1 ) based, at least in part, on the service policy level information  127  within an analysis request message  125  and the operational metadata  150 . The operational metadata  150  is received from the cluster management system  190  deployed within the second subsystem  160  via analysis monitoring service  145 . As a result, the analysis selection service  140  controls the formation and maintenance of a communication session  155  between the particular cluster  185   1  of the object evaluation service  180  and the sensor  110   1  requesting the communication session  155 . 
     After the communication session  155  has been established, logic within the analysis selection service  140  is configured to provide information associated with a suspicious object from the requesting sensor  110   1  to the selected cluster  185   1  within the object evaluation service  180  and to return results of a malware analysis on that suspicious object back to the requesting sensor  110   1 . This logic is identified as a “cloud broker”  610  in  FIG. 6A . 
     As shown, the analysis monitoring service  145  receives, in a periodic or aperiodic manner, the operational metadata  150  from the second subsystem  160  (e.g., cluster management system  190 ). As shown, the operational metadata  150  may be received in response to a query message initiated by the analysis monitoring service  145  (“pull” method) or may be received without any prompting by the analysis monitoring service  145  (“push” method). A portion of the operational metadata  150  or information produced based at least in part on a portion of the operational metadata  150  (referred to as “cluster selection values  157 ”) is made available to the rules engine  142  within the analysis selection service  140 . 
     According to one embodiment of the disclosure, the cluster selection values  157  corresponds to information that (i) pertains to rule-based parameters utilized by the policy and routing rules and (ii) is generated from the operational metadata  150 . As an example, the operational metadata  150  may include cluster queue size or queue length, cluster or compute node workload, cluster or compute node geographic location, and/or software profiles (e.g., guest images) supported for processing of the suspicious object  120  within one or more virtual machines hosted by the compute nodes within the cluster. Based on this example, the cluster selection values  157  may be values generated from the metadata (e.g., current queue length and/or cluster workload) that, when applied to the policy and routing rules controlling operation of the rules engine  142 , identify which cluster or clusters are available to support another sensor and/or their level of availability. As an illustrative example, where the policy and routing rules include a rule that requires a cluster to have 30% queue capacity to service another sensor and the metadata identifies that the queue size is fifty storage elements and the current queue length is 15 storage elements, the cluster selection values  157  would identify that the cluster has 30% (15/50) capacity. 
     From other information (e.g., software profiles or geographic location), the cluster selection values  157  may be values that further refine the cluster selection process by identifying which cluster or clusters should be considered or precluded from consideration for data submissions involving a particular type of object. From still other information (e.g., compute node workload), the cluster selection values  157  may be values that further determine what broker compute node is to be selected for a particular cluster. Additionally, or in the alternative, the cluster selection values  157  may include or may be based on information associated with one or more sensors  110   1 , . . . , and/or  110   N  based on prior communication sessions by the sensor(s)  110   1 , . . . , and/or  110   N  such as sensor activity (e.g., number of submissions, amount of analysis time performed on objects by the particular sensor, number of malicious object detected for a particular sensor, or the like). 
     As described herein, the following operations are performed before the sensor (e.g., sensor  110   1 ) is able to provide data for analysis (sometimes referred to as a “data submission  124 ”) to the malware detection system  100 : (a) sensor  110   1  obtains service policy level information  127  that includes credentials such as the Customer_ID, user name, password, and/or keying material, as well as other parameters such as quality of service “QoS” information applicable to the Customer_ID that may specify, for example, the amount of time allocated per object analysis or any other factors that provide different levels of analysis or responsiveness per the subscription for the customer; (b) sensor  110   1  is authenticated to access services provided by the malware detection system  100  using at least some of the service policy level information  127 ; (c) selection of a cluster (e.g., cluster  185   1 ) to handle malware analyses for the sensor  110   1  (based on incoming cluster selection values  157  and at least a portion of the service policy level information  127 ; and (d) communications with the cluster  185   1  via the communication session  155  have been established. 
     According to one embodiment of the disclosure, the data submission  124  may include the object  120  and/or metadata  122  associated with the object  120 . Herein, according to this embodiment, the data submission  124  includes the metadata  122  while the object  120  is temporarily stored by the sensor  110   1  and uploaded at a later time. For instance, the sensor  110   1  may later upload the object  120  to the object evaluation service  180  via the analysis selection service  140  for malware analysis. This upload may occur once the malware detection system  100  confirms, based on analysis of the metadata  122 , that (a) the object  120  has not been analyzed previously and (b) a particular compute node within a selected cluster is ready to analyze the object  120 . Alternatively, it is contemplated that the sensor  110   1  may concurrently upload the object  120  and its corresponding metadata  122  to the malware detection system  100  for processing. 
     As an optional service, an accounting and license enforcement service  143 , separate from the licensing and enrollment services offered by the subscription review service  170 , may be implemented in the first subsystem  130  and configured to monitor data submissions by the subscriber and account for all of the analysis and actions undertaken that exceed the terms of a license (subscription). The software associated with this service may further implement a “pay-as-you-go” licensing feature, which keeps track of all of the data submissions by a subscriber and charges based on usage of the malware detection system  100 . This licensing feature provides for pre-payment of some reserved object analysis capacity, potentially at a cost savings. 
     Additionally, the accounting and license enforcement service  143  may be configured to confirm the current subscription status assigned to the customer associated with the sensor  110   1  that is attempting to upload the object  120  into the malware detection system  100  for analysis. This confirmation may be accomplished, for example, by accessing one or more databases  175  within the malware detection system  100  (e.g., within the second subsystem  160 , within the first subsystem  130  such as within a portion of the subscription review service  170  hosted by the first subsystem  130 , etc.) using the Sensor_ID or the Customer_ID provided by the sensor  110   1  as an index to obtain information pertaining to the customer&#39;s subscription. Alternatively, this confirmation may be accomplished by using the Sensor_ID to determine the Customer_ID within a Sensor_ID-Customer_ID mapping, and thereafter, conducting a database lookup using the Customer_ID. 
     More specifically, the confirmation of the current subscription status may involve a first determination as to whether the customer has an active subscription to the malware detection system  100 . If the customer does not possess an active subscription to the malware detection system  100 , the sensor  110   1  may be precluded from establishing a communication session and uploading information into the malware detection system  100  for analysis. If so, a second determination is conducted to access information, including service policy level information  127 , which pertains to the customer&#39;s subscription. 
     The service policy level information  127  may include subscription attributes (e.g., subscription tier, QoS thresholds, permissions, access control information, details on cluster availability such as a listed default cluster, cluster selection ordering or preferences, cluster restrictions, etc.) and/or customer-configured attributes (e.g., geographic location permissions or restrictions for compute nodes in processing objects for the sensor  110   1 , type of remediation identified by a remediation settings, notification scheme, etc.), or the like. Of course, it is contemplated that factory set attributes (e.g., default cluster, permissions, etc.), and/or operationally dynamically generated attributes that are dynamic based on past historical operations may be provided as part of the service policy level information  127  as well. It is contemplated that an OEM or third party hosting the object evaluation service may configure the service so that an attribute may be categorized as a subscription, customer-configured, factory set, or operationally dynamic attribute, where some customer-configured attributes allow customers to tailor operability that is not offered by the subscription level. The OEM or third party can decide which attribute or attributes should be configured in conjunction with which subscription level. 
     Additionally, the first subsystem  130  is configured to generate and transmit statistical information  192 , which may be prompted in response to a management query message  194  (as shown) or provided without being in response to signaling from the subscriber site  112 . The management query message  194  may correspond to a request for data that is directed to the operability of a particular sensor or the cluster(s). For instance, the statistical information  192  may be provided to a third party node or reporting logic deployed to operate as part of an on-premises (subscriber-based) management system (see system  606  of  FIG. 6A ) or a centralized management system (not shown) accessible by more than one subscriber site. 
     The on-premises management system  606 , in some embodiments, is also responsible for receiving customer selections of available configurable attributes, as elsewhere described. As shown in  FIG. 6A , the on-premises management system  606  includes a user interface (e.g., an interactive user interface)  606   a , a network interface  606   b , and may be implemented as software stored in memory  606   c  that, which, when executed by one or more hardware processors  606   d , performs the functionality described herein. 
     Referring back to  FIG. 1 , according to one embodiment of the disclosure, the second subsystem  160  includes the subscription review service  170  and the object evaluation service  180 . Herein, the subscription review service  170  may be configured to enable the sensor  110   1  to obtain the service policy level information  127  through licensing services, authenticate the sensor  110   1  through sensor enrollment services as well as coordinate an exchange of information for use in updating operability of the malware detection system  100  and/or sensors  110   1 - 110   M . These authentication operations  172  are described below and illustrated in greater detail in  FIGS. 6A-6B . 
     As shown, the subscription review service  170  is deployed within the second subsystem  160 . However, it is contemplated that the subscription review service  170  may be deployed within the first subsystem  130  or partially within both subsystems  130  and  160 . Furthermore, although not shown, the subscription review service  170  may be communicatively coupled to the analysis selection service  140  and/or the analysis monitoring service  145  to provide subscription information that may adjust operability of one or both of these services (e.g., increase or decrease QoS levels, decrease or increase analysis times, decrease or increase cluster availability, etc.). 
     The object evaluation service  180  includes one or more clusters  185   1 - 185   N  (N≥1). Each cluster  185   1 - 185   N  may be configured to conduct an analysis of a suspicious object (e.g., object  120 ) provided by one of the sensors  110   1 - 110   M  that is enrolled to the subscription-based malware detection system  100 . As described above, each cluster  185   i , . . . , or  185   N  is a scalable architecture, which includes at least one compute node in which additional compute nodes may be added as needed to handle an increased number of object analyses caused by increased network traffic at a subscriber site (e.g., subscriber site  112 ). 
     According to one embodiment, the cluster  185   1  includes a plurality of compute nodes, including (i) one or more compute nodes  186  each operating as a “broker” compute node and (ii) one or more compute nodes  187  each operating as an “analytic” compute node. Herein, a broker compute node  186  may be configured to determine, from received metadata  122  associated with the data submission  124  (e.g., hash value for the object  120 ), whether the suspicious object  120  has been previously processed by the malware detection system  100 . If not, the suspicious object  120  is temporarily stored and is subsequently analyzed by at least one of the analytic compute node(s)  187  to determine whether the suspicious object  120  is associated with malware. The received metadata  122  may be used in the retrieval of the suspicious object  120 . If the suspicious object  120  has been previously processed, however, the results of the prior analysis may be reported by the broker compute node  186  to the sensor  110   1  via the first subsystem  130 . In some embodiments, the sensor  110   1  may provide the results to the on-premises management system  606  of  FIG. 6A . 
     Alternatively, in lieu of the broker compute node  186  determining whether the suspicious object  120  has been previously processed, the first subsystem  130  may include logic that returns results from previously analyzed objects upon detecting a high correlation between metadata associated with the suspicious object  120  and metadata associated with a previously analyzed object. This logic may be implemented to avoid unnecessary analysis to improve response time and mitigate potential false positives or false negatives. 
     Referring now to  FIG. 2 , a block diagram of an exemplary embodiment of logic implemented within a physical deployment of the sensor  110   1  in communication with the malware detection system  100  of  FIG. 1  is shown. According to this embodiment of the disclosure, the sensor  110   1  comprises one or more hardware processors  200  (generally referred to as “processor”), a non-transitory storage medium  210 , and one or more network interfaces  220  (generally referred to as “network interface”). These components are at least partially encased in a housing  230 , which may be made entirely or partially of a rigid material (e.g., hard plastic, metal, glass, composites, or any combination thereof) that protects these components from environmental conditions. 
     In an alternative virtual device deployment, the sensor  110   1  may be implemented entirely as software that may be loaded into a node or network device (as shown) and operated in cooperation with an operating system (“OS”) running on the node. For this implementation, the architecture of the software-based sensor  110   1  includes software modules that, when executed by a processor, perform functions directed to certain functionality of logic  240  illustrated within the storage medium  210 , as described below. 
     The processor  200  is a multi-purpose, processing component that is configured to execute logic  240  maintained within the non-transitory storage medium  210  operating as a data store. As described below, the logic  240  may include, but is not limited or restricted to, (i) subscription control logic  250 , (ii) preliminary analysis logic  260 , (iii) metadata extraction logic  270 , (iv) notification logic  290  and/or (v) cluster selection logic  295 . One example of processor  200  includes an Intel® (x86) central processing unit (CPU) with an instruction set architecture. Alternatively, processor  200  may include another type of CPUs, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a field-programmable gate array, or any other hardware component with data processing capability. 
     According to one embodiment of the disclosure, the sensor  110   1  may include subscription control logic  250  that controls the signaling (handshaking) between licensing logic  640  and enrollment logic  650  of  FIG. 6A . Such signaling enables the sensor  110   1  to acquire credentials that are part of the service policy level information  127  of  FIG. 1  (e.g., Customer_ID, username, password, keying material, etc.) as well as an uniform resource locator (URL) or other communication address for accessing the analysis selection service  140  of  FIG. 1  and establishing communications with at least one cluster (e.g., cluster  185   1 ) of the available clusters  185   1 - 185   N . Additionally, the subscription control logic  250  may maintain information associated with a subscription expiration time that, if not extended through renewal, disables communications with the assigned cluster  185   1  and/or signals a subscriber that renewal payments are due to continue the subscription to the malware detection system  100  (or upgrade to a more robust service policy level). 
     As shown, the network interface  220  is configured to receive incoming data  235  propagating over a network, including the metadata  122  and the object  120 . The incoming data  235  may be received directly from the network or via a network tap or Switch Port Analyzer (SPAN) port, also known as a mirror port, provided by the sensor  110   1 . Processed by processor  200 , the preliminary analysis logic  260  may conduct an analysis of at least a portion of the incoming data  235 , such as the object  120  for example, to determine whether the object  120  is suspicious. Furthermore, the metadata extraction logic  270 , during such processing, may extract metadata  122  from the incoming data  235  and assign an object identifier  275  to correspond to both the metadata  122  and the suspicious object  120 . The object identifier  275  may be unique among the clusters  185   1 - 185   N  (referred to as “universally unique identifier” or “UUID”  275 ). 
     The metadata  122  and UUID  275  may be stored in a metadata data store  280 . The suspicious object  120  and UUID  275  may be stored in a content data store  285 . The content data store  285  may be part of the non-transitory storage medium  210  of the sensor  110   1 . It is contemplated, however, that the content data store  285  may be located externally from the sensor  110   1 . 
     The sensor  110   1  further includes notification logic  290 , which is responsible for handling communications  292  with the selected cluster  185   1  via the analysis selection service  140  of  FIG. 1 . Such communications  292  may include (i) analysis results or (ii) information that signifies (a) the suspicious object  120  has already been analyzed or (b) a timeout event has been detected for the metadata  122  that originated from the sensor  110   1 , where a “timeout event” denotes that the suspicious object  120  has not been analyzed by the object evaluation service  180  of  FIG. 1  within a time allotted by the service policy level information  127  associated with the subscription for the customer or by the sensor  110   1 . 
     Operating in combination with subscription control logic  250  and/or preliminary analysis logic  260 , the cluster selection logic  295  is adapted to select, based on the service policy level information  127  associated with the subscription for the customer, between on-premises cluster (or malware detection system) that resides on the same enterprise network as sensor  110   1  (not shown) or an off-premises cluster within malware detection system  100  of  FIG. 1 . In this regard, the service policy level information  127  may have a customer-configurable attribute that specifies customer preferences regarding on-premises or off-premises cluster selection. Hence, depending on the selected default cluster, the on-premises cluster may be deployed to provide extra capacity when malware analysis thresholds established for cloud-based analyses allowed in accordance with the customer&#39;s subscription level have been exceeded. 
     Alternatively, the off-premises cluster may be deployed to provide extra capacity when malware analysis thresholds provided by the on-premises clusters have been exceeded. It is contemplated that routing decisions for the metadata  122  to either (i) on-premises cluster or (ii) off-premises cluster via the analysis selection service  140  may be based on any number of factors. These factors may include, but are not limited or restricted to object type (e.g., portable document format “PDF” objects are directed to an on-premises cluster and binaries are directed to off-premise cluster); client type (e.g., objects extracted from network traffic originating from certain customers, e.g., governmental agencies are directed to an on-premises cluster while objects extracted from network traffic originating from other governmental agencies are directed to an off-premises cluster); capacity (e.g., objects are directed to an off-premises cluster until a capacity (or subscription) threshold reached); and/or network security level (e.g., objects extracted from network traffic over protected subnetworks are directed to an on-premises cluster while objects extracted from network traffic over unprotected subnetworks are directed to an off-premises cluster). 
     Referring now to  FIG. 3 , an exemplary embodiment of logic implemented within the cluster  185   1  of  FIG. 1  is shown. The cluster  185   1  comprises a plurality of compute nodes  300   1 - 300   P  (P≥1), which are communicatively coupled to a distributed queue  310  (e.g., a logical representation of the collective memory formed by queue memories for each cluster  185   1 - 185   N ) over a first network  315 . Each compute node (e.g., compute node  300   1 ) may feature an analysis coordination system  320   1  and an object analysis system  340   1 . As shown in  FIG. 4 , analysis coordination system  320   1  may be activated or deactivated, such as activation or deactivation of a control line  420  by processor  400 , where the compute node  300   1  operates as a “broker” compute node when the analysis coordination system  320   1  is activated or operates only as an “analytic” compute node when the analysis coordination system  320   1  is deactivated (e.g., compute nodes  300   3  and  300   4 ). As an alternative embodiment, it is contemplated that a “broker” compute node may have a logical architecture different than an “analytic” compute node. For example, a broker compute node may be configured with only an analysis coordination system. An analytic compute node may be configured with only an object analysis system. 
     According to exemplary embodiment of  FIG. 3 , sensors  110   1 - 110   M  are communicatively coupled to one or more broker compute nodes (e.g., compute node  300   1  and compute node  300   2 ) of the first cluster  185   1  via analysis selection service  140  of  FIG. 1 . Any of the analysis coordination systems  320   1  and  320   2  (e.g., system  320   1 ) may be selected by the analysis selection service  140  to receive metadata  122  from any of the sensors  110   1 - 110   M  (e.g., sensor  110   1 ) for storage within the distributed queue  310 . The metadata  122  may be retrieved by an object analysis system  340   1 - 340   4  that is available for analyzing the suspicious object  120  associated with the metadata  122  for malware. 
     As further shown in  FIG. 3 , according to this embodiment of the disclosure, the difference between the “broker” compute nodes  300   1  and  300   2  and the “analytic” compute nodes  300   3  and  300   4  is whether or not the analysis coordination systems have been deactivated. Herein, for the “broker” compute nodes  300   1  and  300   2 , analysis coordination systems  320   1  and  320   2  have been activated while the analysis coordination systems (not shown) for compute nodes  300   3  and  300   4  have been deactivated. It is noted, however, that all of the compute nodes  300   1 - 300   4  within the same cluster  185   1  feature an object analysis system  340   1 - 340   4 , respectively. Each of these object analysis systems  340   1 - 340   4  includes logic that is capable of conducting an in-depth malware analysis of the object suspicious  140  upon determining to have sufficient processing capability. 
     More specifically, each object analysis system  340   1 - 340   4 , when determined to have sufficient processing capability or otherwise determined to have suitable analytical capabilities to meet the required analysis (including that for the particular object and that which satisfies the service policy level information  127  associated with the subscription for the customer), accesses the queue  310  to obtain metadata  122  associated with the suspicious object  120  awaiting malware analysis. For example, during operation, the object analysis system  340   1  may periodically and/or aperiodically (e.g., in response to completion of a prior malware analysis) access the queue  310  and obtain the metadata  122  associated with the suspicious object  120 . The metadata stored in the queue  310  may be prioritized for removal and subsequent analysis of their corresponding objects. For example, the prioritization of the queue  310  may be in accordance with object type (e.g., metadata associated with an object of a first type is queued at a higher priority than metadata associated with an object of a second type). As another example, the prioritization of the queue  310  may be in accordance with the service policy level assigned to the subscriber, namely metadata associated with an object submitted by a subscriber at a first service policy level (e.g., first QoS level) is queued at a higher priority than metadata associated with an object submitted by a subscriber at a second service policy level. 
     Upon retrieval of the metadata  122  and based on at least a portion of the metadata  122 , the object analysis system  340   1  is able to determine the storage location of the suspicious object  120 . Thereafter, the object analysis system  340   1  may retrieve the suspicious object  120 . The suspicious object  120  may be stored in the sensor  110   1 , in the compute node  300   1 , or in an external network device (not shown) that may be accessed via the analysis selection service  140  of  FIG. 1 . 
     Upon receipt of the suspicious object  120 , the object analysis system  340   1  conducts an in-depth malware analysis, namely any combination of behavior (dynamic) analysis, static analysis, or object emulation in order to determine a second level of likelihood (probability) of the suspicious object  120  being associated with malware. Such operations may involve execution of the suspicious object  120  within a virtual machine that is configured with one or more software profiles (e.g., one or more software components including operating system, application(s), and/or plug-in(s)) that allows the virtual machine to execute the suspicious object  120  and monitor behaviors of the virtual machine, including any of the software components. The second level of likelihood is at least equal to and likely exceeding (in probability, in computed score, etc.) a first level of likelihood. For example, the first level of likelihood may be expressed as a probability that exceeds a first threshold to find that the object  120  is suspicious and the second level of likelihood exceeds a second, higher threshold to find that the object is likely malicious and a cyber-attack is likely in process. 
     As an illustrative example, the analysis coordination system  320   1  may be selected by the analysis selection service  140  of  FIG. 1  to receive the metadata  122  associated with the suspicious object  120  and provide information, which may include some or all of the metadata  122 , to the queue  310 . Thereafter, the analysis coordination system  320   1  has no involvement in the routing of such metadata to any of the object analysis systems  340   1 - 340   4  of the compute nodes  300   1 - 300   4 . Instead, an object analysis system (e.g., object analysis system  340   3 ) having sufficient processing capability and capacity to handle a deeper level analysis of the suspicious object  120  may fetch the metadata  122  that is stored in the queue  310  and subsequently fetch the suspicious object  120  based, at least in part, on a portion of the metadata  122 . 
     In summary, as shown in  FIGS. 5A-5B , while referencing  FIGS. 1-4 , the malware detection system  100  is configured to communicate with one or more sensors  110   1 - 110   M , where each sensor  110   1 - 110   M  is configured to receive information that includes at least metadata  122  and a corresponding object  120  for malware analysis (block  500 ). The malware detection system  100  receives a license request message from the customer via a sensor, and in response to granting of the license request, the service policy level information associated with the customer is stored and accessible by the analysis selection service  140  within the malware detection system  100  (blocks  502  and  504 ). 
     Prior to forwarding a portion of the information to the second subsystem  160  for malware analysis, a sensor (e.g., sensor  110   1 ) may complete its enrollment as an analysis logic for a subscriber (customer) of the subscription-based malware detection system  100 , as described in reference to  FIG. 6A  (block  505 ). This enrollment scheme may involve a submission of credentials (e.g. Sensor_ID, Customer_ID, username, and/or password, etc.) to the subscription review service  170  for retrieval of information for accessing the analysis selection service  140  as illustrated in  FIG. 6A  (e.g., URL for accessing the cloud broker  610 , etc.). 
     The analysis selection service  140  utilizes both the service policy level information  127  provided as part of or accessible based on information in the analysis request message  125  and the cluster selection values  157  to establish a communication session (e.g., tunnel) between the sensor (e.g., sensor  110   1 ) and a selected cluster (e.g., cluster  185   1 ) of the second subsystem  160 , as illustrated in  FIG. 6B  (blocks  510 ,  515 ,  520 ,  525  &amp;  530 ). As described herein, the cluster selection values  157  may correspond to information that pertains to rule-based parameters for policy and routing rules of the rules engine  142 , and the cluster selection values  157  are generated from the operational metadata  150  acquired from the cluster management system  190  by the analysis monitoring service  145  and the service policy level information  127  associated with the subscription for the customer. In some implementations, the service policy level information  127  may be at a per-sensor granularity rather than a per-customer level. The cluster selection values  157  may be used in the selection of the particular cluster (e.g., cluster  185   1 ) and/or a compute node (e.g., compute node  300   1 ) within that particular cluster (e.g., cluster  185   1 ) for analyzing objects from the sensor (e.g., sensor  110   1 ). 
     As illustrated examples, the cluster selection values  157  relied upon for selection of the cluster (and/or compute node within the selected cluster) may pertain to values that collectively identify, when applied to policy and routing rules of the rules engine  142 , what cluster or clusters have sufficient resources to support additional data submissions from a sensor. For example, the cluster selection values  157  may include values directed to cluster workload and/or cluster capacity. The cluster workload may be determined based, at least in part, on utilization levels of each of the compute nodes (e.g., compute nodes  750   1 - 750   P ) within that cluster (e.g., cluster  185   1 ). The cluster capacity may be based, at least in part, on the distributed queue size for each cluster  185   1 - 185   N  along with its current queue length (i.e., amount of queue (i.e., number of queue entries) that is not storing pertinent metadata). Additionally, or in the alternative, the cluster selection values  157  may include values directed to software profiles or geographic location of the sensor and/or cluster, that, when applied by the rules engine  142 , may be used to determine which cluster or clusters is best suited for supporting the sensor (e.g., clusters that are geographically close to the sensor may be preferred for reduced transmission latency) and/or best satisfy the service policy level information (attributes) of the subscription for the customer. 
     The sensor (e.g., sensor  110   1 ) receives incoming information for malware analysis. Specifically, the metadata extraction logic  270  of the sensor  110   1  separates the metadata  122  from the object  120 . Thereafter, the preliminary analysis logic  260  conducts an analysis to determine whether the object  120  is suspicious (e.g., meets a first prescribed level of likelihood that the object  120  is associated with malware). This preliminary analysis may include one or more checks (real-time analyses) being conducted on the metadata  122  and/or object  120  without execution of the object  120 . Illustrative examples of the checks may include, but are not limited or restricted to the following: (i) bit pattern comparisons of content forming the metadata  122  or object  120  with pre-stored bit patterns to uncover (a) deviations in messaging practices (e.g., non-compliance in communication protocols, message formats, and/or payload parameters including size), (b) presence of content within the object  120  that is highly susceptible to or widely used by perpetrators for cyber-attacks, and/or (c) prior submission via the sensor  110   1  of certain types of objects, and/or (ii) comparison between a representation of the object  120  (e.g., bit pattern representation as a hash of the object  120  or portions of the object  120 ) and stored representations of previously analyzed objects. 
     Prior to conducting an analysis to determine whether the object  120  is suspicious, it is contemplated that the preliminary analysis logic  260  within the sensor  110   1  may determine whether a prior preliminary (or in-depth malware) analysis has been conducted on the object  120 . In some instances, such as repeated benign or malicious objects or when a prior submission has recently occurred and such analysis has not yet completed, the sensor  110   1  may discontinue further analysis of the object  120 , especially when the prior preliminary (or in-depth malware) analysis has determined that the object  120  is benign (e.g., not malicious) or malicious (e.g., determined to have some association with malware) through one or more of the above-described checks. For some repeated benign or malicious objects, the sensor  110   1  may simply report the results from the prior analysis. However, where the object  120  is an URL or another object type, especially an object with dynamically changing data as in URLs or documents with an embedded URL, the sensor  110   1  may routinely supply the metadata  122  to its assigned broker compute node via the analysis selection service  140 . 
     Herein, the metadata  122  may be an aggregate of metadata retrieved from the incoming data  235  of  FIG. 2  along with additional metadata associated with the sensor  110   1  itself. The metadata  122  is provided to one of the broker compute nodes (e.g., compute node  300   1 ) of the cluster  185   1  that is assigned by the analysis selection service  140  to conduct an in-depth malware analysis of a suspicious object to be subsequently submitted by the sensor  110   1  (block  535 ). A portion of the metadata  122  may be used by an analytic compute node to retrieve the suspicious object  120  associated with the metadata  122  for processing within a virtual machine, monitoring behaviors of the object (and virtual machine) during such processing, and determining whether the object may be malicious based on these monitored behaviors (blocks  540  and  545 ). The analysis results may be returned to the sensor  110   1  via the analysis selection service  140  (block  550 ). Metadata associated with this analysis (e.g., sensor identifier that requested analysis, cluster workload, object type, etc.) and other analyses may be collected by the cluster management system  190  for use by the analysis monitoring service  145  to assist the analysis selection service  140  in cluster assignment to sensors  110   1 - 110   M  (block  555 ). 
     III. Operational Flow 
     Referring now to  FIG. 6A , a more detailed embodiment of the operational flow in establishing communications between sensors  110   1 - 110   M  and the malware detection system  100  of  FIG. 1  is shown. According to this embodiment of the disclosure, the analysis selection service  140  of the first subsystem  130  includes a cloud broker  610  that is communicatively coupled to the system monitoring logic  630  of the analysis monitoring service  145 , where the architecture of the cloud broker  610  and system monitoring logic  630 , either individually or collectively, may include one or more hardware processors and memory including software that, when executed, performs their functionality described below. Alternatively, the cloud broker  610  and/or the system monitoring logic  630  may be deployed as software. 
     The second subsystem  160  features subscription review service  170 , which includes licensing logic  640  along with enrollment logic  650  and security content updating logic  670 . In accordance with one embodiment of the disclosure, the architecture of the subscription review service  170  may include one or more hardware processors and memory including licensing logic  640  along with enrollment logic  650  and security content updating logic  670  described below. Additionally, the object evaluation service  180  of the second subsystem  160  includes one or more clusters  185   1 - 185   N , and/or cluster management system  190  to manage the organization of the cluster(s)  185   1 - 185   N  and the configuration of the compute nodes (not shown) deployed within the clusters  185   1 - 185   N . The architecture of the cluster management system  190  may include one or more hardware processors and memory including software that, when executed, performs its functionality described below. However, as alternative embodiments, the subscription review service  170  and/or some or all of the object evaluation service  180 , including the cluster management system  190 , may be deployed as software that is executed by the same or different hardware circuitry deployed within the second subsystem  160 . 
     The sensors  110   1 - 110   M  may be positioned at various locations on a transmission medium  602  that may be part of an enterprise network  600  (e.g., connected at various ingress points on a wired network or positioned at various locations for receipt of wireless transmissions). For an email threat detection embodiment, a sensor (e.g., sensor  110   2 ) may be incorporated in a message transfer agent deployed in-line with the email traffic flow and between an anti-spam gateway and a network&#39;s internal mail server (e.g., Microsoft Exchange®). For use in a deployment involving a cloud-based messaging service, the email may be delivered to the sensor  110   2  as a next-hop before the email reaches the internal mail server. 
     As shown in  FIG. 6A , deployed as physical or virtual logic units, the sensors  110   1 -110 2  are located, e.g., at subscriber site  112 , which may include an on-premises (subscriber-based) management system (as shown for subscriber site  114 ). The sensors  110   1 - 110   M  are configured to monitor data traffic propagating over a network, such as the enterprise network  600  for example. The “traffic” may include an electrical transmissions as files, email messages, web pages, or other types of content. 
     More specifically, according to one embodiment of the disclosure, the sensor  110   1  may be implemented as a network device or deployed as software within a network device that is coupled to the transmission medium  602  directly or is communicatively coupled with the transmission medium  602  via an interface  604  operating as a data capturing device. According to this embodiment, the interface  604  is configured to receive incoming data and subsequently process the incoming data, as described below. For instance, the interface  604  may operate as a network tap (in some embodiments with mirroring capability) that provides to the sensor  110   1  at least one or more data submissions  124  acquired from network traffic propagating over the transmission medium  602 . Alternatively, although not shown, the sensor  110   1  may be configured as an in-line appliance to receive traffic (e.g., files or other objects) and to provide data submissions that are associated with “suspicious” objects for subsequent analysis. As yet another alternative, the sensor  110   1  may be configured to receive information that is not provided over the network  600 . For instance, as an illustrative example, the interface  604  may operate as a data capturing device (e.g., port) for receiving data submissions manually provided via a suitable dedicated communication link or from portable storage media such as a flash drive. 
     It is contemplated that the security content updating logic  670  may be communicatively coupled to (i) the cluster management system  190  via a first transmission medium  672  and (ii) one or more subscribed-based management systems (e.g., on-premises management system  606 ) via a second transmission medium  673 . The cluster management system  190  is configured to manage a cluster or multiple clusters of the object evaluation service  180  while the on-premises management system  606  is configured to manage a sensor or multiple sensors of the subscriber site  114 , as shown. Hence, updates to the functionality of components within the object evaluation service  180  (e.g., signatures, rules, executables, software patches, OS versions, plug-ins, etc.) may be propagated to the compute nodes  300   1 - 300   P  via the cluster management system  190 , which received the updates from the security content updating logic  670  via the first transmission medium  672 . Similarly, updates to the functionality of components within the sensors  110   3 - 110   M  may be propagated via the on-premises management system  606 , which received the updates from the security content updating logic  670  via the second transmission medium  673 . Furthermore, the security content updating logic  670  supports two-way communications to receive information associated with analysis results conducted by sensors or clusters with the malware detection system  100  via communication path  674  and/or analysis results from other sources outside of the malware detection system  100  via communication path  675 . 
     A. Licensing and Enrollment 
     Referring now to  FIGS. 6A-6B , to obtain access to the malware detection system  100 , the sensor  110   1  of the sensors  110   1 - 110   M  may require a software license that includes software license (subscription) credentials  642  necessary for the sensor  110   1  to communicate with the enrollment logic  650 . Hence, in some embodiments, the customer requests to purchase a subscription, which is communicated to the subscription review service  170 . For initiating the request, the customer may enter data via the user interface  606   a  of the on-premises management system  606  or a web portal, and typically will need to arrange or make payment of a subscription fee. The subscription review service  170  assigns an identifier to the customer (Customer_ID), maps the identifier of the sensor (Sensor_ID) to the Customer_ID, and further maps at least the service policy level information  127  provided by a paid subscription to the Customer_ID. 
     In some embodiments, the customer may be offered a plurality of tiers of subscription, each with an associated service policy level specified by a set of subscription attributes. For instance, a subscription attribute may specify a specific duration (or latency) allocated for an analysis of an object by the malware detection system  100  before the analysis times-out and for the classification of the object as malware or benign. Another subscription attribute may specify a maximum number of customer endpoints, e.g., laptops and other computers to be supported and protected against cyber-attacks by the malware detection system. Yet another subscription attribute includes a number and/or rate of data submissions allowed for the subscription tier selected. The subscription attributes are included in the service policy level information  127  of the subscription. 
     Moreover, the customer may also have an opportunity to select (e.g., via the user interface  606   a ) from among a set of customer-configurable attributes, which, though not dictated by the subscription type or tier, once selection, become associated with the subscription, included in the service policy level information  127 , and used in in managing the object evaluation services  180  of the malware detection system  100 . These customer-configurable attributes may include, by way of example, (i) a geographic location attribute that specifies the customer&#39;s preferred or required geographic location for the cluster used to analyze submission data from the customer, e.g., to protect sensitive information, and (ii) a guest image attribute that specifies one or more software profiles (e.g., brand and/or version of computer programs included in the software profiles) preferred or required by the customer. 
     More specifically, as shown, the sensor  110   1  may acquire the software license credentials  642  by transmitting one or more license request messages  644  to licensing logic  640 . The license request message(s)  644  may include information uniquely associated with the sensor  110   1  (e.g., public Secure Shell “SSH” key assigned to the sensor  110   1  or other keying material). Additionally, the license request message(s)  644  may include information associated with the customer and/or financial information to purchase the software license. The software license credentials  642  includes service policy level information  127 , which includes subscription information pertaining to the customer that may be made available to the sensor  110   1  and/or the on-premises management system  606  associated with that customer. As described above, the service policy level information  127  may include the Customer_ID along with information directed to a service policy (subscription) level of the customer represented by the Customer_ID (e.g., attributes such as QoS level, permissions, access control information, cluster availability for the current service level, remediation settings, geographic location permitted for compute nodes within a selected cluster, notification schemes, etc.) and other attributes. 
     After receipt of the software license credentials  642 , to enroll for access to the malware detection system  100 , the sensor  110   1  of the sensors  110   1 - 110   M  establishes a communication session  652  with the enrollment logic  650 . During the communication session  652 , as shown in  FIG. 6B , the enrollment logic  650  receives an enrollment request message  654 , which includes information that identifies the sensor  110   1  (or the subscriber) at the subscriber site  112 . The identifying information may include the Customer_ID, sensor identifier (ID), username, password, and/or keying material. Based on this information, the enrollment logic  650  authenticates the sensor  110   1  through use of a directory (e.g., LDAP lookup), and upon authentication, returns a network address  658  to the sensor  110   1 , such as a uniform resource locator (URL) for example, for accessing the cloud broker  610  of  FIG. 6A . 
     Additionally, as represented by transmission medium  659 , the enrollment logic  650  may be communicatively coupled to the cloud broker  610  of  FIG. 6A  to directly provide an array of attributes, as briefly discussed above, associated with the subscribed customer and/or enrolled sensor  110   1 . The attributes may be used by the cloud broker  610  of  FIG. 6A  in assigning a cluster to handle malware analyses on objects provided by the enrolled sensor  110   1  (e.g., selection of the cluster may be based on sensor location; sensor assigned QoS; customer subscription level; etc.). Besides subscription attributes, the attributes may include factory set attributes, customer configurable via a command line interface (CLI) or web user interface offered by the sensor or on-premises management system  606 , or cloud-configured attributes via connectivity between a customer console (e.g., web portal) that can access cloud services. Additionally, one or more attributes (operationally dynamically generated attributes) may be generated dynamically during operation of the malware detection system, for example, an attribute may specify aspects of a history of communications (e.g., email or web downloads; number or rate of data submissions for in-depth analysis) with the sensor  110   1 , where the history may assist in the selection of the cluster for the enrolled sensor  110   1 . 
     As a result, as shown in  FIG. 6A , the sensor  110   1  may establish communications with the cloud broker  610  through transmission of the analysis request message  125  which, in turn, establishes the communication session  155  with the selected broker compute node (e.g., broker  300   1 ). Thereafter, the sensor  110   1  may provide a data submission  124  (including at least metadata  122 ) to commence analysis of the object  120 . Of course, in the event that the sensor  110   1  has not been authenticated via the enrollment logic  650 , no data submissions by the sensor  110   1  are forwarded by the cloud broker  610  to a selected cluster (e.g., cluster  185   1 ) for processing. 
     Alternatively, in accordance with a second embodiment of the disclosure as shown in  FIG. 6B , in lieu of a sensor directly interacting with the malware detection system  100  for enrollment, the on-premises management system  606  may be configured to indirectly enroll a sensor (e.g., sensor  110   3 ). Communicatively coupled to the sensor  110   3 - 110   M , the on-premises management system  606  monitors and/or controls operability of the sensor  110   3 - 110   M  at subscriber site  114 . In response to a triggering event occurring for sensor  110   3 , the on-premises management system  606  establishes a communication session  660  with the enrollment logic  650  on behalf of the sensor  110   3 . As described above, via the on-premises management system  606 , the enrollment logic  650  authenticates the sensor  110   3 , where the authentication may include confirming that the sensor  110   3  features an active license to the malware detection system  100 . Such confirmation may be accomplished by, after receipt of an enrollment request message  662  via the on-premises management system  606  by enrollment logic  650 , determining that the message  662  includes information stored in a database in the enrollment logic  650  that identifies the sensor  110   1  and/or the customer associated with the sensor  110   3  (e.g., Customer_ID, username, password, and/or keying material associated with the sensor  110   3 ). Upon authentication of the sensor  110   3 , the URL  658  is acquired by the enrollment logic  650  and returned to the sensor  110   3  via the on-premises management system  606 . 
     B. Data Submission 
     Referring back to  FIG. 6A , after successful enrollment, the sensor  110   1  establishes the communication session  612  with the cloud broker  610  (illustrated separately from signaling that establishes the session  612 ). In particular, the sensor  110   1  transmits an analysis request message  125  to the cloud broker  610 , which operates as a proxy on a per sensor basis. According to one embodiment of the disclosure, the analysis request message  125  may include at least an identifier for the sensor  110   1  (hereinafter, “Sensor_ID”  614 ) and some or all of the service policy level information  127 . The Sensor_ID  614  may be used in selecting a cluster (e.g., cluster  185   1 ) and a broker compute node of the cluster  185   1  (e.g., broker node  300   1 ) to handle malware analyses for the sensor  110   1 . The Sensor_ID  614  is also passed with the metadata  122  from the sensors  110   1  for storage within the distributed queue  310  and subsequently retrieved from the queue  310  by one of the compute nodes  300   1 - 300   P  for use (if needed) in retrieval of the corresponding object  120  for analysis. The Sensor_ID  614  accompanies the malware analysis results of the object  120 , which are returned from the cluster  185   1  to the sensor  110   1 , and the operational metadata  150  received from the cluster management system  190 . A mapping between Sensor_IDs and their corresponding Customer_IDs is accessible to the cloud broker  610  via the one or more databases described above. 
     Additionally, a portion of the service policy level information  127  (e.g., Customer_ID) may be used in controlling operation of the object evaluation service  180 , such as selecting a cluster to handle malware analyses for the sensor  110   1 . However, according to this embodiment of the disclosure, the Customer_ID is not forwarded to the selected cluster  185   1 . Rather, using the Sensor_ID or the Customer_ID as a lookup parameter, the cloud broker  610  may be configured to access one or more databases within the malware detection system  100  (e.g., within the second subsystem  160 ) to collect subscription information that may influence cluster selection. Examples of the subscription information may include, but are not limited or restricted to subscription tier value, QoS threshold(s) based on the subscription level, cluster availability based on the subscription level (e.g., the default cluster for the subscription, cluster selection ordering or preferences if the default cluster is unavailable or is unable to satisfy the QoS threshold(s), cluster restrictions, etc.), geographic location permissions or restrictions for compute nodes associated with the selected cluster, remediation setting (e.g., type of remediation) set for the customer, customer notification scheme preference, and other attributes. 
     It is contemplated that the entire communication session  155 / 512  between the sensor  110   1  and the cluster  185   1  via the cloud broker  610  may remain active until a session termination event has occurred. One example of a session termination event may occur in response to the sensor  110   1  detecting that its local data store has no suspicious objects currently awaiting processing by object evaluation service  180 . Detection of this event may cause the sensor  110   1  to terminate the existing communication session  612  with the cloud broker  610 . As another example, a session termination event may occur when the communication session  612  between the sensor  110   1  and the cloud broker  610  has been active for a duration that exceeds a prescribed period of time or a scheduled take-down of the selected cluster  185   1  is to occur. The monitoring of the duration of the communication session  612  may be handled by the cloud broker  610 , sensor  110   1 , or its on-premises management system  606 , in conjunction with the cluster management system  190 . The termination of the communication session  612  may be handled once all suspicious objects from the sensor  110   1  that were awaiting analysis by the selected cluster  185   1  prior to the session termination event have been completed. 
     Referring still to  FIG. 6A , the system monitoring logic  630  is communicatively coupled to the cloud broker  610  of the first subsystem  130  and the cluster management system  190  of the second subsystem  160 . Configured to provide the cloud broker  610  with sufficient visibility of cluster and/or sensor operability, the system monitoring logic  630  collects, on a periodic or aperiodic basis, the operational metadata  150  from the cluster management system  190 . Thereafter, the system monitoring logic  630  provides the cloud broker  610  with either access to a portion of the operational metadata  150  or with cluster selection values  157  that can be based on at least portions of the operational metadata  150  representing the operability and availability of the clusters  185   1 - 185   N  hosted by the object evaluation service  180  and on the service policy level information  127  associated with the subscription for the customer (e.g., attributes associated with a particular sensor or a subscriber such as QoS level, permissions, access control information such as URL for accessing the cloud broker  610 , etc.). 
     According to one embodiment of the disclosure, the cluster selection values  157  may be based on cluster-based metadata, e.g., metadata representing the availability of each cluster  185   1 - 185   N  to analyze an incoming object for malware. For example, the cluster selection values  157  may be based on cluster queue size and cluster workload. The cluster selection values  157  may also or alternatively include information that represents a higher level of specificity than the foregoing cluster-based metadata, e.g., subscriber-based metadata and/or compute node (CN) based metadata. 
     Examples of the cluster-based metadata, subscriber-based metadata and CN-based metadata include some or all of the following: 
     Cluster-Based Metadata: Operational information regarding the cluster(s), including (i) workload (e.g., cluster workload or utilization level, etc.); (ii) location (e.g., cluster geographic location, etc.); (iii) configuration (e.g., software profile(s) supported by cluster, etc.); and/or (iv) storage capacity (e.g., queue size for use in storage of metadata awaiting processing to prompt fetching of the corresponding object, etc.). 
     Subscriber-Based Metadata: Operational information regarding the customer(s) or one or more of the sensors of the customer(s), including: (i) submission rate (e.g., number of objects submitted (per sensor or per subscriber) over a given time period or other aggregate, rate of submission over a given time period such as number of objects submitted” divided by “given time period,” etc.); (ii) submission type (e.g., types of objects submitted (per sensor or per subscriber) over a given time period or other aggregate, etc.); and/or (iii) detection rate (e.g., number of submitted objects determined as potentially malicious by a cluster over a given time period or other aggregate, etc.). 
     CN-Based Metadata: (i) node workload (e.g., workload or utilization level of a particular compute node “CN”, etc.); (ii) location (e.g., geographic location of the particular CN, etc.); (iii) configuration (e.g., software profile(s) supported by the particular CN, etc.); and/or (iv) rate of submission (e.g., “number of objects” divided by “given time period” by the particular CN). 
     It is contemplated that the system monitoring logic  630  may include a software module (e.g., a rule-based routine) that is configured to receive the service policy level information  127  (e.g., customer preferences) that may influence the selection of a cluster and/or a compute node within that selected cluster. For instance, as an illustrative example, the system monitoring logic  630  may be accessible by the customer or a third party associated with the customer via the cloud broker  610 . The cloud broker  610  may provide a web-based interface, which includes subscriber-selectable preferences for object processing (e.g., types of software profiles, workload thresholds, geographic location based on sensor location, etc.). The access by the customer may be effected via the on-premises management system  606  or other computer system or device. Upon selection, the service policy level information  127  may be passed to the system monitoring logic  630 . As an illustrative example, the customer (or third party) may select only compute nodes that feature a certain software profile or certain software profiles to conduct virtual machine-based behavioral analysis of an object for malware originating from the subscriber&#39;s network, thereby eliminating those clusters that do not feature compute nodes with the software profile(s). Additionally, or in the alternative, compute node selection may be at least partially performed automatically (without subscriber input) based on at least a portion of the service policy level information  127  (e.g., Customer_ID), which may restrict or enlarge the types of compute nodes or groupings of compute nodes based on subscription level, geographic location based on the location of sensor having the object for submission, etc.). 
     In order to ensure compute node configurability, the system monitor logic  630  may be configured to provide cluster selection values  157  that include metadata used by the cloud broker  610  to control what compute node or compute nodes are permitted to process submitted objects from a particular subscriber. For instance, this metadata (e.g., a portion of metadata  122  as illustrated in  FIG. 1 ), which is used in the retrieval of an object for malware analysis, may signal the cloud broker  610  to appropriately tag the metadata  122  prior to transmission to a targeted broker compute node (e.g., broker compute node  300   1 ) of a selected cluster for temporary storage in the cluster queue  310 . The tag may be used to identify preferred or requisite compute nodes (or group of compute nodes) for recovery of the metadata  122  for subsequent retrieval of a corresponding object for malware analysis. Each compute node (e.g., compute 300 1 ), when accessing the cluster queue  310  to retrieve metadata, may scan the queue  310  for a prescribed time or prescribed number of entries (e.g., less than 10) to determine whether any of the queued metadata is targeted for exclusive handling by that compute node  300   1  (or a group of which the compute node is a member). If so, the compute node  300   1  may retrieve that metadata thereby deviating from a first-in, first-out (FIFO) queue retrieval scheme. 
     The FIFO retrieval scheme may be the default retrieval scheme for all compute nodes (e.g., compute node  300   1 - 300   P ) in a cluster (e.g., cluster  185   1 ) in some embodiments. In such embodiments, upon completing processing of an object, the compute node  185   1  simply retrieves the metadata of the next entry in the queue  310  that remains unprocessed and available for processing by a compute node. In other embodiments that are equipped to provide certain subscribers premium service with reduced latency, one or more of the compute nodes of a cluster may be preselected (or an entire cluster is selected) to deviate from a FIFO retrieval scheme, whereby each of these compute node(s) seeks to next process an entry tagged as being from premium service customers. For example, these compute node(s) may check for the next tagged entry in the queue  310  corresponding to such premium service or premium service subscriber, and process that entry. In some embodiments, the compute node(s) may check only “n” next entries in the queue  310 , where the number “n” is a positive integer, and if such an entry is not found, returns to retrieval of the metadata through a FIFO scheme by default so as to select the least recent (top) available entry. 
     Upon receipt of the cluster selection values  157 , the cloud broker  610  is better able to select a cluster (e.g., cluster  185   1 ) from the cluster  185   1 - 185   N  for handling analyses of objects from the sensor  110   1 , where such selection is governed by policy and routing rules within the rules engine  142 . The selection of the cluster (e.g., cluster  185   1 ) may be based, at least in part, on the cluster selection values  157  and/or content within the analysis request message  125  itself (e.g., service policy level information  127 ) as applied to the policy and routing rules by the rules engine  142  (see  FIG. 7 ) within the cloud broker  610 . Stated differently, the cluster selection values  157  provided from the system monitoring logic  630  and/or at least a portion of the service policy level information  127  provided from the sensor  110   1  or accessed from a data store accessible by the cloud broker  610  (e.g., one or more databases) operate as input for the policy and routing rules within the rules engine  142 . Upon selection of the cluster, a new communication session (e.g., tunnel) is established between the cloud broker  610  and one of the broker compute nodes within the cluster  185   1  for receipt of data submissions from the sensor  110   1 . 
     Additionally, the policy and routing rules controlling operations of the cloud broker  610  may be designed to confirm compliance with one or more performance and/or operation thresholds for the selected subscription level by comparing values associated with certain cluster selection values  157  (or operational metadata  150 ) to values associated with certain attributes within the service policy level information  127 . In response to determining that the operability of the cluster  185   1  is not compliant with performance and/or operation thresholds for a subscription level selected by the customer (e.g., failure to satisfy a prescribed number of performance thresholds or a particular performance threshold, number of submissions exceeds a prescribed maximum, etc.), the cloud broker may issue an alert to the sensor  110   1  regarding detected non-compliance. The alert may include a message that is routed to an on-premises management system or an endpoint  608  controlled by an administrator provides one or more suggestions to improve performance (e.g., increase capacity through an increased subscription level, sensor-cluster rebalancing, decrease configurable analysis parameters such as analysis time per object or number of analyses performed per object, terminate communications with the selected cluster and seek a different cluster, etc.). 
     As an illustrative example, the policy and routing rules of the rules engine  142  may be coded to select from a certain subset of clusters (e.g., clusters  185   1 - 185   2 ), numbering less than the available clusters (e.g., e.g., clusters  185   1 - 185   5 ), based on at least a portion of the service policy level information  127  provided to the sensor  110   1  or the on-premises management system  606 , and/or retrieval of subscription information retrieved using a portion of the service policy level information  127  (e.g., Customer_ID) as described above. Thereafter, the selection of a particular cluster (e.g., cluster  185   1 ) from the subset of clusters (e.g., clusters  185   1 - 185   2 ) may be based on an evaluation of cluster selection values  157  associated with each cluster of the subset of clusters. This evaluation may include (i) a comparison of the current workload of each cluster (e.g., cluster  185   1  and cluster  185   2 ) as represented by certain cluster selection values  157 ; (ii) a determination as to which cluster(s) of the subset of clusters (e.g., clusters  185   1  or  185   2 ) support a software profile needed to process the type of object for analysis (e.g., PDF reader application, word processing application, a web browser) or a software profile required by a particular subscriber as represented by other cluster selection values  157 ; and/or (iii) a determination of the geographic region in which each cluster of the subset of clusters ( 185   1  or  185   2 ) is located as represented by the service policy level information  127 . It is contemplated that the ordering (or weighting) for some or all of these rules may vary for different versions of the policy and routing rules of the rules engine  142 . 
     Other aspects of the operation of the object evaluation service  180  may also be influenced by the service policy level information  127  for the customer and operational metadata related to the clusters of the malware detection system. For example, the cloud broker  610  may cooperate with the system monitoring logic  630  and the cluster management system  190  to assure the analysis of an object by a selected cluster commences or completes prior to a latency threshold being surpassed, where the latency threshold is specified by an attribute, for example, an attributed associated with the subscription tier purchased by a customer or a customer-configured attribute, depending on the embodiment. 
     Besides assigning a sensor to a particular cluster, the cloud broker  610  may be configured to return statistical information  192  in response to the management query message  194 . The statistical information  192  is based on one or more portions of the operational metadata  150  and is included as part of reporting data  193 . The reporting data  193  may be aggregated and displayed, by the on-premises management system  606  or a centralized management system, in a manner that is directed to the operability of any customer (as the Customer_IDs may be cross-referenced to the Sensor_IDs) as well as any sensor, any cluster, or any compute node within one of the clusters. In particular, the management query message  194  may correspond to a request directed to the cloud broker  610  for metadata directed to the operability of a particular cluster, compute node, or sensor. After authenticating the node (e.g., sensor  110   1 ) and/or the user of the node that submitted the management query message  194 , the statistical information  192  may be returned back to that node (see  FIG. 1 ) or routed to another node as the reporting data  193  (See  FIG. 6A ). 
     C. Subscription Service Levels 
     The malware detection system  100  may offer differentiated subscription levels or tiers of service, managed by the cloud broker  610  and the broker compute nodes  300   1 - 300   1  (i&gt;1) in association with the license/enrollment services (described above) or the authentication node (described below). According to one illustrative example, as described above, based on an identifier of the sensor (Sensor_ID) and/or an identifier of the customer (Customer_ID), the cloud broker  610  (acting as an initial coordinator) can query enrollment/license logic  650 / 640  (or authentication node  760  of  FIG. 7 ) to obtain QoS information as part of the service policy level information (including one or more related attributes) stored in a database created for the customer. The customer can pay a premium fee to obtain a higher subscription level that guarantees minimal delays (low latencies) for commencement or completion of analysis of submissions. The cloud broker  610  (and/or a selected broker compute node  300   1 ) can push all data submissions from sensors (and their corresponding subscribers who paid for this higher subscription level) to a high priority queue (an allocated part of queue  310 ) to handle the analysis of the data submission within a pre-agreed time allotment. In contrast data submissions handled by a non-premium level of service (lower subscription level) are provided to a different “standard” queue. Alternatively, the cloud broker  610  (and/or a selected broker compute node  300   1 ) can tag entries in the queue (not shown) as premium requests and the analytic computer nodes will process a number of premium requests before resuming with processing a standard request. 
     As another example, for entry level service, the distributed queue  310  may be monitored by logic within the cloud broker  610  (e.g., accounting and license enforcement service described above), where the malware detection system may limit the total number of data submission per subscriber (subscriber site) per a prescribed time period (e.g., hour/day/week/month/year). Alternatively, the malware detection system may limit the data submissions based on a prescribed amount of content based on the level of service per the subscription (e.g., 1 gigabytes/second “GPS” of traffic for Tier 1 service level and 2 GPS for Tier 2 service level). 
     As yet another example, the data submissions from a certain customer (Customer_ID) or certain sensors (e.g., Sensor_ID) at subscriber sites  112  and/or  114  may be tracked by the cloud broker  610  (and/or selected broker compute node). Such tracking may be conducted where the customer is billed based on the overall usage of the object evaluation service  180 . As a result, the level of subscription paid for by the customer may be used to control throughput, volume of submissions, and/or SLA (service level agreement). 
     Also, the malware detection system may differentiate service level commitments based on the type of object, for example, URL analysis may be performed in a shorter time than file analysis. Alternatively, different clusters or analytic compute nodes within a single cluster can be dedicated to certain tiers of service or types of object analysis (URLs, email, files, webpages) that may consume more or less time to complete. 
     IV. Cloud Broker Architecture 
       FIG. 7  is an exemplary embodiment of the cloud broker  610  being a portion of the logic implemented within the analysis selection service  140  of  FIG. 1 . The cloud broker  610  offers centralized control of policy and routing decisions for object evaluation service  180  and a level of abstraction that precludes exposure of a particular broker compute node within the clusters  185   1 - 185   N  to the sensors  110   1 - 110   M . This level of abstraction may assist in compliance with certain outbound firewall rules at an enterprise network  600  of  FIG. 6A  that may require a single endpoint connection. According to this embodiment, the cloud broker  610  includes one or more proxy modules  700   1 - 700   R  (R≥1), interface logic  710  and reporting logic  720 . 
     Herein, the proxy module(s)  700   1 - 700   R  include one or more software modules that collectively operate as a proxy server, which conducts load balancing of communications from the sensors  110   1 - 110   M  as governed by the policy and routing rules of the rules engine  142 . The load balancing is based, at least in part, on the cluster selection values  157  that are produced by the system monitoring logic  630  from the collected operational metadata  150 . These cluster selection values  157  are made available to the proxy module(s)  700   1 - 700   R  via interface logic  710 , which provides a mechanism to propagate load-balancing updates to the proxy module  700   1 - 700   R . Configured to select a cluster (and in one embodiment a particular broker compute node), the proxy module(s)  700   1 - 700   R  may use the cluster selection values  157  as input parameters for the rule engine  142  which, based on the policy and routing rules, results in the selection of a particular cluster (e.g., cluster  185   1 ) from the set of clusters  185   1 - 185   N  available to a requesting sensor (e.g., sensor  110   1 ). 
     According to another embodiment, besides the cluster selection values  157 , service policy level information  127  within the analysis request message  125  from the sensor  110   1  may be considered by at least one of the proxy modules (e.g., proxy module  700   R ) in determining a selected cluster (e.g., cluster  185   1 ). For instance, as an example, the Sensor_ID included as part of the analysis request message  125  may be provided to at least one of the proxy modules (e.g., proxy module  700   R ), where the Sensor_ID may identify a geographic region of the sensor and the Sensor_ID may be used to retrieve additional service policy level information  127  from a data store within the first subsystem  130  or a data store within the second subsystem  160  (e.g., a database within the subscription review service  170 ). Additionally, or in the alternative, the Customer_ID may be included as part of the analysis request message  125  for use in accessing service policy level information  127  maintained within the cluster broker  610  or stored remotely from the cluster broker  610 . 
     Depending on such information, the proxy module  700   R  may utilize (i) the cluster selection values  157  accessible from the sensor monitoring node  630 , (ii) the Sensor_ID and/or the Customer_ID and its associated service policy level information as other inputs for the policy and routing rules in determining what cluster (and/or broker compute node) to select for communications with the sensor  110   1  (e.g., increasing the cluster selection value associated with a cluster (or compute node) within a certain geographic proximity to the sensor than clusters outside this geographic region). Also, a portion of the service policy level information  127  may be considered by at least one of the proxy modules (e.g., proxy module  700   1 ) in determining the selected cluster (e.g., cluster  185   1 ). For instance, the QoS level may cause the rules engine  142  to consider different cluster(s) or subsets of clusters to which the sensor  110   1  is permitted to communicate. A high QoS level may provide the sensor  110   1  with a greater number of possible clusters than a low QoS level. 
     The reporting logic  720  of the cloud broker  610  gathers metrics from the proxy module(s)  700   1 - 700   R . These metrics may be aggregated to formulate statistical information that is searchable and available to metric collection tools for display. 
     The key storage modules  740  operates as a key value store that maintains state information, including information as to which clusters assigned to which sensors, keying material (e.g., keys) and relevant operational metadata for use by the cloud broker  610 . 
     It is contemplated that the proxy modules  700   1 - 700   R  may utilize information from an authentication node  760 , which operates as a backend system in providing stored information (gathered from one or more subscription review services) that allows the proxy modules  700   1 - 700   R  confirm the accuracy of the service policy level information  127  submitted by the sensor  110   1 . For instance, the authentication node  760  may be used to confirm the subscription, QoS level and/or permissions assigned to the sensor  110   1 . Additionally, the authentication node  760  may include additional information that, when provided to the rules engine  142 , influences cluster selection for the sensor  110   1 . For instance, the Customer_ID may identify a subscriber that may be re-assigned to a higher or lower QoS level based on partial or non-payment of the subscription. 
     V. Alternative Cloud Broker Architecture 
       FIG. 8  is a block diagram of a second exemplary embodiment of a scalable, malware detection system is shown. Herein, the first subsystem  130  further comprises cloud-based analysis logic  800 , a local data store  810  and a global data store  820 . The cloud-based analysis logic  800  provides operability during the enrollment phase and data submission phase of the malware analysis. In particular, the cloud-based analysis logic  800  is configured to receive an enrollment request message  830 , which includes information  832  that identifies the sensor  110   1  and/or the subscriber associated with the sensor  110   1  (e.g., username, password, keying materials such as a key, etc.). Based on this information, the cloud-based analysis logic  800  routes the information  832  to the enrollment logic  650  located at the second subsystem  160 . 
     As shown, the enrollment logic  650  accesses a directory (e.g., LDAP)  835  to authenticate the sensor  110   1 , and upon authentication, returns access control credentials  840  from the directory  835  to the enrollment logic  650 . The access control credentials  840  may be provided to sensor  110   1  via cloud-based analysis logic  800  or directly to the sensor  110   1  via communication path  845 . The access control credentials  840  may include URL  842 . The keying material assigned to the sensor  110   1  is provided to the cloud broker  610  so that the cloud broker  610  may terminate communications with a cluster (e.g.,  185   1 ) selected to operate with the sensor  110   1 . The URL  842  provides the sensor  110   1  with an ability to access the cloud broker  610  so that the sensor  110   1  can establish communications with the cloud broker  610  to provide a data submission (e.g., metadata  122 ) to commence analysis of the object  120 . As an optional feature, along with the URL  842 , the sensor  110   1  may provide the Sensor_ID  844  and the service policy level information  846 , which may be used to select the particular cluster (e.g., cluster  185   1 ) for interaction with the sensor  110   1 . 
     After receipt of the credentials  840 , the sensor  110   1  may provide a data submission  850  to the cloud-based analysis logic  800 , which conducts a preliminary analysis on the metadata associated with a corresponding object (e.g., metadata  122  associated with the object  120 ) to determine whether the object  120  is suspicious, malicious or benign. The analysis may involve a comparison of contents within the global data store  820 , which may include a black list and a white list. The “black list” includes identifiers for all objects previously detected as malicious by the cluster  185   1  while the “white list” includes identifiers for all objects previously detected as “benign” by the cluster  185   1  or any other cluster of the second subsystem  160 . If the cloud-based analysis logic  800  determines that the object  120  is benign, such as confirming that a portion of the metadata associated with the object compares to an entry in the white list for examples, the cloud-based analysis logic  800  returns analytic results to the sensor  110   1  that identify that the object  120  is not malicious. However, the cloud-based analysis logic  800  is inconclusive or determines that the object  120  is malicious (e.g., the portion of the metadata compares to an entry in the black list), the metadata  122  is provided to the cloud broker  610  for routing to a selected cluster based, at least in part, on the operational metadata  150  retrieved by the system monitoring logic  630 . 
     In the foregoing description, the invention is described with reference to specific exemplary embodiments thereof. However, it will be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the appended claims.