Patent Publication Number: US-10785255-B1

Title: Cluster configuration within a scalable malware detection system

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of priority on U.S. Provisional Patent Application No. 62/313,642, filed Mar. 25, 2016, the entire contents of which are incorporated by references. 
    
    
     FIELD 
     Embodiments of the disclosure relate to the field of cybersecurity. More specifically, one embodiment of the disclosure relates to a scalable, malware detection system. 
     GENERAL BACKGROUND 
     Over the last decade, cybersecurity attacks have become a pervasive problem for internet users as many networked devices and other resources have been subjected to attack and compromised. The attack may involve the infiltration of malicious software onto a network device or concentration on an exploit residing within a network device to perpetrate the cybersecurity attack (generally referred to as “malware”). 
     Recently, malware detection has undertaken three different approaches. One approach involves the installation of anti-virus software within network devices forming an enterprise network. Given that advanced malware is able to circumvent anti-virus analysis, this approach has been determined to be deficient. 
     Another approach involves the placement of dedicated malware detection appliances at various ingress points throughout a network or subnetwork. The malware detection appliances are configured to extract information propagating over the network at the ingress point, analyze the information to determine a level of suspiciousness, and conduct malware analysis 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, this appliance-based approach may exhibit resource constraints. Stated differently, the dedicated, malware detection appliance has a prescribed (and finite) amount of resources (for example, bandwidth and processing power) that, once fully in use, requires either the malware detection appliance to resort to more selective traffic inspection or additional (and/or upscaled) malware detection appliances to be installed. The later solution requires a large outlay of capital and network downtime, as IT resources are needed to install the new malware detection appliances. Also, these dedicated, malware detection appliances provide limited scalability and flexibility in deployment. 
     Yet another approach involves the use of exclusive, cloud-based malware detection appliances. However, this exclusive, cloud-based solution suffers from a number of disadvantages, including the inability of providing on-site deployment of resources at an enterprise&#39;s premises (e.g., as devices that are part of the enterprise&#39;s network infrastructure). On-site deployment may be crucial for compliance with requirements as to personally identifiable information (PII) and other sensitive information including those mandated at local, state, country or regional governmental levels. 
    
    
     
       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 an exemplary embodiment of a malware detection system. 
         FIG. 2  is a first exemplary embodiment of logic implemented within a cluster operating as part of the centralized analysis system of  FIG. 1  deploying an asynchronous load balancing architecture. 
         FIG. 3  is a block diagram of an exemplary embodiment of logic implemented within a sensor deployed within the malware detection system of  FIG. 1 . 
         FIG. 4  is a block diagram of an exemplary embodiment of logic implemented within a computing node configured in accordance with an asynchronous load balancing architecture. 
         FIG. 5A  is a block diagram of an exemplary embodiment of logic implemented within an analysis coordination system that is operating as part of the computing node of  FIG. 4 . 
         FIG. 5B  is a block diagram of an exemplary embodiment of logic implemented within an object analysis system that is operating as part of the computing node of  FIG. 4 . 
         FIG. 6  is a flow diagram of operations conducted by an exemplary embodiment of logic implemented within the sensor of  FIG. 3  and the computing node of  FIG. 4 . 
         FIG. 7  is a flow diagram of operations conducted by an exemplary embodiment of logic implemented within the analysis coordination system of  FIG. 5A  and the object analysis system of  FIG. 5B . 
         FIG. 8  is a second exemplary embodiment of logic implemented within a cluster operating as part of the centralized analysis system of  FIG. 1  deploying a synchronous load balancing architecture. 
         FIG. 9  is a block diagram of an exemplary embodiment of logic implemented within a computing node configured in accordance with the synchronous load balancing architecture. 
         FIG. 10  is a block diagram of an operational flow between exemplary embodiments of a sensor, an analysis coordination system, and an object analysis system within a cluster of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present disclosure generally relate to a cluster architecture for a scalable, malware detection system. The scalable, malware detection system may be configured in accordance with either an asynchronous load balancing architecture (see  FIGS. 2-7 ) or a synchronous load balancing architecture (see  FIGS. 8-10 ). Each of these architectures includes one or more sensors and one or more clusters of computing nodes. 
     In general, the asynchronous load balancing architecture relies on a distributed queue architecture for each cluster, where each computing node within that cluster individually determines whether it has sufficient resources to conduct an analysis for malware on objects provided from one or more on-site sensors (described below). Stated differently, each of the computing nodes for a cluster has access to the distributed queue and retrieves metadata associated with suspicious objects awaiting malware analysis from the queue upon determining that it has sufficient resources to conduct malware analysis on a suspicious object. In contrast, the synchronous load balancing architecture deploys a computing node with a load balancing logic that is configured to receive load information from logic within each of the computing nodes within the cluster. Based on the load information, the load balancing logic selects the one of these computing nodes that is responsible for analyzing a suspicious object for malware. 
     Each cluster is a scalable architecture that includes at least one computing node and allows additional computing nodes to be added as the network traffic increases. Highly scalable in number based on network load, the cluster of computing nodes is configured so that the computing nodes collectively analyze suspicious objects received from the sensors. In particular, a particular computing node of the cluster obtains metadata associated with a suspicious object from a distributed queue when the computing node is available to conduct a malware analysis of a suspicious object. The metadata is used by the computing node to obtain the suspicious object from the second data store. 
     Based on the results of the malware analysis, the computing node may determine that the suspicious object is associated with malware. The results of the malware analysis may include, but is not limited or restricted to (i) information that classifies the object as potentially malicious or benign, (ii) a hash value of the suspicious object, (iii) information that denotes a severity of the malware associated with the suspicious object determined to correspond to a malicious object, and/or (iv) resultant information from the analysis of the suspicious object. The resultant information may be used, at least in part, to generate an alert that is reported to a targeted entity (e.g., network administrator, a management system, a forensics analysis system, or the like). The resultant information may also be utilized in the generation of a threat (malware) signature used locally by the computing node or globally by other computing nodes and/or clusters. Additionally, the resultant information may be uploaded to the distributed data store for use as analytic data. 
     As described herein, with respect to the cluster configured in accordance with an asynchronous load balancing architecture, each computing node includes an analysis coordination system (also referred to as “analysis coordinator”) and an object analysis system (also referred to as an “object analyzer”). Depending on the role of the computing node, the analysis coordination system may be activated or deactivated. For instance, when the analysis coordination system is activated, the computing node is configured to operate as a “broker” computing node (e.g., a network device that is selected to directly communicate with any or all of the sensors that are assigned to use the cluster for more in-depth malware analysis of a suspicious object). As a “broker” computing node, the analysis coordination system may be responsible for (i) analyzing the metadata to determine whether the suspicious object is identical or substantially similar to an object that has previously undergone malware analysis within a cluster of the malware detection system, and if so, reporting the results of the previous malware analysis to the sensor; (ii) distributing the metadata associated with the suspicious object to the distributed data store, where at least a portion of the metadata may be used by an object analysis system (of the same computing node or a different computing node) to obtain the suspicious object for processing; and/or (iii) monitoring for timeout events based on a lack of timely analysis of the suspicious object corresponding to the metadata within the distributed data store. As an optional feature, the analysis coordination system may be responsible for assigning an identifier in response to receipt of metadata corresponding to the suspicious object as provided by a sensor. 
     According to one embodiment of the disclosure, as described above, the metadata provides information that enables the analysis coordination system to determine whether an identical (or substantially similar) object was previously analyzed for malware and to monitor for a timeout event caused by delayed analysis of the suspicious object. As such, the metadata may include a representation of the suspicious object (e.g., checksum, hash, etc.), and/or an identifier associated with the object and its corresponding metadata (e.g., assigned name, sequence of characters, etc.), which is sometimes referred to as the “object identifier”. 
     Independent of the analysis coordination system being activated or deactivated, in response to confirming sufficient resources, one of the object analysis systems associated with cluster of computing nodes accesses the metadata in the distributed queue. The object identifier within the stored metadata is used to obtain the suspicious object from the second data store, namely a data store external to the cluster. Upon receipt of the suspicious object, the object analysis system is configured to conduct one or more analyses on the object to determine whether the likelihood of the suspicious object being associated with malware exceeds a second prescribed threshold that may be greater than the first prescribed threshold. Examples of these analyses may include a static analysis (analysis of characteristics of the suspicious object), a behavior analysis (analysis of behaviors of a virtual machine processing the suspicious object), and/or an emulation of the operability of the suspicious object. The results of any analysis or analyses may be stored within the distributed data store, where the results are made available to other computing nodes forming the cluster. The results may also be made available to the sensor that provided the suspicious object for analysis. 
     According to another embodiment of the disclosure, as described above, the cluster may be configured in accordance with a synchronous load balancing architecture. Herein, the “broker” computing node includes a load balancer that monitors workload of each object analysis system in the cluster. Hence, the analysis coordination system of the broker computing node is configured to determine which object analysis system (of a plurality of object analysis systems) is to handle malware analysis for a newly submitted object. Such routing is accomplished by a proxy server deployed as part of the analysis coordination system. Herein, in lieu of a distributed data store, each object analysis system includes a load monitor that provides load information to each load balancer of the one or more “broker” computing nodes within the cluster. 
     I. Terminology 
     In the following description, certain terminology is used to describe features of the invention. In certain situations, terms “logic,” “engine,” “subsystem,” and “component” may be representative of hardware, firmware and/or software that is configured to perform one or more functions. As hardware, logic (or engine/subsystem/component) 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. 
     Logic (or engine/subsystem/component) may be software in the form of one or more software modules, such as executable code in the form of an executable application, an application programming interface (API), a subroutine, a function, a procedure, an applet, a servlet, a routine, source code, object code, a shared library/dynamic load library, or one or more instructions. These software modules 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 is 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 Hypertext Transfer Protocol (HTTP), HTTP Secure (HTTPS), Simple Mail Transfer Protocol (SMTP), iMESSAGE, Post Office Protocol (POP), Instant Message Access Protocol (IMAP), or the like. Hence, each message may be in the form of one or more packets, frames, or any other series of bits having the prescribed format. Messages may correspond to HTTP data transmissions, email messages, text messages, or the like. 
     According to one embodiment, the term “malware” may be construed broadly as any code or activity that initiates a malicious attack or any operations associated with anomalous or unwanted behavior. For instance, malware may correspond to a type of malicious computer code that executes an exploit to take advantage of a vulnerability, for example, to harm or co-opt operation of a network device or misappropriate, modify or delete data. In the alternative, malware may correspond to an exploit, namely information (e.g., executable code, data, command(s), etc.) that attempts to take advantage of a vulnerability in software and/or an action by a person gaining unauthorized access to one or more areas of a network device to cause the network device to experience undesirable or anomalous behaviors. The undesirable or 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 (a file is opened by a first process where the file is configured to be opened by a second process and not the first process); (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 yet another alternative, malware may correspond to information that pertains to the unwanted behavior such as a process that causes data such as a contact list from a network (endpoint) device to be uploaded by a network to an external storage device without receiving permission from the user. 
     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. 
     The term “network device” should be construed as any electronic device with the capability of processing data and connecting to a network. Such a 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 network device may include, but are not limited or restricted to, a laptop, a mobile phone, a tablet, a computer, standalone appliance, a router or other intermediary communication device, etc. Other examples of a network device includes a sensor (described above) as well as a computing node, namely hardware and/or software that operates as a network device to receive information from a sensor, and when applicable, perform malware analysis on that information. 
     The term “transmission medium” may be construed as a physical or logical communication path between two or more network devices (e.g., any devices with data processing and network connectivity such as, for example, a sensor, a computing node, mainframe, a computer such as a desktop or laptop, netbook, tablet, firewall, smart phone, router, switch, bridge, etc.) or between components within a network device. 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” is a collection of data including an object and/or metadata associated with that object. The term “object” generally relates to content having a logical structure or organization that enables it to be classified for purposes of analysis for malware. 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 may be retrieved from information in transit (e.g., a plurality of packets) or 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. 
     Herein, a “flow” generally refers to related packets that are received, transmitted, or exchanged within a communication session, where multiple (two or more) flows each being received, transmitted or exchanged within a corresponding communication session is referred to as a “multi-flow”. A “data element” generally refers to as a plurality of packets carrying related payloads, e.g., a single webpage 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. Therefore, “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. Scalable Malware Detection System 
     Referring to  FIG. 1 , an exemplary block diagram of a distributed, malware detection system  100  is shown. The malware detection system  100  comprises one or more sensors  110   1 - 110   M (M≥1) that are communicatively coupled to a centralized analysis system  140 . Some or all of the centralized analysis system  140  may be located at an enterprise&#39;s premises (e.g., located as any part of the enterprise&#39;s network infrastructure whether located at a single facility utilized by the enterprise or at a plurality of facilities). As an alternative embodiment, some or all of the centralized analysis system  140  may be located outside the enterprise&#39;s network infrastructure, generally referred to as public or private cloud-based services that may be hosted by a cybersecurity provider or another entity separate from the enterprise (service customer). Obtaining a high degree of deployment flexibility, embodiments can also provide “hybrid” solutions, where the malware detection system  100  can include some of the centralized analysis system  140  located on premises and some as a cloud-based service. This provides optimal scaling with controlled capital expense as well as the ability to control location(s) of deployments to satisfy local requirements, e.g., as to sensitive information. 
     As shown in  FIG. 1 , the sensors  110   1 - 110   M  may be positioned at various locations on a transmission medium  115  that is part of the network  120  (e.g., connected at various ingress points on a wired network or positioned at various locations for receipt of wireless transmissions) and monitor data traffic propagating over the transmission medium  115 . The “traffic” may include an electrical transmission of files, email messages, or the like. For instance, each sensor  110   1 - 110   M  may be implemented either as a standalone network device, as logic implemented within a network device, or integrated into a firewall, or as software running on a network device. 
     More specifically, according to one embodiment of the disclosure, the sensor  110   1  may be implemented as a network device that is coupled to the transmission medium  115  directly or is communicatively coupled with the transmission medium  115  via an interface  125  operating as a data capturing device. According to this embodiment, the interface  125  is configured to receive the incoming data and subsequently process the incoming data, as described below. For instance, the interface  125  may operate as a network tap (in some embodiments with mirroring capability) that provides at least one or more data submissions (or copies thereof) extracted from data traffic propagating over the transmission medium  115 . Alternatively, although not shown, the sensor  110   1  may be configured to receive files or other objects automatically (or on command), accessed from a storage system. As yet another alternative, the sensor  110   1  may be configured to receive information that is not provided over the network  120 . For instance, as an illustrative example, the interface  125  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. 
     As further shown in  FIG. 1 , one sensor  110   1  may be deployed individually or multiple sensors  110   1 - 110   M  may be positioned in close proximity, perhaps sharing the same power source (e.g., common bus plane as described below). The sensors  110   1 - 110   M  are configured to receive intercepted or copied data traffic and conduct an analysis on one or more packets within the data traffic to determine whether any packet or a set of related packets (flow or multi-flow) is suspicious. Such analysis may involve a determination as to whether any packets are sourced by or directed to a particular network device in a “blacklist” or a determination as to whether the body of the packet includes a certain data pattern. In the event that one or more of the packets are determined as suspicious, the monitoring sensor uploads a data submission, including metadata and an object for analysis, to the centralized analysis system  140 . 
     Although not shown, it is contemplated that the sensor  110   1  may be implemented entirely as software for uploading into a network device and operating in cooperation with an operating system running on the network device. For this implementation, the software-based sensor is configured to operate in a manner that is substantially similar or identical to a sensor implemented as a network device. Hence, the logic for the software-based sensor corresponds to software modules that, when executed by a processor, perform functions similarly to the functions performed by logic that is part of the sensor implemented as a network device. 
     The centralized analysis system  140  features one or more clusters of computing nodes  150   1 - 150   N  (N≥1), where these computing nodes are grouped in order to conduct collective operations for a set of sensors (e.g., sensors  110   1 - 110   M ). Each cluster  150   1 - 150   N  may include computing nodes equipped for malware analysis, including behavioral monitoring while executing (running) objects within one or more virtual machines (VMs). The virtual machines may have different guest image bundles that include a plurality of software profiles each with a different type of operating system (OS), application program, or both. Alternatively, each cluster  150   1 - 150   N  may include computing nodes having identical guest image bundles that include software profiles directed to the same operating system (e.g., Windows® OS cluster, MAC® OS X cluster, etc.). Additionally, the cluster  150   1 - 150   N  may be located to communicate with sensors within the same state, Provence, region or country to ensure compliance with governmental regulations. 
     As shown, for illustrative purposes, a cluster  150   1  may include a plurality of computing nodes  160   1 - 160   P  (P≥1). The plurality of computing nodes  160   1 - 160   P  may be arranged in a “blade server” type deployment, which allows additional computing nodes to be seamlessly added to or removed from the cluster  150   1  (e.g., computing nodes  160   1 - 160   P  being connected to a common bus plane (network) that may provide both power and signaling between the computing nodes, a hot-swapping deployment of the computing nodes forming the cluster  150   1 , or any other deployment that allows a scalable computing node architecture). However, it is contemplated that any or all of clusters  150   1 - 150   N  may be virtualized and implemented as software, where the computing nodes  160   1 - 160   P  are software modules that communicate with each other via a selected communication protocol. 
     Additionally according to this embodiment of the disclosure, each of the clusters  150   1 - 150   N  (e.g., cluster  150   1 ) is communicatively coupled to a distributed data store  170  and a distributed queue  175 . The distributed data store  170  and the distributed queue  175  may be provided through a separate memory node  180 , which is communicatively coupled to and accessed by computing nodes  160   1 - 160   P . For this embodiment, a data store  182  for storage of the malicious objects (hereinafter “object data store”) may be provided in memory node  180 . Alternatively, as shown, it is contemplated that the distributed data store  170  and the distributed queue  175  may be provided as a collection of synchronized memories within the computing nodes  160   1 - 160   P  (e.g., synchronized data stores  170   1 - 170   P  that collectively form distributed data store  170 ; synchronized queues  175   1 - 175   P  that collectively form distributed queue  175  where each of the queues  175   1 - 175   P  is synchronized to store the same information) each accessible by the computing nodes  160   1 - 160   P  respectively. The distributed data store  170  (formed by local data stores  170   1 - 170   P  operating in accordance with a selected memory coherence protocol) are accessible by the computing nodes  160   1 - 160   P , and thus, data stores  170   1 - 170   P  may be configured to store the same information. Alternatively, the data stores  170   1 - 170   P  may be configured to store different information, provided the collective information is available to all of the computing nodes  160   1 - 160   P  in the same cluster  150   1 . 
     Referring still to  FIG. 1 , one or more management systems  192  may be communicatively coupled to the centralized analysis system  140 , where such communications allow for an exchange of information. For instance, although not shown, a management system may be coupled to the interconnects between the computing nodes  160   1 - 160   P  of each of the clusters  150   1 - 150   N . As a result, the management system may be configured to receive local threat signatures generated by an object analysis system of a specific computing node (e.g., computing node  160   P ), and thereafter, proliferate these signatures to other computing nodes  160   1 - 160   P−1  and/or other clusters  150   2 - 150   N  throughout the malware detection system  100 . 
     In order to provide sufficient processing capabilities to the sensors  110   1 - 110   M  deployed throughout the network  120 , the centralized analysis system  140  is scalable by allowing a flexible clustering scheme for computing nodes as well as allowing for the number of clusters to be increased or decreased in accordance with system processing capability. Stated differently, one or more computing nodes (e.g., computing node  160   P+1 ) may be added to the cluster  150   1  based on an increase in the current workload of the malware detection system  100 . Likewise, one or more computing nodes may be removed from the cluster  150   1 , now forming computing nodes  160   1 - 160   P−1 , based on a decrease in the current workload. 
     As an optional feature, one or more of the clusters  150   1 - 150   N  may be configured with reporting logic  184  to provide alerts to a customer such as a network administrator  190  of the customer for example, that identify degradation of the operability of that cluster. For example, the reporting logic (illustrated in  FIG. 1  as “customer alert logic  184 ”) may be configured to monitor metadata within at least one of the queue  175   1  (when the contents of each queue  175   1 - 175   P  are identical) for metadata approaching a timeout condition (e.g., where the amount of time that the metadata has been retained in the queue  175   1 , sometimes referred to as “metadata queuing time,” exceeds a timeout value (e.g., the amount of time remaining to conduct a malware analysis on the object corresponding to the metadata). Herein, a selected time threshold (e.g. within a number of minutes, hours, etc.) is set for the cluster  150   1 , where the threshold may be a fixed time, a variable time that is based on cluster size or other factors such as subscription level or customer preference. Accordingly, upon detecting that a certain number of queued metadata entries will potentially experience a timeout condition within the selected time threshold, the customer alert logic  184  transmits an alert signal to the customer reporting a potential degradation in cluster performance. The alert signal identifies to the customer that procurement of additional computing nodes for the cluster  150   1  may be warranted to avoid anticipated degradation in performance by the cluster  150   1 . 
     As further shown, clusters  150   1 - 150   N  may be configured to provide at least a portion of the malware analysis results for an object to a management system  192  that monitors the health and operability of the network  120  and may include an enrollment service that controls formation of the clusters  150   1 - 150   N  and monitors for an active subscription that indicates whether or not a sensor is authorized to submit objects to a particular cluster or clusters for evaluation and monitors for the type (level) of subscription (e.g., a service level with basic malware analysis functionality, another service level with more robust malware analysis such as increased analysis time per object, increased or user-selectable guest image support, greater quality of service than offered with the basic subscription, access to computing nodes dedicated to processing certain object types, etc.). Additionally, the object and/or analysis results from any of the clusters  150   1 - 150   N  may be provided to a forensic analysis system  194  for further detailed analysis as to confirm that the object is associated with malware and the nature of the malware. Although not shown, the clusters  150   1 - 150   N  may be communicatively coupled to remotely located services to receive threat signatures that identify uncovered malware (or information to formulate threat signatures) from the clusters  150   1 - 150   N  and proliferate these signatures throughout the malware detection system  100 . 
     A. Asynchronous Load Balancing Architecture 
     Referring now to  FIG. 2 , a first exemplary embodiment of logic implemented within the cluster  150   1  that is operating as part of the centralized analysis system  140  of  FIG. 1  is shown. The cluster  150   1  comprises a plurality of computing nodes  160   1 - 160   P , which are communicatively coupled to the distributed queue  175  (logical representation of the collective memory of queues  175   1 - 175   P ) over a first network  250 . Each computing node (e.g., computing node  160   1 ) comprises an analysis coordination system  220   1  and an object analysis system  240   1 . The analysis coordination system  220   1  may be activated or deactivated, where the computing node  160   1  operates as a “broker” computing node when the analysis coordination system  220   1  is activated or operates as an “analytic” computing node when the analysis coordination system  220   1  is deactivated. As an alternative embodiment, it is contemplated that a “broker” computing node may have a logical architecture different than an “analytic” computing node. For example, a broker computing node may be configured with only an analysis coordination system. An analytic computing node may be configured with only an object analysis system. 
     Herein, each computing node  160   1 - 160   P  may include one or more virtual machines (VMs) that can be provisioned with specific guest image instances selected on a per customer basis. The VM configurations may support one or more application versions and/or one or more operating system(s). Additionally, the VMs for each computing node may be provided for dedicated processing of a certain object type such as emails, network traffic including webpages/URLs, or the like. 
     According to this illustrative embodiment, sensors  110   1 - 110   M  are communicatively coupled over a second network  255 , which is different than the first network  250 , to the first cluster  150   1  via the broker computing nodes (e.g., computing node  160   1  and computing node  160   P ). Each analysis coordination system  220   1  and  220   2  is configured to receive metadata from the sensors  110   1 - 110   M , and based on the metadata, fetch corresponding objects for analysis. As an alternative, each analysis coordination system  220   1  and  220   2  may be configured to receive both the metadata and object from the sensors  110   1 - 110   M . 
     More specifically, as shown, the malware detection system  100  features one or more sensors  110   1 - 110   M , each sensor  110   1 - 110   M  is configured to receive information that includes at least metadata  202  and a corresponding object  204 . Upon receipt of the information  200 , a sensor (e.g., sensor  110   1 ) separates the metadata  202  from the object  204  and conducts a preliminary analysis to determine whether the object  204  is suspicious (e.g., meets a first prescribed level of likelihood that the object is associated with malware). The preliminary analysis may include one or more checks (real-time analyses) being conducted on the metadata  202  and/or object  204  without execution of the object  204 . Examples of the checks may include bit pattern comparisons of content forming the metadata  202  or object  204  with pre-stored bit patterns to uncover (i) deviations in messaging practices (e.g., non-compliance in communication protocols, message formats or ordering, and/or payload parameters including size); (ii) presence of content within the object that is highly susceptible to malicious attack; (iii) prior submission via the sensor  110   1  of certain types of objects (or an object that is highly correlated upon determining a shared amount of similar data) to a cluster for malware analysis, and if so, whether or not such malware analysis has been completed (e.g., completed, experienced timeout event, awaiting processing, etc.) or the like. 
     In the event that logic within the sensor  110   1  (e.g., processing engine  600  of  FIG. 6 ) detects that a prior preliminary (or malware) analysis has been conducted on the object  204 , in some instances, the sensor  110   1  may discontinue further analysis of the object  204 , especially when the prior preliminary (or malware) analysis has determined that the object  204  is benign (e.g., not malicious) or malicious (e.g., determined to have some association with malware). For example, where the object  204  is an Uniform Resource Locator (URL) or another type of reference to dynamically changing data, the sensor  110   1  may routinely supply the metadata  202  to its associated broker computing node given the dynamic nature of content associated with the URL (or reference element). However, for other repeated malicious objects, the sensor  110   1  may report the results from the prior analysis to the management system  192  at an elevated level to identify a re-occurring malicious attack. 
     According to one embodiment of the disclosure, this preliminary analysis may involve a comparison between a representation of the object  204  (e.g., bit pattern representation as a hash of the object  204  or portions of the object  204 , certain content of the object  204 , etc.) and stored representations of previously analyzed objects. Optionally, the preliminary analysis may further involve a comparison between the representation of the object  204  and representations of other objects analyzed by the cluster  150   1  (or even other clusters) that have been determined to be benign (whitelist) or malicious (blacklist). 
     Additionally, based on a state of the prior preliminary analysis, the sensor  110   1  may refrain from supplying the metadata  202  to its associated broker computing node (e.g., computing node  160   1  or computing node  160   2 ) to avoid initiating an in-depth malware analysis of the object  204 . As an illustrative example, the sensor  110   1  may refrain from supplying the metadata  202  when a prior submission has recently occurred and such analysis has not yet completed (and no timeout event has been detected). However, for Uniform Resource Locators (URLs) and other references to dynamically changing data, the presence of any prior preliminary analysis may not operate as a filter in determining whether to conduct a check as to whether the object  204  is suspicious. 
     In the event that no prior preliminary analysis of the object  204  has occurred (or occurrence with a timeout event) and the sensor  110   1  conducts a second real-time analysis of the object  204  to detect whether the object  204  is suspicious, but does not detect that the object  204  is suspicious, the sensor  110   1  may refrain from supplying the metadata  202  to its associated broker computing node. In other instances, however, the sensor  110   1  may supply at least a portion of the metadata  202  to its associated broker computing node when the object is determined to be suspicious based on the preliminary analysis. 
     In response to the sensor  110   1  detecting that the object  204  is suspicious, additional metadata may be added to the metadata  202  for storage, including a timeout period that is allocated based, at least in part, on characteristics of object  204  (e.g., object type). Metadata  202  and other metadata produced therefrom produces aggregated metadata  206 , which is provided to one of the broker computing nodes (e.g., computing node  160   1 ) that is assigned to support the sensor  110   1  during a prior enrollment process and to initiate an in-depth malware analysis of the suspicious object  204 . The aggregated metadata  206  may include (i) a sensor identifier (ID)  207  that identifies sensor  110   1  as the source of metadata  202  (e.g., a serial number, a device identifier such as a Media Access Control “MAC” address, an IP address, and/or another identifier unique to the cluster  150   1 ), (ii) a timestamp  208  that denotes a particular time during initial analysis of the suspicious object  204  (e.g., time of receipt, time of detection of suspiciousness, etc.), (iii) a timeout value  209  that denotes a total time remaining from an overall amount of time allocated for malware analysis of the object, (iv) representative content  210  of the suspicious object  204  (e.g., hash value, checksum, etc.), (v) object identifier  211 , and/or (vi) an operation mode identifier  212  (e.g. active or passive). Other optional metadata may include, but is not limited or restricted to source or destination IP addresses, or the like. 
     In particular, a portion of the aggregated metadata  206  (generally referred to as “metadata  206 ”) is analyzed by the analysis coordination system  220   1  to determine whether an identical object or a determined malicious object with similar metadata (e.g., from the same malicious source, etc.) has already been analyzed by any of the computing nodes  160   1 - 160   4 . This may be accomplished by conducting a search of representative objects within the distributed data store  170  as shown in  FIG. 1 . If so, the results of the analysis are returned to the sensor  110   1 . If not, the metadata  206  is loaded into the distributed queue  175  (e.g., queue  175   1 ). The metadata  206  in the queue  175   1  may be accessible by any of the object analysis systems  240   1 - 240   4  of the computing nodes  160   1 - 160   4 , where the metadata  206  identifies the location of the suspicious object  204  that is fetched for further analysis. According to this embodiment, the analysis coordination systems  220   1  and  220   2  have no involvement in the routing of metadata to a particular object analysis system. 
     As shown in  FIG. 2 , the difference between the “broker” computing nodes  160   1  and  160   2  and the analytic computing nodes  160   3  and  160   4  is whether or not the analysis coordination systems have been deactivated. Herein, for the “broker” computing nodes  160   1  and  160   2 , analysis coordination systems  220   1  and  220   2  have been activated while the analysis coordination systems (not shown) for computing nodes  160   3  and  160   4  have been deactivated. It is noted, however, that all of the computing nodes  160   1 - 160   4  within the same cluster  150   1  feature an object analysis system  240   1 - 240   4 , respectively. Each of these object analysis systems  240   1 - 240   4  includes logic that is capable of conducting an in-depth malware analysis of the object suspicious  204  upon determining to have sufficient processing capability. 
     More specifically, each object analysis system  240   1 - 240   4 , when determined to have sufficient processing capability or otherwise determined to have suitable analytical capabilities to meet the required analysis, accesses the queue  175  to obtain metadata associated with a suspicious object awaiting malware analysis. For example, during operation, the object analysis system  240   1  may periodically and/or aperiodically (e.g., in response to completion of a prior malware analysis) access the queue  175  and obtain the metadata  206  associated with the suspicious object  204 . Responsive to obtaining the metadata  206 , the object analysis system  240   1  accesses a portion of the metadata  206  to locate the storage location of the suspicious object  204 , and thereafter, fetches the suspicious object  204 . The suspicious object  204  may be stored in the sensor  110   1 , in the computing node  160   1  or in an external network device (not shown). 
     Upon receipt of the suspicious object  204 , the object analysis system  240   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  204  being associated with malware. The second level of likelihood is at least equal to and likely exceeding (in probability, in computed score, etc.) the first level of likelihood. 
     As shown, the analysis coordination system  220   1  is configured to receive metadata associated with specific objects and provide information, inclusive of some or all of the metadata, to the queue  175 . Thereafter, the analysis coordination system  220   1  has no involvement in the routing of such metadata to any of the object analysis systems  240   1 - 240   4  of the computing nodes. An object analysis system  240   1 , . . . , or  240   4  is configured to fetch metadata that is stored in the queue  175  when that object analysis system is determined to have sufficient processing capability to handle a deeper level analysis of the object. 
     Referring to  FIG. 3 , a block diagram of an exemplary embodiment of logic implemented within the sensor  110   1  deployed within 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  300  (referred to as “processor(s)”), a non-transitory storage medium  310 , and one or more network interfaces  320  (referred to as “network interface(s)”). These components are at least partially encased in a housing  340 , 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. Where the sensor  110   1  is software, the interface may operate as an interface to an Application Programming Interface (API) for example. 
     The processor(s) is a multi-purpose, processing component that is configured to execute logic  350  maintained within the non-transitory storage medium  310  that is operating as a data store. As described below, the logic  350  may include, but is not limited or restricted to, (i) subscription control logic  352 , (ii) packet (object) analysis logic  355 , (iii) metadata extraction logic  360 , (iv) timestamp generator logic  365 , (v) events (timeout) monitoring logic  370 , (vi) metadata data store (MDS) monitoring logic  375 , (vii) notification logic  380 , and/or (viii) result aggregation logic  385 . One example of processor(s)  300  include an Intel® (x86) central processing unit (CPU) with an instruction set architecture. Alternatively, processor(s)  300  may include another type of CPUs, a digital signal processor (DSP), an Application Specific Integrated Circuit (ASIC), a field-programmable gate array (FPGA), 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  352  that controls the signaling (handshaking) with an enrollment service (e.g., within the management system  192  of  FIG. 1 ). Such signaling enables the sensor  110   1  to join a cluster as well as support continued communications with an enrollment service (e.g., within the management system  192  of  FIG. 1 ) to re-evaluate whether the sensor  110   1  should remain in communication with a particular cluster. Additionally, the subscription control logic  352  instance, may detect maintain information associated with the subscription expiration time that, if not extended to a renewal, disables communications with the assigned cluster and potentially signals a customer of renewal payments necessary to continue the subscription (or upgrade to a higher subscription level). 
     As shown, the network interface(s)  320  is configured to receive the information  200 , including metadata  202  and object  204 , directly from the network or via a network tap. The information  200  may be temporarily stored prior to processing. Herein, upon receiving the information  200 , the processor(s)  300  (e.g., packet analysis logic  355 ) may conduct an analysis of at least a portion of the information  200 , such as the object  204  for example, to determine whether the object  204  is suspicious. 
     Upon detecting the object  204  is suspicious, the processor  300  processes the metadata extraction logic  360  that, during such processing, extracts the metadata  202  from the received information  200  and assigns the object identifier  211  for the metadata  202  and the suspicious object  204 , which may be unique for the cluster (referred to as “universally unique identifier” or “UUID”). The metadata  202  along with other information is stored in a metadata data store  390 . The suspicious object  204 , UUID  211  along with certain information associated with the suspicious object  204  may be stored in a content data store  395 . The content data store  395  may be part of the non-transitory storage medium  310  of the sensor  110   1 . It is contemplated, however, that the content data store  395  may be stored externally from the sensor  110   1  in another network device. 
     In response to detecting the storage of the metadata  202  in the metadata data store  390 , the MDS monitoring logic  375  accesses the metadata data store  390  to obtain at least a portion of the aggregated metadata  206 . The portion of the metadata  206  may include (i) a sensor identifier  207 , (ii) a timestamp  208 , (iii) the timeout value  209 , (iv) a representation  210  of the suspicious object  204  (e.g., hash value, checksum, etc.), (v) UUID  211 , and/or (vi) the operation mode identifier  212  (e.g. active or passive), as illustrated. Thereafter, the MDS monitoring logic  375  determines a (remaining) timeout value, which represents an amount of time allocated for analyzing the object  204  for malware that still remains, and provides the metadata  206  to the cluster  150   1 . The MDS monitoring logic  375  may use the timeout period assigned to the object  204  and timestamp  208  to produce the timeout value  209 , representing an amount of the time period that is remaining to complete malware analysis of the object  204 . Thereafter, the MDS monitoring logic  375  generates a request message  376 , including the portion of the metadata  206 , to send to an analysis coordination system associated with a broker computing node that is assigned to service the sensor  110   1 . 
     Additionally, the UUID  211  along with certain information associated with suspicious object  204  may be stored in a content data store  395 . The content data store  395  may include a data store that is part of the non-transitory storage medium  310  of the sensor  110   1 . It is contemplated, however, that the content data store  395  may be stored on the computing node  160   1 , or stored externally from the sensor  110   1  in another network device. 
     For a certain type of object, such as the suspicious object  204  being a file for example, the file and its related UUID are collectively stored in the content data store  395 . For another type of object, such as a URL or a document with an embedded script for example, the URL (or document with the embedded script) along with information associated with network traffic pertaining to the URL (or document with embedded script) may be collectively stored with its related UUID. The information associated with the network traffic may include information associated with web pages accessed via the URL (or script) over a period of time (e.g., during a communication session, portion of a communication session, etc.). 
     Additionally, the sensor  110   1  comprises timestamp generator logic  365 , which is configured to receive a time value from a source clock (e.g., real-time clock, not shown) and generate a timestamp based on the clock value and the received information  200 . For instance, according to one embodiment of the disclosure, the timestamp generator logic  365  generates a timestamp once the packet analysis logic  355  determines that the object  204  is suspicious (and no prior preliminary analysis of the object  204  precludes continued analysis of the object  204  as described above). Of course, it is contemplated that the timestamp generator logic  365  may be configured to generate the timestamp in response to extraction of the metadata by the metadata extraction logic  360  or storage of the suspicious object  204  with the content data store  395 . 
     The sensor  110   1  further includes notification logic  380 , which is responsible for handling communications  377  with particular logic within the computing node  160   1 , namely sensor notification logic (see  FIG. 5A ) or reporting logic (see  FIG. 5B ). Such communications  377  may include (i) analysis results  595  from reporting logic of an object analysis system or (ii) information  596  from the sensor notification logic  520  that signifies (a) the suspicious object  204  has already been analyzed or (b) a timeout event has been detected for the portion of the metadata  206  residing in the queue  175   1  that originated from the sensor  110   1 . 
     As an illustrative example, in response to receipt of communications from the sensor notification logic, which may include the UUID  211  for the suspicious object  204 , the sensor identifier and the unique identifier of a previously analyzed object, the notification logic  380  may access the metadata data store  390  in order to identify that the suspicious object  204  has been processed (e.g., set a timeout indicator associated with an entry of the metadata data store  390  that includes the suspicious object  204 ). Although not shown, the notification logic  380  may further notify the event (timeout) monitoring logic  370  that analysis of the suspicious object  204  has been completed and no timeout events have occurred. 
     Referring to both  FIG. 2  and  FIG. 3 , when the “broker” computing node  160   1  for the sensor  110   1  is operating in a passive mode, as provided by the operation mode identifier  212 , the result aggregation logic  385  of the sensor  110   1  may periodically or aperiodically (e.g., in response to a timeout event) access the distributed data store  170   1  for analysis results or timeout events. The access may be based, at least in part, on the UUID  211 . Alternatively, when the “broker” computing node  160   1  is operating in an active mode, the timeout events associated with suspicious objects detected the sensor  110   1  may be provided from event (timeout) monitoring logic within the broker computing node  160   1  to the notification logic  380  of the sensor  110   1 . Also, the results of an in-depth malware analysis of the suspicious object  204  may be provided to the notification logic  380  of the sensor  110   1  from reporting logic of the computing node handling the in-depth malware analysis (e.g., “broker” computing node  160   1  or another computing node) as well as timeout events detected by the computing node handling the in-depth malware analysis. The notification logic  380  may provide the results of the in-depth malware analysis to metadata data store  390  and/or content data store  395  for storage or may store data to signify completion of the analysis or an occurrence of a timeout event that denotes expiration of the time allocated for conducting malware analysis of the suspicious object  204 . 
     In response to neither the notification logic  380  nor the result aggregation logic  385  receiving information that conveys the suspicious object  204  has been analyzed before a timeout period has elapsed (e.g., no analysis results have been uploaded into the distributed data store  170   1  of  FIG. 1  or provided to notification logic  380 ), the event (timeout) monitoring logic  370  determines that the timeout event has occurred and notifies the processor  300  of the timeout event. Normally, the processor(s)  300  record information associated with the timeout event into a log  398  that maintains analytic data associated with sensor operations (e.g., number of timeout events, number of objects offered for analysis by the sensor  110   1 , etc.). Data, including the stored analytic data, may be sent as messages by the processor(s)  300  to the management system or directly to network administrators at an enterprise being monitored by sensor  110   1 . It is contemplated, however, that the processor(s)  300  may decide to resubmit the suspicious object  204 , where the decision may be based on the type of object and/or the level of suspiciousness associated with that object. 
     Referring now to  FIG. 4 , a block diagram of an exemplary embodiment of logic implemented within the computing node  160   1  that is operating as part of the centralized analysis system  140  of  FIG. 1  is shown. Herein, the computing node  160   1  comprises one or more processors  400 , one or more network interfaces  410 , logic associated with the analysis coordination system  220   1  and logic associated with the object analysis system  240   1 . These components are at least partially encased in a housing  415 , which may be made entirely or partially of a rigid material (e.g., hard plastic, metal, glass, composites, or any combination thereof) that protects the components from environmental conditions. 
     As shown, the processor(s)  400  is figured to activate or deactivate the analysis coordination system  220   1  as illustrated by a control line  420 . When the analysis coordination system  220   1  is activated, the processor(s)  400  supports communications between the analysis coordination system  220   1  and any enrolled sensors (e.g., sensor  110   1 ). The contents of the analysis coordination system  220   1  are shown in  FIG. 5A . 
     Referring to  FIG. 5A , a block diagram of an exemplary embodiment of logic implemented within an analysis coordination system  220   1  that is operating as part of the computing node  160   1  of  FIG. 4  is shown. Herein, according to one embodiment of the disclosure, the analysis coordination system  220   1  features a local storage medium that includes logic, such as request detector/ID generator logic  500 , filtering (pre-analysis) logic  510 , and sensor notification logic  520  for example, that relies on processing functionality provided by the processor(s)  400  and connectivity provided by the network interface(s)  410  of the computing node  160   1 . Of course, it is contemplated that the analysis coordination system  220   1  may be configured to utilize a different processor, such as one or more different processor cores for example, than the object analysis system  240   1  within the same computing node  160   1 . Additionally, the analysis coordination system  220   1  includes a portion of the local storage medium that operates as part of the distributed data store  170   1  (as shown) or has access to the distributed data store  170   1  hosted within a separate memory device as shown in  FIG. 1 . As stated above, the distributed data store  170   1  is accessible by each and every analysis coordination system within the cluster  150   1  that is activated (e.g., analysis coordination systems  220   1 - 220   2  of  FIG. 4 ). 
     The request detector/ID generator logic  500  is configured to detect the request message  376  with the metadata  206  from the MDS monitoring logic  375  of  FIG. 3  and provide the metadata  206  to the pre-analysis (filtering) logic  510 . Identified by dashed lines, it is contemplated that the detector/ID generator logic  500  may be adapted to generate a response message that returns the unique identifier (UUID) for the metadata  206  and the suspicious object  204  to the MDS monitoring logic  375  if the sensor  110   1  does not feature logic to generate an object identifier. 
     The pre-analysis (filtering) logic  510  determines whether the metadata associated with a suspicious object for analysis corresponds to any previously analyzed suspicious object. This determination may involve a comparison of representative content  210  of the suspicious object  204 , which is included as part of the received metadata  206 , against representative content  535  of previously analyzed suspicious objects stored in the distributed data store  170 , including distributed data store  170   1 . The representative content  210  of the suspicious object  204  may include a checksum or a hash value of the suspicious object  204 . It is contemplated that the representative content  210  may include other parameters such as an indicator of a timeout event has occurred during processing of the suspicious object  204  or the original name of the object, especially when the suspicious object  204  is a file. The presence of other parameters may be useful in reducing the chances of false negatives in such detection. 
     Additionally, it is contemplated that the pre-analysis (filtering) logic  510  may be configured to identify one or more characteristics of the suspicious object  204 , and based on the characteristic(s), determine whether further in-depth malware analysis of the suspicious object  204  is not desired in order to reduce workload. For example, the metadata  206  may provide information that identifies the suspicious object  204  is a type of object for which further in-depth malware analysis is not currently targeting or has little significance when compared to other types of objects. As another example, the metadata  206  may identify that the suspicious object  204  originated from a trusted source. Yet as another example, the metadata  206  may identify that the suspicious object  204  is associated with a particular software profile that is different from objects with certain software profiles that are now more frequently under attack. This determination may involve a comparison of the sensor ID  207  and/or the representative content  210  of the suspicious object  204 , which is included as part of the received metadata  206 , against content  535  stored in the distributed data store  170 , including distributed data store  170   1 . 
     In response to determining that the representative content  210  associated with the suspicious object under analysis compares to representative content  535  of a previously analyzed object, the sensor notification logic  520  signals the notification logic  380  of  FIG. 3  within the sensor  110   1  that the suspicious object  204  has already been processed (or no in-depth, behavioral malware analysis is of interest at this time). Such signaling may include the UUID  211  and sensor ID  207  associated with the metadata  206  being processed by the pre-analysis (filtering) logic  510  and the UUID  540  associated with the previously analyzed object. Thereafter, the results  545  of the analysis may be obtained by the sensor  110   1  from the distributed data store  170   1  utilizing the UUID  540  associated with the previously analyzed object or received via the object analysis system conducting an analysis of the suspicious object  204 . It is contemplated that, for types of suspicious objects (e.g., URLs), in-depth malware analyses are conducted even when the representative content  210  associated with the suspicious object  204  compares to representative content  535  of a previously analyzed object. This occurs because the content of websites is dynamic. For these cases, the pre-analysis (filtering) logic  510  may bypass the above-described operations and store a portion of the metadata  206  in the queue  175   1 . 
     In response to determining that the representative content  210  associated with the suspicious object  204  under analysis fails to compare to any representative content associated with previously analyzed objects stored in the distributed data store  170 , the pre-analysis (filtering) logic  510  records the UUID  211  along with the representative content  210  and the sensor ID  207  that are provided as part of the metadata  206  into the distributed data store  170   1 . The results of the analysis are subsequently uploaded to a corresponding entry associated with the UUID  211  at a later time after completion of the malware analysis of the suspicious object  204 . The results may be referenced by other analysis coordination systems (analysis coordinators) within the cluster to mitigate unnecessary workload. 
     The timeout monitoring logic  530  is responsible for monitoring at least two different types of timeout events at the queue  175   1 . For a first type of timeout event, namely the object  204  failing to undergo malware analysis by a prescribed timeout period and, the timeout monitoring logic  530  utilizes the timeout value  209  provided as part of the queued metadata  206 . The timeout value  209  generally synchronizes timing in the monitoring of timeout events by the object analysis system  240   1  and the sensor  110   1 . For this type of timeout event, the timeout monitoring logic  530  monitors the metadata queuing time for the metadata  206  associated with the object  204  to determination where this duration exceeds the timeout value  209  (e.g., the duration that the metadata  206  resides in the queue  175   1  exceeds the timeout value  209 ). For the second type of timeout event, the timeout monitoring logic  530  monitors the metadata queuing time for the object  204 , where the duration exceeds a prescribed threshold, the timeout monitoring logic  530  may initiate actions that cause the metadata  206  to be made available to other object analysis systems. The timeout monitoring logic  530  is communicatively coupled to the distributed data store  170   1  and the sensor notification logic  520  to identify whether metadata  206  experienced a timeout event. 
     Referring back to  FIG. 2 , each object analysis system  240   1 - 240   4  of the computing nodes  160   1 - 160   4  is responsible for retrieval of metadata that denotes a suspicious object awaiting an in-depth malware analysis to be conducted thereon. Furthermore, upon retrieval of the suspicious object, the object analysis system  240   1 , . . . , or  240   4  is responsible for conducting the malware analysis on the suspicious object. A logical representation of an object analysis system, such as object analysis system  240   1  for example, is shown in  FIG. 5B . 
     Referring to  FIG. 5B , a block diagram of an exemplary embodiment of logic implemented within the object analysis system  240   1  that is operating as part of the computing node  160   1  of  FIG. 4  is shown. According to one embodiment of the disclosure, the object analysis system  240   1  features logic, namely management logic  550 , object processing logic  570  and reporting logic  590 , that relies on processing functionality provided by the processor(s)  400  and connectivity provided by the network interface(s)  410  of the computing node  160   1 . Of course, it is contemplated that the object analysis system  240   1  may be configured to utilize a different processor, such as one or more different processor cores for example, than the analysis coordination system  220   1  operating within the same computing node  160   1 . As shown, the management logic  550  includes capacity determination logic  560 , queue access logic  562 , and content retrieval logic  564 . The object processing logic  570  includes control logic  580  that orchestrates operations conducted by the static analysis logic subsystem  582 , behavior analysis logic subsystem  584 , emulation analysis logic subsystem  586 , and correlation/classification logic  588 . 
     Herein, the capacity determination logic  560  is responsible for determining whether the computing node  160   1  featuring the object analysis system  240   1  has sufficient processing capacity to handle another in-depth malware analysis of a suspicious object. This may involve a checking of current processor workload, the number of virtual machines available for behavioral analysis of the suspicious object, or the like. If no sufficient resources, the capacity determination logic  560  refrains from notifying the queue access logic  562  to access metadata within the distributed queue  175 . If so, the capacity determination logic  560  notifies the queue access logic  562  to commence selection of metadata from the distributed queue  175  of  FIG. 2 . The selection may be based on a First-In-First-Out (FIFO) queue selection scheme where the oldest metadata awaiting processing by an analysis system is selected. Of course, it is contemplated that the selection scheme may be arranged in accordance with factors in addition to or other than capacity such as a level of suspiciousness of the object, anticipated object type, type of communications being monitored (e.g., email, network traffic, etc.), service levels (QoS) associated with the sensor or analysis coordination system as identified by the metadata, sensor priority where certain sensors may be located to protect certain highly sensitive resources within the enterprise network, user-specified priority based on selected object characteristics, geographic location of the computing node  160   1  in relation to the sensor that captured the metadata (in the same region, state, country, etc.) as may be required by privacy laws or service level agreements, or the like. 
     One feature of the asynchronous load balancing architecture provides an implicit synchronization among the computing nodes  160   1 - 160   P  across the cluster  150   1 , notably synchronization of the object analysis systems  240   1 - 240   P  without the need for synchronized load balancers. In particular, once an object analysis system  240   1  removes a metadata entry from queue  175   1  of the distributed queue  175  is reflected in the other queues  175   2 - 175   P  (when configured to store the same information). Hence, other object analysis systems  240   2 - 240   P  of the cluster  150   1  will become aware of the removal. According to one embodiment, the “removed” entry may be set to a state that precludes other object analysis systems from accessing metadata within the “removed” entry so that the next available entry of the distributed queue  175  in accordance with the selection scheme is accessed for processing. It is noted that, where processing of the object associated with the metadata does not complete within a prescribed period of time (e.g., the object analysis system fails during processing, etc.), it is contemplated that the “removed” entry may be returned to a state that allows another object analysis systems  240   2 - 240   P  to access that metadata with that restored entry. 
     Also, queue access logic  562  may include timeout monitor logic  563  that determines whether the metadata removed from the distributed queue  175  has experienced a timeout. If so, the timeout monitor logic  563  provides the UUID and sensor ID associated with the metadata to the reporting logic  590  via communication path  568  to bypass in-depth malware analysis of the suspicious object by the object processing logic  570 . In response, the reporting logic  590  is configured to provide information  591  associated with the timeout event (hereinafter “timeout event information  591 ”) to the distributed data store  170  and/or the notification logic  380  of the sensor  110   1  of  FIG. 2  when the object analysis system  240   1  is operating in active mode. 
     Upon receipt of the selected metadata, the content retrieval logic  564  commences retrieval of the suspicious object corresponding to the metadata. This retrieval may be accomplished by obtaining the sensor ID  207  that indicates what sensor is responsible for the submission of the retrieved metadata and storage of the object, along with the UUID provided by the metadata for identifying the object corresponding to the metadata. A request message  565  is sent to the sensor including the sensor identifier  207  and UUID  211  as parameters. A response message  566  may be returned from the sensor, where the response message  566  includes a link to the suspicious object (from which the suspicious object may be accessed), such as IP addresses, URLs, domain names, or the suspicious object itself (i.e., object  204 ). Although this illustrative embodiment describes the object analysis system  240   1  acquiring the suspicious object  204  directly from the sensor  110   1 , it is contemplated that all communications with the sensor  110   1  may be coordinated through the analysis coordination system (e.g., system  220   1 ) of the broker computing node in communication with sensor  110   1    
     Thereafter, the returned information (link to object or object  204 ) may be temporarily stored in a data store (not shown) awaiting processing by one or more of the static analysis logic subsystem  582 , the behavior analysis logic subsystem  584 , and/or the emulation analysis logic subsystem  586 . The control logic  580  controls the processing of the suspicious object  204  as described below for  FIG. 7 . The results of the malware analysis being conducted through the processing of the object by one or more of the static analysis logic subsystem  582 , the behavior analysis logic subsystem  584 , and/or the emulation analysis logic subsystem  586  are provided to the correlation/classification logic  588 . The correlation/classification logic  588  receives the results and determines whether the results denote that the likelihood of the suspicious object  204  being associated with malware exceeds a second prescribed threshold. If so, the suspicious object  204  is determined to be malicious. Otherwise, the suspicious object  204  is determined to be non-malicious. 
     The analytic results from the correlation/classification logic  588  along with certain portions of the metadata associated with the object (e.g., UUID  211 ) are provided to the reporting logic  590 . The reporting logic  590  may be responsible for generating alerts directed to the client administrators or management system as shown in  FIG. 1 . Additionally, or in the alternative, the reporting logic  590  may be responsible for providing at least a portion of the analytic results  595  to the distributed data store  170  for storage in accordance with the UUID associated with the analyzed, suspicious object. The sensor  110   1  may gain access the stored analytic results  595  and provide the alerts to the network administrator  190  as illustrated in  FIG. 1  or may forward the analytic results  595  to the management system  192  that may issue the alerts as well as distribute threat signatures generated by (or based on data supplied from) the object processing logic  570 . 
     Referring to  FIG. 6 , a flow diagram of operations conducted by an exemplary embodiment of logic implemented within the sensor  110   1  and the computing node  160   1  is shown. Herein, the processing engine  600  of the sensor  110   1  is configured to receive the information  200 , including the metadata  202  and the object  204 , directly from the network or via a network tap. Although not shown, the information  200  may be temporarily stored prior to processing. The processing engine  600  includes the packet analysis logic  355 , metadata extraction logic  360  and the timestamp generator logic  365  of  FIG. 3 . 
     After receipt of the information  200 , the processing engine  600  (e.g., logic  355 - 365  of  FIG. 3 ) conducts an analysis of at least a portion of the information  200 , such as the object  204  for example, to determine whether the object  204  is suspicious. If so, the processing engine  600  (metadata extraction logic  360  of  FIG. 3 ) extracts the metadata  202  from the received information  200  and may assigns UUID  211  to the metadata  202 . Furthermore, the processing engine  600  may include logic, such as a feature of timestamp generation logic  365  or a separate timeout period computation logic (not shown), which determines a timeout period allocated to conduct a malware analysis on the object  204  (e.g., seconds, minutes or hours). Some of the metadata  202  along with additional information (e.g., sensor ID, etc.), which forms part of the (aggregated) metadata  206 , may be stored in the metadata data store  390  while the suspicious object  204  may be stored in the content data store  395 . The metadata extraction logic  360  relates the UUID  211  with the suspicious object  204 . 
     Additionally, logic within the processing engine  600  (e.g., timestamp generator logic  365  of  FIG. 3 ) is configured to generate a timestamp with receipt of the information  200 . For instance, according to one embodiment of the disclosure, logic within the processing engine  600  (e.g., timestamp generator logic  365 ) may generate a timestamp upon determining that the object  204  is suspicious. Of course, the point of time when the timestamp is generated may vary anywhere between initial detection of the information  200  by the sensor  110   1  and the fetching of the metadata  202  by the MDS monitoring logic  375 . The occurrence of a timeout event is based on a period of time (timeout period) that has elapsed and no information (received or fetched) identifies that a malware analysis for a particular object has occurred, where the duration of the timeout period may be fixed or may vary depending on the type of content under analysis (e.g., object type). For example, the timeout period may be fixed for certain object types or all object types. Alternatively, the timeout period may be dynamic that provides flexibility for increasing or decreasing the timeout period of time based on findings or service subscription levels or customer needs. It is contemplated that the timeout period may be initially stored as part of the metadata associated with object  204 , while the timeout value  209  (remaining amount of timeout period for analysis of the object  204 ) may be provided to the cluster. 
     The MDS monitoring logic  375  may be configured to poll the metadata data store  390  for newly stored metadata (e.g., metadata  206 ). In response to detecting storage of the metadata  206  in the metadata data store  390 , the MDS monitoring logic  375  fetches at least a portion of the metadata  206  for forwarding to the analysis coordination system  220   1  of the computing node  160   1  and computes the timeout value  209  based on the timeout period. This portion of the metadata  206  may include, but is not limited or restricted to the following: (i) the sensor ID  207  for sensor  110   1 , (ii) the timestamp  208  that identifies a start time for the analysis of the suspicious object  204 , (iii) the assigned timeout value  209  (e.g., a time remaining from a time assigned by the processing engine that is based, at least in part, on the object type), (iv) representative content  210  of the suspicious object  204  (e.g., hash value, checksum, etc.), (v) UUID  211  of the suspicious object, and/or (vi) the operation mode identifier  212 . Thereafter, the MDS monitoring logic  375  generates a request message  376 , including some or all of the metadata  206 , to the analysis coordination system  220   1  that is assigned to service the sensor  110   1 . 
     The request detector/ID generator logic  500  is configured to receive the request message  376  from the MDS monitoring logic  375  and provide the metadata  206  to the pre-analysis (filtering) logic  510 . It is contemplated that, in response to providing the request message  376  to the request detector/ID generator logic  500 , the request detector/ID generator logic  500  may additionally assign a UUID associated with at least a portion of the metadata  206  and return the UUID to the MDS monitoring logic  375 . Thereafter, the MDS monitoring logic  375  would relate the UUID to the metadata  206 , where such metadata and its relationship are stored in the metadata data store  390 . 
     As shown, the request detector/ID generator logic  500  of the analysis coordination system  220   1  provides the metadata  206  to the pre-analysis (filtering) logic  510 . Herein, the pre-analysis (filtering) logic  510  determines, from content within the metadata  206 , whether the suspicious object  204  corresponds to any previously analyzed suspicious object within the cluster  150   1  or perhaps within other clusters  150   2 - 150   N  where the distributed data store  170   1  is updated based on stored content in other computing nodes  160   2 - 160   P  or computing nodes in other clusters  150   2 - 150   N . This determination involves a comparison of representative content  210  (e.g., checksum, hash value, etc.) UUID  211  (or original object name) of the suspicious object  204 , which is part of the metadata  206 , against representative content of previously analyzed suspicious objects stored in the distributed data store  170 . 
     In response to determining that the representative content  210  for the suspicious object  204  compares to representative content of a previously analyzed object, the pre-analysis (filtering) unit  510  signals the sensor notification logic  520  to transmit a message to the notification logic  380  within the sensor  110   1  that signifies that the suspicious object  204  has already been processed. The message may include the UUID  211  and sensor ID  207  associated with the metadata  206  being processed by the pre-analysis (filtering) logic  510  and the UUID associated with the previously analyzed object. Thereafter, the results of the analysis may be obtained from the distributed data store  170  utilizing the UUID associated with the previously analyzed object. 
     Responsible for handling communications with the sensor notification logic  520  and upon receipt of communications from the sensor notification logic, the notification logic  380  uses the UUID  211  of the suspicious object  204  to access the metadata data store  390  to indicate that the suspicious object  204  has been processed and notify the event (timeout) monitoring logic  370 , through modification of an entry associated with the metadata  206  corresponding to object  204  in metadata data store  390  that analysis of the suspicious object  204  has been completed. The result aggregation logic  385  may be configured to periodically or aperiodically (e.g., in response to a timeout event) send a request message to retrieval logic  525  to access the distributed data store  170  for results associated with the suspicious object  204  corresponding to the UUID  211 . 
     However, in response to determining that the representative content  210  of the suspicious object  204  under analysis fails to compare to any representative content within the distributed data store  170 , the pre-analysis (filtering) logic  510  creates a storage entry associated with the suspicious object  204 , including the UUID  211  along with the representative content  210  and the sensor ID  207  that are provided as part of the metadata  206  into the distributed data store  170 . The results of the analysis are subsequently uploaded into this storage entry after completion of the malware analysis of the object. 
     In the event that the timeout monitoring logic  370  detects a timeout event, which signifies that the suspicious object  204  has not been analyzed by an analysis system before a timeout period has elapsed (e.g., the result aggregation logic  385  has not been able to retrieve analytic results  595  associated with the suspicious object  204  from the distributed data store  170   1  when broker computing node  160   1  is operating in passive mode), the timeout monitoring logic  370  notifies the processing engine  600  of the timeout event. 
     Additionally, the notification logic  380  may be adapted to signify a timeout event (or failure to analyze the suspicious object  204  associated with provided metadata  206  within a prescribed period of time that may be determined based on the timeout period, the timestamp  208  and/or the current clock value) in response to receipt of timeout event information  591  when broker computing node  160   1  is operating in active mode or receipt of information  532  that identifies metadata associated with suspicious object  204  has not been timely processed. This information (or portion thereof)  534  may also be provided for storage with the distributed data store  170  (via distributed data store  170   1 ), which is accessible by other computing nodes  160   2 - 160   P . 
     Herein, the processing engine  600  may record information associated with the timeout event into the log  398 , which maintains analytic data associated with the sensor operations (e.g., number of timeout events, number of objects offered for analysis by the sensor  110   1 , etc.). Alternatively, the processing engine  600  may resubmit the suspicious object  204 , which may be accomplished, for example, by toggling a flag associated with a storage entry for the metadata  206  that causes the metadata  206  to appear as being newly added to the metadata data store  390 . The MDS monitoring logic  375  would commence fetching a portion of the metadata  206 , as described above. 
     Referring to  FIG. 7 , a flow diagram of operations conducted by an exemplary embodiment of logic implemented within the analysis coordination system  220   1  of  FIG. 5A  and the object analysis system  240   1  of  FIG. 5B  is shown. As described in  FIG. 6 , in response to the pre-analysis (filtering) logic  510  determining that the malware detection system  100  has not processed any objects identical or substantially related to the suspicious object  204 , the pre-analysis (filtering) logic  510  creates a storage entry associated with the suspicious object  204 , including the UUID  211  along with the representative content  210 , the sensor ID  207  and the operation mode identifier  212  that are provided as part of the metadata  206 , into the distributed data store  170 . The portions of the metadata  206  are subsequently uploaded to the distributed queue  175 . 
     Within the object analysis system  240   1 , the capacity determination logic  560  determines whether the computing node  160   1 , which features the object analysis system  240   1 , has sufficient processing capacity to handle an in-depth malware analysis of a suspicious object associated with the metadata  206 , is provisioned with guest images necessary for conducting a particular malware analysis on the object  204  associated with the metadata  206 , is configured for handling an object type corresponding to the object  204 , or the like. This may involve an analysis of the operating state of the computing node  160   1 , such as determining whether the current processing capacity of the processor  400  of  FIG. 4  falls below a load threshold (e.g., 90%), the number of virtual machines available for behavioral analysis of the suspicious object  204  is greater than a selected threshold (e.g., virtual machines), or the like. This logic provides load balancing capabilities without requiring synchronization of the computing nodes 
     If the operating state of the computing node  160   1  would support performance of a malware analysis of a suspicious object, the capacity determination logic  560  notifies the queue access logic  562  to commence selection of metadata from the distributed queue  175  of  FIG. 2 . The selection may be based on a First-In-First-Out (FIFO) queue selection scheme where the oldest metadata awaiting processing by any analysis system is selected. Of course, it is contemplated that the selection may be arranged in accordance with another scheme, such as a level of suspiciousness of the object, anticipated object type, sensor priority where certain sensors may be located to protect certain highly sensitive resources within the enterprise network, or the like. 
     It is contemplated that the queue access logic  562  may include timeout monitor logic  563  that determines whether the portion of the metadata  206  removed from the distributed queue  175  has experienced a timeout. If so, the timeout monitor logic  563  provides the UUID and sensor ID associated with the metadata  206  to the reporting logic  590  via the communication path  568 . In response, the reporting logic  590  is configured to provide the timeout event information  591  to the distributed data store  170  and/or the notification logic  380  of the sensor  110   1  of  FIG. 2  when the object analysis system  240   1  is operating in active mode. When operating in passive mode, as identified by the operation mode identifier  212  within the metadata  206 , the analytic results and any detected timeout events determined by timeout monitor logic  563  are made available to a requesting network device. 
     Upon receipt of the metadata  206 , the content retrieval logic  564  commences retrieval of the suspicious object  204  that corresponds to the metadata. First, the content retrieval logic  564  obtains the sensor ID  207  that identifies sensor  110   1  submitted the metadata  206  and is responsible for storage of the suspicious object  204 . Second, besides the sensor ID  207 , the content retrieval logic  564  further obtains the UUID  211  accompanying the metadata  206  for use in identifying the suspicious object  204 . The content retrieval logic  564  sends the request message  565  including the sensor ID  207  and the UUID  211  as parameters to logic  396  that manages accesses to the content data store  395  (sometimes referred to as “data store management logic”) and awaits the response message  566  that includes a link to the object (from which the object may be accessed) or the suspicious object itself (i.e., suspicious object  204 ). Although not shown, it is contemplated that an object stored in the content data store  395  is deleted in response to a timeout event occurring for that object, as detected by the timeout monitoring logic  370 . 
     Referring back to  FIG. 7 , the returned information (link to object or object) may be temporarily stored in a data store  700  awaiting processing by the object processing logic  570 , which includes one or more of the static analysis logic subsystem  582 , the behavior analysis logic subsystem  584 , and/or the emulation analysis logic subsystem  586 . The control logic  580  controls the processing of the suspicious object  204 . 
     More specifically, the object processing logic  570  includes the static analysis logic subsystem  582 , the behavior analysis logic subsystem  584 , and/or the emulation analysis logic subsystem  586  as well as the correlation/classification logic  588  and the control logic  580 . Although the analysis logic  582 ,  584  and  586  disposed within the object analysis system  240   1  is shown in a parallel topology, it is contemplated that the analysis logic  582 ,  584  and  586  may be communicatively coupled in a serial configuration or a daisy-chain configuration. It should be appreciated that the static analysis logic subsystem  582 , the behavior analysis logic subsystem  584 , the emulation analysis logic subsystem  586 , the correlation/classification logic  588 , and the reporting logic  590  may each be separate and distinct components, but any combination of such logic may also be implemented in a single memory block and/or core. 
     According to one embodiment, it is contemplated that the metadata  206  that may be used, at least in part by a virtual machine manager (VMM)  710 , for provisioning one or more virtual machines  720  in the behavior analysis logic subsystem  584 . The one or more virtual machines (VMs)  720  may conduct run-time processing of at least some of the information associated with the suspicious object  204 . It is contemplated that the metadata  206  may include data directed to the object type (e.g., PDF file, word processing document, HTML (web page) file, etc.), the type of operating system at the source that provided the object  160 , web browser type, or the like. 
     Additionally, or in an alternative, the metadata  206  may further include information that may be utilized by the correlation/classification logic  588  for classifying the suspicious object  204 . The metadata  206  may include information associated with the delivery mechanism for the suspicious object  204  which, depending on the object type, may include information extracted from a header of a packet (e.g., source IP address, destination IP address, etc.) or from the body or header of the email message (e.g., sender&#39;s email address, recipient&#39;s email address, subject line, etc.). Hence, although not shown in detail, the metadata  206  may operate as another analysis type in addition to the static analysis (characteristics), dynamic analysis (behaviors), and/or emulation (e.g., emulation results). 
     Referring still to  FIG. 7 , the static analysis logic subsystem  582  is configured to inspect information associated with the suspicious object  204  using logic models  730  for anomalies in characteristics such as formatting anomalies for example. In some embodiments, the static analysis logic subsystem  582  may also be configured to analyze the suspicious object  204  for certain characteristics, which may include the object&#39;s name, type, size, path, or protocols. Additionally, or in the alternative, the static analysis logic subsystem  582  may analyze the suspicious object  204  by performing one or more checks, including one or more signature checks, which may involve a comparison between (i) content of the suspicious object  204  and (ii) one or more pre-stored signatures associated with known malware. In one embodiment, pre-stored signatures may be stored on the distributed data store  170 . Checks may also include an analysis to detect exploitation techniques, such as any malicious obfuscation, using for example, probabilistic, heuristic, and/or machine-learning algorithms. 
     Additionally, the static analysis logic subsystem  582  may feature a plurality of rules that may be stored on the data store  700 , for example, wherein the rules control the analysis conducted on the suspicious object  204 . The rules may be based, at least in part, on machine learning; pattern matching; heuristic, probabilistic, or determinative analysis results; experiential knowledge; analyzed deviations in messaging practices set forth in applicable communication protocols (e.g., HTTP, HTTPS, TCP, etc.); analyzed compliance with certain message formats established for the protocol (e.g., out-of-order commands); and/or analyzed header or payload parameters to determine compliance. It is envisioned that the rules may be updated from an external source, such as via a remote source (e.g., threat intelligence network), in a periodic or aperiodic manner. 
     It is envisioned that information associated with the suspicious object  204  may be further analyzed using the behavior (dynamic) analysis logic subsystem  584 . Herein, the behavior analysis logic subsystem  584  features the VMM  710  and one or more virtual machines (VMs)  720 , namely VM 1    725   1 -VM R    725   R  (R≥1), and monitoring logic  730 . One or more of the VMs  725   1 - 725   R  are configured to process the suspicious object  204 , and the behaviors of the suspicious object  204  and/or VM(s)  725   1 - 725   R  may include anomalous behaviors. In general terms, each of the VMs  720  includes at least one run-time environment, which features a selected operating system and one or more applications to process the suspicious object  204 , which is expected for the type of suspicious object  204  under analysis or based on the targeted destination for the suspicious object  204 . For instance, where the suspicious object  204  is a URL, the run-time environment may include a specific OS type along with one or more web browser applications. Herein, the control logic  580  or logic within the dynamic analysis logic subsystem  584  may be adapted to provision one or more VMs  725   1 - 725   R  (e.g., VM 1 -VM R ) using information within the metadata  206  and/or information from the static analysis logic subsystem  582 . 
     Herein, it is contemplated that the VMs  725   1 - 725   R  may be provisioned with the same or different guest image bundles, where one VM  725   1  may be provisioned with one or more application instances supported by a first type of operating system (e.g., Windows®) while another VM  725   2  may be provisioned with a second type of operating system (e.g., MAC® OS X) supporting one or more other application instances. Furthermore, VMs  725   1 - 725   R  may be provisioned with customer specific guest image instances. According to one embodiment, the provisioning may be accomplished through a customer preference configuration option that is uploaded to the VMM  710  of the dynamic analysis logic subsystem  584 . The configuration option may be structured to identify the application version(s) and/or operating system(s) supported by the VMs  725   1 - 725   R . As an illustrative embodiment, each VM  725   1  . . . or  725   R  may be provisioned with one or more guest images directed to a single application version/operating system version (e.g., Microsoft® Word 2013 and Windows® 7 OS), multiple (two or more) application versions and a single OS version (e.g., Microsoft® Words® applications supported by Windows® 10 OS), multiple application versions and multiple OS versions (e.g., Microsoft® Words® applications supported by one or more Windows®-based OSes or MAC®-based OSes), or even single application and multiple OS deployment. 
     Additionally, the VMs  725   1 - 725   R  for each computing node may be provided for dedicated processing of a certain object type such as emails, network traffic including webpages/URLs, or the like. For this configuration, it is contemplated that queue  175   1  may be segmented in which one or more portions of the queue  175   1  are reserved for metadata associated with the certain object type while other object types are maintained in another portion of the queue  175   1 . In lieu of segmenting queue  175   1 , it is further contemplated that a different queue may be assigned for objects of the certain object type. 
     Furthermore, it is contemplated that the VMs within the object analysis systems (e.g., VMs  725   1 - 725   R  of object analysis system  240   1 ) may be provisioned so that different object analysis systems (computing nodes) support different types or levels of malware analysis. For instance, computing node  160   1  of  FIG. 2  may be configured to support malware analyses directed to email communications while computing node  160   2  may be configured to support malware analyses directed to webpage/URL network traffic. Also, the computing node  160   1  may be configured to support more in-depth malware analyses or more recent code releases than computing node  160   2 . As an example, computing node  160   1  of  FIG. 2  may be configured to support (i) longer or shorter malware analyses, (ii) more in-depth malware analyses or (iii) more recent code releases than computing node  160   2  of  FIG. 2 . 
     Monitoring logic  730  within the dynamic analysis logic subsystem  584  may observe one or more behaviors with respect to the suspicious object  204  that are attributable to the object  204  or attributable to the execution of the object  204  within one or more VMs  720 . These monitored behaviors may be used in a determination by the correlation/classification logic  588  as to whether the suspicious object  204  is associated with malware (i.e., the likelihood of the suspicious object  204  including malware and deemed malicious exceeds the second prescribed threshold). During processing of certain types of objects, such as the URL for example, the one or more VMs  720  (e.g., VM  725   1 ) may initiate a request message or successive request messages  567  to data store management logic  396  via the content retrieval logic  564  for additional information prompted through the processing of the URL. This information may involve web pages that would have been accessed during activation of the URL as well as objects within the web pages themselves. If the requested information is available, the data store management logic  396  returns the requested information via the content retrieval logic  564 , operating as a proxy, to the VM  725   1 . If the requested information is not available, however, the control logic  580  operating alone or in combination with other logic (e.g. the emulation analysis logic  586 ) may serve the request to enable the VM  725   1  to continue processing the URL (suspicious object  204 ). 
     As further shown in  FIG. 7 , the suspicious object  204  may be further analyzed using the emulation analysis logic subsystem  586 , which is configured so as to enable the analysis system  240   1  to behave like any another computer system (“guest” system). It is envisioned that the emulation analysis logic subsystem  586  may be configured so as to enable the analysis system  240   1  to simulate the operations of any of various software, applications, versions and the like, designed for the guest system. More specifically, the emulation analysis logic subsystem  586  may be configured so as to model hardware and software. 
     It should be understood that the static analysis logic subsystem  582 , the dynamic analysis logic subsystem  584 , the emulation analysis logic subsystem  586 , the correlation/classification logic  588 , and/or the reporting logic  590  may be implemented as one or more software modules executed by one or more processors as shown in  FIGS. 4 &amp; 5A-5B . 
     As further shown in  FIG. 7 , the correlation/classification logic  588  includes attribute correlation logic  740 , threat index generation logic  750  and object classification logic  760 . Herein, the attribute correlation logic  740  is configured to receive results  770   1 ,  770   2  and/or  770   3  from logic subsystems  582 ,  584  and/or  586 , respectively. The attribute correlation logic  740  attempts to correlate some or all of attributes (e.g., behaviors and/or characteristics) within the results  770   1 - 770   3  associated with the suspicious object  204  in accordance with a prescribed correlation rule set (not shown). The correlation rule set may be stored locally or in the data store  700  and may be updated. For this embodiment, the correlation determines what particular attributes and/or combination of attributes have been collectively detected by the static analysis logic subsystem  582  and dynamic analysis logic subsystem  584  in accordance with the attribute patterns set forth in the correlation rule set. 
     Herein, as a non-limiting illustration, the attributes and/or combinations of attributes constitute contextual information associated with the suspicious object  204 , which is provided to the threat index generation logic  750  to determine one or more threat indices. The operability of the threat index generation logic  750  is controlled by a threat index data set (not shown), which may be stored locally or within the data store  700 . The one or more threat indices are used by the object classification logic  760  to determine whether or not the suspicious object  204  is malicious, where such analysis is described in U.S. patent application Ser. No. 14/986,416 entitled “Malware Detection System With Context Analysis,” filed Dec. 31, 2015, the entire contents of which are incorporated by reference. 
     The analytic results  780  from the correlation/classification logic  588  along with certain portions of the metadata associated with the object (e.g., UUID) are provided to the reporting logic  590 . The reporting logic  590  may generate alerts directed to the client administrators or management system as shown in  FIG. 1 . Also, the reporting logic  590  may provide (i) at least a portion of the analytic results  595  to the distributed data store  170  for storage in accordance with the UUID associated with the analyzed, suspicious object, or (ii) at least the portion of the analytic results  595  to metadata data store  390  via the notification logic  380 . 
     B. Synchronous Load Balancing Architecture 
     As an alternative embodiment to the asynchronous load balancing architecture described above, a synchronous load balancing architecture may be utilized as depicted in  FIGS. 8-10  and described below. Each of these architectures includes one or more sensors and one or more clusters of computing nodes. As shown in  FIG. 8 , the cluster  150   1  comprises a plurality of computing nodes  160   1 - 160   P  (P≥1, P=4) where each computing node (e.g., computing node  160   1 ) comprises an analysis coordination system  800   1  and an object analysis system  820   1 . The analysis coordination system  800   1  may be activated or deactivated, where the computing node  160   1  operates as a “broker” computing node when the analysis coordination system  800   1  is activated or operates as an “analytic” computing node when the analysis coordination system  800   1  is deactivated. 
     Differing from the asynchronous load balancing architecture illustrated in  FIG. 2 , each object analysis system  820   1 - 820   4  within the cluster  150   1  is configured to provide load information  825  to each active analysis coordination system within the same cluster  150   1  (e.g., analysis coordination system  800   1  and  800   2 ). The active analysis coordination systems  800   1  and  800   2  are responsible for performing load balancing operations for the cluster  150   1 . The load information  825  may include information directed to the amount of computational work currently being performed by the object analysis system, where the amount of computational work may be represented by one or more measurable factors, including number of analyses of objects being currently performed, the number of virtual machines being utilized, processor load or processor utilization, or the like. Hence, the analysis coordination systems  800   1  and  800   2  are responsible for selecting the particular object analysis system  820   1 , . . . , or  820   4  based, at least in part, on workload. 
     Herein, the load balancing for each of the object analysis system  820   1 - 820   4  avoids bottlenecks or long latencies. However, it is contemplated that more complex considerations may be used besides load. For instance, where the loads are equivalent but the object analysis system  820   1  begins to operate in a degraded mode, one or more of the other object analysis systems  820   2 , . . . , or  820   4  will need to increase performance/ 
     As shown, for a communication session, sensors  110   1 - 110   M  are communicatively coupled directly to the first cluster  150   1  via a broker computing node, where each sensor  110   1 - 110   M  is assigned to a particular broker computing node during registration process and this assignment is assessed periodically or aperiodically in case an adjustment is needed due to workload. Herein, each sensor  110   1 , . . . , or  110   M  is configured to transmit a first message  830  (e.g., a Hypertext Transfer Protocol “HTTP” transmission) as a data submission to its assigned analysis coordination system  800   1  or  800   2 . As shown, sensor  110   1  transmits the data submission  830  to analysis coordination system  800   1 . 
     In the event that this transmission is associated with a new communication session, the analysis coordination system  800   1  conducts a load balance analysis and selects one of the object analysis systems  820   1 - 820   4  to handle malware analysis for an object  835  that has been detected by the sensor  110   1  as suspicious. An identifier  840  of the selected object analysis system, sometimes referred to as a “cookie”, is returned to the sensor  110   1  from the analysis coordination system  800   1 . 
     In response to receiving the cookie  840  and without terminating the communication session, the sensor  110   1  transmits a second message  850  to the selected object analysis system (e.g., object analysis system  820   3 ). The second message  850  includes the object  835  for analysis, metadata  836  associated with the object  835 , the identifier  840  of the selected object analysis system  820   3  as a targeted destination, and an identifier  860  of the sensor  110   1  as a source. The analysis coordination system  800   1  translates the identifier  840  to appropriate address information of the selected object analysis system  820   3  and redirects the second message  850  to the selected object analysis system  820   3  for conducting malware analysis on the object  835 . 
     Similar to the operations described in  FIG. 2 , prior to the communication exchange with the assigned analysis coordination system  800   1 , the sensor  110   1  is configured to receive incoming data that includes the object  835  and corresponding metadata  836 . Upon receipt of the incoming data, the sensor  110   1  separates the metadata  836  from the object  835  and conducts a preliminary analysis of the object  835  to determine whether the object  835  is suspicious (e.g., a first level of likelihood that the object includes malware). The preliminary analysis may include one or more checks being conducted on the object  835  and/or the metadata  836  (e.g., bit pattern comparisons, blacklist or whitelist analysis, etc.). 
     Upon failing to determine that the object  835  is suspicious, the sensor  1101  avoids transmission of the first message  830  that initiates an in-depth malware analysis of the object  835 . However, in response to the sensor  110   1  detecting that the object  835  is suspicious, the sensor  110   1  transmits the first message  830  to initiate the communication session and commence routing of the object  835  to a selected object analysis system. 
     Referring to  FIG. 9 , a block diagram of an exemplary embodiment of the logic implemented within a computing node  160   1  configured in accordance with the synchronous load balancing architecture is shown, where the computing node  160   1  is configured in accordance with the synchronous load balancing architecture of  FIG. 8 . Herein, the computing node  160   1  features the analysis coordination system  800   1  and the object analysis system  820   1 . The analysis coordination system  800   1  is communicatively coupled to object analysis systems  820   3  and  820   4  of computing nodes  160   3  and  160   4 , respectively. Herein, the communications with the object analysis system  820   2  are not shown for clarity purposes. 
     As shown, the analysis coordination system  800   1  features a proxy server  900  communicatively coupled to the load balancer  910 . The proxy server  900  is responsible for determining whether the data submission  830  from the sensor  110   1  includes a cookie, which denotes an object analysis system targeted to receive the data submission. The load balancer  910  is responsible for the handling of load balancing for the object analysis systems  820   1 - 820   4  within the cluster  150   1 . As shown, load balancer  910  receives load information  825  from load monitors  920   1 - 920   3  that are configured to monitor workload of the object analysis systems  820   1 - 820   3 , respectively. 
     Herein, in response to receipt of the first message  830  from the sensor  110   1 , the proxy server  900  determines whether the first message  830  includes a cookie  840  that identifies one of the object analysis systems within the cluster  150   1 . If no cookie is found, the proxy server  900  forwards the first message  830  to the load balancer  910 , which returns a message  930  with the assigned cookie  840  identifying the selected object analysis system (e.g., object analysis system  820   3 ) to the proxy server  900 . Thereafter, the proxy server  900  returns at least the cookie  840  from the message  930  to the server  110   1 , which causes the sensor  110   1  to transmit the second message  850 , including the object  835  for analysis, back to the proxy server  900 . 
     Upon receipt of the second message  850 , the proxy server  900  redirects the second message  850  to a web server  940 , which effectively provides an address (e.g., IP address) for the object analysis system  820   3  within the computing node  160   1 . Thereafter, the web server  940  may parse the second message  850  to extract the object  835  for processing and the metadata  836  for use in VM configuration of the object processing logic  570 , as described above. 
     Referring to  FIG. 10 , a block diagram illustrating an operational flow between exemplary embodiments of the sensor  110   1 , analysis coordination system  800   1 , and object analysis system  820   3  within the cluster  150   1  deploying a synchronous load balancing architecture is shown. Herein, in response to receipt of a message from the sensor  110   1 , such as web (API) client that controls the load balancing signaling with the sensor  110   1  (operation “ 1 ”), the proxy server  900  determines whether the message includes a cookie that identifies one of the object analysis systems within the cluster  150   1 . If no cookie is found, the proxy server  900  forwards the message to the load balancer  910  (operation “ 2 ”), which returns a message with an assigned cookie identifying the selected object analysis system (e.g., object analysis system  820   3 ) to the proxy server  900  (operation “ 3 ”). Thereafter, the proxy server  900  returns contents of the message to the server  110   1  (operation “ 4 ”). The receipt of the returned message causes the sensor  110   1  to transmit a second message, including the object for analysis along with its metadata, back to the proxy server  900  (operation “ 5 ”). 
     Upon receipt of the second message, the proxy server  900  redirects the second message to the web (API) server  940  (operation “ 6 ”), which parse the second message to extract the object  835  for processing and the metadata  836  for use in VM configuration of the object processing logic  570  (operation “ 7 ”). Within the object processing logic  570 , the object  835  undergoes static analysis, behavioral (dynamic) analysis and/or emulation analysis to produce attributes that are analyzed by correlation/classification logic to determine whether the object  835  is associated with malware. The results of the analysis by the object processing logic  570  may be returned to the proxy server  900  (operation “ 8 ”), and subsequently made available to the sensor  110   1  through a push or pull data delivery scheme (operation “ 9 ”). Although not shown, it is contemplated that object analysis system  820   3  includes content retrieval logic (e.g., content retrieval logic  564  of  FIG. 7 ) that operates to retrieval additional information requested by the VM during processing of the object  835 . 
     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.