Patent Publication Number: US-2022217164-A1

Title: Inline malware detection

Description:
CROSS REFERENCE TO OTHER APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 16/517,463 entitled INLINE MALWARE DETECTION filed Jul. 19, 2019 which is incorporated herein by reference for all purposes. 
    
    
     BACKGROUND OF THE INVENTION 
     Malware is a general term commonly used to refer to malicious software (e.g., including a variety of hostile, intrusive, and/or otherwise unwanted software). Malware can be in the form of code, scripts, active content, and/or other software. Example uses of malware include disrupting computer and/or network operations, stealing proprietary information (e.g., confidential information, such as identity, financial, and/or intellectual property related information), and/or gaining access to private/proprietary computer systems and/or computer networks. Unfortunately, as techniques are developed to help detect and mitigate malware, nefarious authors find ways to circumvent such efforts. Accordingly, there is an ongoing need for improvements to techniques for identifying and mitigating malware. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments of the invention are disclosed in the following detailed description and the accompanying drawings. 
         FIG. 1  illustrates an example of an environment in which malicious applications are detected and prevented from causing harm. 
         FIG. 2A  illustrates an embodiment of a data appliance. 
         FIG. 2B  is a functional diagram of logical components of an embodiment of a data appliance. 
         FIG. 3  illustrates an example of logical components that can be included in a system for analyzing samples. 
         FIG. 4  illustrates portions of an example embodiment of a threat engine. 
         FIG. 5  illustrates an example of a portion of a tree. 
         FIG. 6  illustrates an example of a process for performing inline malware detection on a data appliance. 
         FIG. 7A  illustrates an example hash table for a file. 
         FIG. 7B  illustrates an example threat signature for a sample. 
         FIG. 8A  illustrates an example of a process for performing feature extraction. 
         FIG. 8B  illustrates an example of a process for generating a model. 
     
    
    
     DETAILED DESCRIPTION 
     The invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor. In this specification, these implementations, or any other form that the invention may take, may be referred to as techniques. In general, the order of the steps of disclosed processes may be altered within the scope of the invention. Unless stated otherwise, a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task. As used herein, the term ‘processor’ refers to one or more devices, circuits, and/or processing cores configured to process data, such as computer program instructions. 
     A detailed description of one or more embodiments of the invention is provided below along with accompanying figures that illustrate the principles of the invention. The invention is described in connection with such embodiments, but the invention is not limited to any embodiment. The scope of the invention is limited only by the claims and the invention encompasses numerous alternatives, modifications and equivalents. Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. These details are provided for the purpose of example and the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured. 
     I. Overview 
     A firewall generally protects networks from unauthorized access while permitting authorized communications to pass through the firewall. A firewall is typically a device, a set of devices, or software executed on a device that provides a firewall function for network access. For example, a firewall can be integrated into operating systems of devices (e.g., computers, smart phones, or other types of network communication capable devices). A firewall can also be integrated into or executed as one or more software applications on various types of devices, such as computer servers, gateways, network/routing devices (e.g., network routers), and data appliances (e.g., security appliances or other types of special purpose devices), and in various implementations, certain operations can be implemented in special purpose hardware, such as an ASIC or FPGA. 
     Firewalls typically deny or permit network transmission based on a set of rules. These sets of rules are often referred to as policies (e.g., network policies or network security policies). For example, a firewall can filter inbound traffic by applying a set of rules or policies to prevent unwanted outside traffic from reaching protected devices. A firewall can also filter outbound traffic by applying a set of rules or policies (e.g., allow, block, monitor, notify or log, and/or other actions can be specified in firewall rules or firewall policies, which can be triggered based on various criteria, such as are described herein). A firewall can also filter local network (e.g., intranet) traffic by similarly applying a set of rules or policies. 
     Security devices (e.g., security appliances, security gateways, security services, and/or other security devices) can include various security functions (e.g., firewall, anti-malware, intrusion prevention/detection, Data Loss Prevention (DLP), and/or other security functions), networking functions (e.g., routing, Quality of Service (QoS), workload balancing of network related resources, and/or other networking functions), and/or other functions. For example, routing functions can be based on source information (e.g., IP address and port), destination information (e.g., IP address and port), and protocol information. 
     A basic packet filtering firewall filters network communication traffic by inspecting individual packets transmitted over a network (e.g., packet filtering firewalls or first generation firewalls, which are stateless packet filtering firewalls). Stateless packet filtering firewalls typically inspect the individual packets themselves and apply rules based on the inspected packets (e.g., using a combination of a packet&#39;s source and destination address information, protocol information, and a port number). 
     Application firewalls can also perform application layer filtering (e.g., application layer filtering firewalls or second generation firewalls, which work on the application level of the TCP/IP stack). Application layer filtering firewalls or application firewalls can generally identify certain applications and protocols (e.g., web browsing using HyperText Transfer Protocol (HTTP), a Domain Name System (DNS) request, a file transfer using File Transfer Protocol (FTP), and various other types of applications and other protocols, such as Telnet, DHCP, TCP, UDP, and TFTP (GSS)). For example, application firewalls can block unauthorized protocols that attempt to communicate over a standard port (e.g., an unauthorized/out of policy protocol attempting to sneak through by using a non-standard port for that protocol can generally be identified using application firewalls). 
     Stateful firewalls can also perform state-based packet inspection in which each packet is examined within the context of a series of packets associated with that network transmission&#39;s flow of packets. This firewall technique is generally referred to as a stateful packet inspection as it maintains records of all connections passing through the firewall and is able to determine whether a packet is the start of a new connection, a part of an existing connection, or is an invalid packet. For example, the state of a connection can itself be one of the criteria that triggers a rule within a policy. 
     Advanced or next generation firewalls can perform stateless and stateful packet filtering and application layer filtering as discussed above. Next generation firewalls can also perform additional firewall techniques. For example, certain newer firewalls sometimes referred to as advanced or next generation firewalls can also identify users and content (e.g., next generation firewalls). In particular, certain next generation firewalls are expanding the list of applications that these firewalls can automatically identify to thousands of applications. Examples of such next generation firewalls are commercially available from Palo Alto Networks, Inc. (e.g., Palo Alto Networks&#39; PA Series firewalls). For example, Palo Alto Networks&#39; next generation firewalls enable enterprises to identify and control applications, users, and content—not just ports, IP addresses, and packets—using various identification technologies, such as the following: APP-ID for accurate application identification, User-ID for user identification (e.g., by user or user group), and Content-ID for real-time content scanning (e.g., controlling web surfing and limiting data and file transfers). These identification technologies allow enterprises to securely enable application usage using business-relevant concepts, instead of following the traditional approach offered by traditional port-blocking firewalls. Also, special purpose hardware for next generation firewalls (implemented, for example, as dedicated appliances) generally provides higher performance levels for application inspection than software executed on general purpose hardware (e.g., such as security appliances provided by Palo Alto Networks, Inc., which use dedicated, function specific processing that is tightly integrated with a single-pass software engine to maximize network throughput while minimizing latency). 
     Advanced or next generation firewalls can also be implemented using virtualized firewalls. Examples of such next generation firewalls are commercially available from Palo Alto Networks, Inc. (e.g., Palo Alto Networks&#39; VM Series firewalls, which support various commercial virtualized environments, including, for example, VMware® ESXi™ and NSX™, Citrix® Netscaler SDX™, KVM/OpenStack (Centos/RHEL, Ubuntu®), and Amazon Web Services (AWS)). For example, virtualized firewalls can support similar or the exact same next-generation firewall and advanced threat prevention features available in physical form factor appliances, allowing enterprises to safely enable applications flowing into, and across their private, public, and hybrid cloud computing environments. Automation features such as VM monitoring, dynamic address groups, and a REST-based API allow enterprises to proactively monitor VM changes dynamically feeding that context into security policies, thereby eliminating the policy lag that may occur when VMs change. 
     II. Example Environment 
       FIG. 1  illustrates an example of an environment in which malicious applications (“malware”) are detected and prevented from causing harm. As will be described in more detail below, malware classifications (e.g., as made by security platform  122 ) can be variously shared and/or refined among various entities included in the environment shown in  FIG. 1 . And, using techniques described herein, devices, such as endpoint client devices  104 - 110  can be protected from such malware. 
     The term “application” is used throughout the Specification to collectively refer to programs, bundles of programs, manifests, packages, etc., irrespective of form/platform. An “application” (also referred to herein as a “sample”) can be a standalone file (e.g., a calculator application having the filename “calculator.apk” or “calculator.exe”) and can also be an independent component of another application (e.g., a mobile advertisement SDK or library embedded within the calculator app). 
     “Malware” as used herein refers to an application that engages in behaviors, whether clandestinely or not (and whether illegal or not), of which a user does not approve/would not approve if fully informed. Examples of malware include Trojans, viruses, rootkits, spyware, hacking tools, keyloggers, etc. One example of malware is a desktop application that collects and reports to a remote server the end user&#39;s location (but does not provide the user with location-based services, such as a mapping service). Another example of malware is a malicious Android Application Package .apk (APK) file that appears to an end user to be a free game, but stealthily sends SMS premium messages (e.g., costing $10 each), running up the end user&#39;s phone bill. Another example of malware is an Apple iOS flashlight application that stealthily collects the user&#39;s contacts and sends those contacts to a spammer. Other forms of malware can also be detected/thwarted using the techniques described herein (e.g., ransomware). Further, while n-grams/feature vectors/output accumulation variables are described herein as being generated for malicious applications, techniques described herein can also be used in various embodiments to generate profiles for other kinds of applications (e.g., adware profiles, goodware profiles, etc.). 
     Techniques described herein can be used in conjunction with a variety of platforms (e.g., desktops, mobile devices, gaming platforms, embedded systems, etc.) and/or a variety of types of applications (e.g., Android .apk files, iOS applications, Windows PE files, Adobe Acrobat PDF files, etc.). In the example environment shown in  FIG. 1 , client devices  104 - 108  are a laptop computer, a desktop computer, and a tablet (respectively) present in an enterprise network  140 . Client device  110  is a laptop computer present outside of enterprise network  140 . 
     Data appliance  102  is configured to enforce policies regarding communications between client devices, such as client devices  104  and  106 , and nodes outside of enterprise network  140  (e.g., reachable via external network  118 ). Examples of such policies include ones governing traffic shaping, quality of service, and routing of traffic. Other examples of policies include security policies such as ones requiring the scanning for threats in incoming (and/or outgoing) email attachments, website content, files exchanged through instant messaging programs, and/or other file transfers. In some embodiments, data appliance  102  is also configured to enforce policies with respect to traffic that stays within enterprise network  140 . 
     An embodiment of a data appliance is shown in  FIG. 2A . The example shown is a representation of physical components that are included in data appliance  102 , in various embodiments. Specifically, data appliance  102  includes a high performance multi-core Central Processing Unit (CPU)  202  and Random Access Memory (RAM)  204 . Data appliance  102  also includes a storage  210  (such as one or more hard disks or solid state storage units). In various embodiments, data appliance  102  stores (whether in RAM  204 , storage  210 , and/or other appropriate locations) information used in monitoring enterprise network  140  and implementing disclosed techniques. Examples of such information include application identifiers, content identifiers, user identifiers, requested URLs, IP address mappings, policy and other configuration information, signatures, hostname/URL categorization information, malware profiles, and machine learning models. Data appliance  102  can also include one or more optional hardware accelerators. For example, data appliance  102  can include a cryptographic engine  206  configured to perform encryption and decryption operations, and one or more Field Programmable Gate Arrays (FPGAs)  208  configured to perform matching, act as network processors, and/or perform other tasks. 
     Functionality described herein as being performed by data appliance  102  can be provided/implemented in a variety of ways. For example, data appliance  102  can be a dedicated device or set of devices. The functionality provided by data appliance  102  can also be integrated into or executed as software on a general purpose computer, a computer server, a gateway, and/or a network/routing device. In some embodiments, at least some services described as being provided by data appliance  102  are instead (or in addition) provided to a client device (e.g., client device  104  or client device  110 ) by software executing on the client device. 
     Whenever data appliance  102  is described as performing a task, a single component, a subset of components, or all components of data appliance  102  may cooperate to perform the task. Similarly, whenever a component of data appliance  102  is described as performing a task, a subcomponent may perform the task and/or the component may perform the task in conjunction with other components. In various embodiments, portions of data appliance  102  are provided by one or more third parties. Depending on factors such as the amount of computing resources available to data appliance  102 , various logical components and/or features of data appliance  102  may be omitted and the techniques described herein adapted accordingly. Similarly, additional logical components/features can be included in embodiments of data appliance  102  as applicable. One example of a component included in data appliance  102  in various embodiments is an application identification engine which is configured to identify an application (e.g., using various application signatures for identifying applications based on packet flow analysis). For example, the application identification engine can determine what type of traffic a session involves, such as Web Browsing—Social Networking; Web Browsing—News; SSH; and so on. 
       FIG. 2B  is a functional diagram of logical components of an embodiment of a data appliance. The example shown is a representation of logical components that can be included in data appliance  102  in various embodiments. Unless otherwise specified, various logical components of data appliance  102  are generally implementable in a variety of ways, including as a set of one or more scripts (e.g., written in Java, python, etc., as applicable). 
     As shown, data appliance  102  comprises a firewall, and includes a management plane  232  and a data plane  234 . The management plane is responsible for managing user interactions, such as by providing a user interface for configuring policies and viewing log data. The data plane is responsible for managing data, such as by performing packet processing and session handling. 
     Network processor  236  is configured to receive packets from client devices, such as client device  108 , and provide them to data plane  234  for processing. Whenever flow module  238  identifies packets as being part of a new session, it creates a new session flow. Subsequent packets will be identified as belonging to the session based on a flow lookup. If applicable, SSL decryption is applied by SSL decryption engine  240 . Otherwise, processing by SSL decryption engine  240  is omitted. Decryption engine  240  can help data appliance  102  inspect and control SSL/TLS and SSH encrypted traffic, and thus help to stop threats that might otherwise remain hidden in encrypted traffic. Decryption engine  240  can also help prevent sensitive content from leaving enterprise network  140 . Decryption can be controlled (e.g., enabled or disabled) selectively based on parameters such as: URL category, traffic source, traffic destination, user, user group, and port. In addition to decryption policies (e.g., that specify which sessions to decrypt), decryption profiles can be assigned to control various options for sessions controlled by the policy. For example, the use of specific cipher suites and encryption protocol versions can be required. 
     Application identification (APP-ID) engine  242  is configured to determine what type of traffic a session involves. As one example, application identification engine  242  can recognize a GET request in received data and conclude that the session requires an HTTP decoder. In some cases, e.g., a web browsing session, the identified application can change, and such changes will be noted by data appliance  102 . For example, a user may initially browse to a corporate Wiki (classified based on the URL visited as “Web Browsing—Productivity”) and then subsequently browse to a social networking site (classified based on the URL visited as “Web Browsing—Social Networking”). Different types of protocols have corresponding decoders. 
     Based on the determination made by application identification engine  242 , the packets are sent, by threat engine  244 , to an appropriate decoder configured to assemble packets (which may be received out of order) into the correct order, perform tokenization, and extract out information. Threat engine  244  also performs signature matching to determine what should happen to the packet. As needed, SSL encryption engine  246  can re-encrypt decrypted data. Packets are forwarded using a forward module  248  for transmission (e.g., to a destination). 
     As also shown in  FIG. 2B , policies  252  are received and stored in management plane  232 . Policies can include one or more rules, which can be specified using domain and/or host/server names, and rules can apply one or more signatures or other matching criteria or heuristics, such as for security policy enforcement for subscriber/IP flows based on various extracted parameters/information from monitored session traffic flows. An interface (I/F) communicator  250  is provided for management communications (e.g., via (REST) APIs, messages, or network protocol communications or other communication mechanisms). 
     III. Security Platform 
     Returning to  FIG. 1 , suppose a malicious individual (using system  120 ) has created malware  130 . The malicious individual hopes that a client device, such as client device  104 , will execute a copy of malware  130 , compromising the client device, and, e.g., causing the client device to become a bot in a botnet. The compromised client device can then be instructed to perform tasks (e.g., cryptocurrency mining, or participating in denial of service attacks) and to report information to an external entity, such as command and control (C&amp;C) server  150 , as well as to receive instructions from C&amp;C server  150 , as applicable. 
     Suppose data appliance  102  has intercepted an email sent (e.g., by system  120 ) to a user, “Alice,” who operates client device  104 . A copy of malware  130  has been attached by system  120  to the message. As an alternate, but similar scenario, data appliance  102  could intercept an attempted download by client device  104  of malware  130  (e.g., from a website). In either scenario, data appliance  102  determines whether a signature for the file (e.g., the email attachment or website download of malware  130 ) is present on data appliance  102 . A signature, if present, can indicate that a file is known to be safe (e.g., is whitelisted), and can also indicate that the file is known to be malicious (e.g., is blacklisted). 
     In various embodiments, data appliance  102  is configured to work in cooperation with security platform  122 . As one example, security platform  122  can provide to data appliance  102  a set of signatures of known-malicious files (e.g., as part of a subscription). If a signature for malware  130  is included in the set (e.g., an MD5 hash of malware  130 ), data appliance  102  can prevent the transmission of malware  130  to client device  104  accordingly (e.g., by detecting that an MD5 hash of the email attachment sent to client device  104  matches the MD5 hash of malware  130 ). Security platform  122  can also provide to data appliance  102  a list of known malicious domains and/or IP addresses, allowing data appliance  102  to block traffic between enterprise network  140  and C&amp;C server  150  (e.g., where C&amp;C server  150  is known to be malicious). The list of malicious domains (and/or IP addresses) can also help data appliance  102  determine when one of its nodes has been compromised. For example, if client device  104  attempts to contact C&amp;C server  150 , such attempt is a strong indicator that client  104  has been compromised by malware (and remedial actions should be taken accordingly, such as quarantining client device  104  from communicating with other nodes within enterprise network  140 ). As will be described in more detail below, security platform  122  can also provide other types of information to data appliance  102  (e.g., as part of a subscription) such as a set of machine learning models usable by data appliance  102  to perform inline analysis of files. 
     A variety of actions can be taken by data appliance  102  if no signature for an attachment is found, in various embodiments. As a first example, data appliance  102  can fail-safe, by blocking transmission of any attachments not whitelisted as benign (e.g., not matching signatures of known good files). A drawback of this approach is that there may be many legitimate attachments unnecessarily blocked as potential malware when they are in fact benign. As a second example, data appliance  102  can fail-danger, by allowing transmission of any attachments not blacklisted as malicious (e.g., not matching signatures of known bad files). A drawback of this approach is that newly created malware (previously unseen by platform  122 ) will not be prevented from causing harm. 
     As a third example, data appliance  102  can be configured to provide the file (e.g., malware  130 ) to security platform  122  for static/dynamic analysis, to determine whether it is malicious and/or to otherwise classify it. A variety of actions can be taken by data appliance  102  while analysis by security platform  122  of the attachment (for which a signature is not already present) is performed. As a first example, data appliance  102  can prevent the email (and attachment) from being delivered to Alice until a response is received from security platform  122 . Assuming platform  122  takes approximately 15 minutes to thoroughly analyze a sample, this means that the incoming message to Alice will be delayed by 15 minutes. Since, in this example, the attachment is malicious, such a delay will not impact Alice negatively. In an alternate example, suppose someone has sent Alice a time sensitive message with a benign attachment for which a signature is also not present. Delaying delivery of the message to Alice by 15 minutes will likely be viewed (e.g., by Alice) as unacceptable. As will be described in more detail below, an alternate approach is to perform at least some real-time analysis on the attachment on data appliance  102  (e.g., while awaiting a verdict from platform  122 ). If data appliance  102  can independently determine whether the attachment is malicious or benign, it can take an initial action (e.g., block or allow delivery to Alice), and can adjust/take additional actions once a verdict is received from security platform  122 , as applicable. 
     Security platform  122  stores copies of received samples in storage  142  and analysis is commenced (or scheduled, as applicable). One example of storage  142  is an Apache Hadoop Cluster (HDFS). Results of analysis (and additional information pertaining to the applications) are stored in database  146 . In the event an application is determined to be malicious, data appliances can be configured to automatically block the file download based on the analysis result. Further, a signature can be generated for the malware and distributed (e.g., to data appliances such as data appliances  102 ,  136 , and  148 ) to automatically block future file transfer requests to download the file determined to be malicious. 
     In various embodiments, security platform  122  comprises one or more dedicated commercially available hardware servers (e.g., having multi-core processor(s), 32G+ of RAM, gigabit network interface adaptor(s), and hard drive(s)) running typical server-class operating systems (e.g., Linux). Security platform  122  can be implemented across a scalable infrastructure comprising multiple such servers, solid state drives, and/or other applicable high-performance hardware. Security platform  122  can comprise several distributed components, including components provided by one or more third parties. For example, portions or all of security platform  122  can be implemented using the Amazon Elastic Compute Cloud (EC2) and/or Amazon Simple Storage Service (S3). Further, as with data appliance  102 , whenever security platform  122  is referred to as performing a task, such as storing data or processing data, it is to be understood that a sub-component or multiple sub-components of security platform  122  (whether individually or in cooperation with third party components) may cooperate to perform that task. As one example, security platform  122  can optionally perform static/dynamic analysis in cooperation with one or more virtual machine (VM) servers, such as VM server  124 . 
     An example of a virtual machine server is a physical machine comprising commercially available server-class hardware (e.g., a multi-core processor, 32+ Gigabytes of RAM, and one or more Gigabit network interface adapters) that runs commercially available virtualization software, such as VMware ESXi, Citrix XenServer, or Microsoft Hyper-V. In some embodiments, the virtual machine server is omitted. Further, a virtual machine server may be under the control of the same entity that administers security platform  122 , but may also be provided by a third party. As one example, the virtual machine server can rely on EC2, with the remainder portions of security platform  122  provided by dedicated hardware owned by and under the control of the operator of security platform  122 . VM server  124  is configured to provide one or more virtual machines  126 - 128  for emulating client devices. The virtual machines can execute a variety of operating systems and/or versions thereof. Observed behaviors resulting from executing applications in the virtual machines are logged and analyzed (e.g., for indications that the application is malicious). In some embodiments, log analysis is performed by the VM server (e.g., VM server  124 ). In other embodiments, analysis is performed at least in part by other components of security platform  122 , such as a coordinator  144 . 
     In various embodiments, security platform  122  makes available results of its analysis of samples via a list of signatures (and/or other identifiers) to data appliance  102  as part of a subscription. For example, security platform  122  can periodically send a content package that identifies malware apps (e.g., daily, hourly, or some other interval, and/or based on an event configured by one or more policies). An example content package includes a listing of identified malware apps, with information such as a package name, a hash value for uniquely identifying the app, and a malware name (and/or malware family name) for each identified malware app. The subscription can cover the analysis of just those files intercepted by data appliance  102  and sent to security platform  122  by data appliance  102 , and can also cover signatures of all malware known to security platform  122  (or subsets thereof, such as just mobile malware but not other forms of malware (e.g., PDF malware)). As will be described in more detail below, platform  122  can also make available other types of information, such as machine learning models that can help data appliance  102  detect malware (e.g., through techniques other than hash-based signature matching). 
     In various embodiments, security platform  122  is configured to provide security services to a variety of entities in addition to (or, as applicable, instead of) an operator of data appliance  102 . For example, other enterprises, having their own respective enterprise networks  114  and  116 , and their own respective data appliances  136  and  148 , can contract with the operator of security platform  122 . Other types of entities can also make use of the services of security platform  122 . For example, an Internet Service Provider (ISP) providing Internet service to client device  110  can contract with security platform  122  to analyze applications which client device  110  attempts to download. As another example, the owner of client device  110  can install software on client device  110  that communicates with security platform  122  (e.g., to receive content packages from security platform  122 , use the received content packages to check attachments in accordance with techniques described herein, and transmit applications to security platform  122  for analysis). 
     IV. Analyzing Samples Using Static/Dynamic Analysis 
       FIG. 3  illustrates an example of logical components that can be included in a system for analyzing samples. Analysis system  300  can be implemented using a single device. For example, the functionality of analysis system  300  can be implemented in a malware analysis module  112  incorporated into data appliance  102 . Analysis system  300  can also be implemented, collectively, across multiple distinct devices. For example, the functionality of analysis system  300  can be provided by security platform  122 . 
     In various embodiments, analysis system  300  makes use of lists, databases, or other collections of known safe content and/or known bad content (collectively shown in  FIG. 3  as collection  314 ). Collection  314  can be obtained in a variety of ways, including via a subscription service (e.g., provided by a third party) and/or as a result of other processing (e.g., performed by data appliance  102  and/or security platform  122 ). Examples of information included in collection  314  are: URLs, domain names, and/or IP addresses of known malicious servers; URLs, domain names, and/or IP addresses of known safe servers; URLs, domain names, and/or IP addresses of known command and control (C&amp;C) domains; signatures, hashes, and/or other identifiers of known malicious applications; signatures, hashes, and/or other identifiers of known safe applications; signatures, hashes, and/or other identifiers of known malicious files (e.g., Android exploit files); signatures, hashes, and/or other identifiers of known safe libraries; and signatures, hashes, and/or other identifiers of known malicious libraries. 
     A. Ingestion 
     In various embodiments, when a new sample is received for analysis (e.g., an existing signature associated with the sample is not present in analysis system  300 ), it is added to queue  302 . As shown in  FIG. 3 , application  130  is received by system  300  and added to queue  302 . 
     B. Static Analysis 
     Coordinator  304  monitors queue  302 , and as resources (e.g., a static analysis worker) become available, coordinator  304  fetches a sample from queue  302  for processing (e.g., fetches a copy of malware  130 ). In particular, coordinator  304  first provides the sample to static analysis engine  306  for static analysis. In some embodiments, one or more static analysis engines are included within analysis system  300 , where analysis system  300  is a single device. In other embodiments, static analysis is performed by a separate static analysis server that includes a plurality of workers (i.e., a plurality of instances of static analysis engine  306 ). 
     The static analysis engine obtains general information about the sample, and includes it (along with heuristic and other information, as applicable) in a static analysis report  308 . The report can be created by the static analysis engine, or by coordinator  304  (or by another appropriate component) which can be configured to receive the information from static analysis engine  306 . In some embodiments, the collected information is stored in a database record for the sample (e.g., in database  316 ), instead of or in addition to a separate static analysis report  308  being created (i.e., portions of the database record form the report  308 ). In some embodiments, the static analysis engine also forms a verdict with respect to the application (e.g., “safe,” “suspicious,” or “malicious”). As one example, the verdict can be “malicious” if even one “malicious” static feature is present in the application (e.g., the application includes a hard link to a known malicious domain). As another example, points can be assigned to each of the features (e.g., based on severity if found; based on how reliable the feature is for predicting malice; etc.) and a verdict can be assigned by static analysis engine  306  (or coordinator  304 , if applicable) based on the number of points associated with the static analysis results. 
     C. Dynamic Analysis 
     Once static analysis is completed, coordinator  304  locates an available dynamic analysis engine  310  to perform dynamic analysis on the application. As with static analysis engine  306 , analysis system  300  can include one or more dynamic analysis engines directly. In other embodiments, dynamic analysis is performed by a separate dynamic analysis server that includes a plurality of workers (i.e., a plurality of instances of dynamic analysis engine  310 ). 
     Each dynamic analysis worker manages a virtual machine instance. In some embodiments, results of static analysis (e.g., performed by static analysis engine  306 ), whether in report form ( 308 ) and/or as stored in database  316 , or otherwise stored, are provided as input to dynamic analysis engine  310 . For example, the static report information can be used to help select/customize the virtual machine instance used by dynamic analysis engine  310  (e.g., Microsoft Windows 7 SP 2 vs. Microsoft Windows 10 Enterprise, or iOS 11.0 vs. iOS 12.0). Where multiple virtual machine instances are executed at the same time, a single dynamic analysis engine can manage all of the instances, or multiple dynamic analysis engines can be used (e.g., with each managing its own virtual machine instance), as applicable. As will be explained in more detail below, during the dynamic portion of the analysis, actions taken by the application (including network activity) are analyzed. 
     In various embodiments, static analysis of a sample is omitted or is performed by a separate entity, as applicable. As one example, traditional static and/or dynamic analysis may be performed on files by a first entity. Once it is determined (e.g., by the first entity) that a given file is malicious, the file can be provided to a second entity (e.g., the operator of security platform  122 ) specifically for additional analysis with respect to the malware&#39;s use of network activity (e.g., by a dynamic analysis engine  310 ). 
     The environment used by analysis system  300  is instrumented/hooked such that behaviors observed while the application is executing are logged as they occur (e.g., using a customized kernel that supports hooking and logcat). Network traffic associated with the emulator is also captured (e.g., using pcap). The log/network data can be stored as a temporary file on analysis system  300 , and can also be stored more permanently (e.g., using HDFS or another appropriate storage technology or combinations of technology, such as MongoDB). The dynamic analysis engine (or another appropriate component) can compare the connections made by the sample to lists of domains, IP addresses, etc. ( 314 ) and determine whether the sample has communicated (or attempted to communicate) with malicious entities. 
     As with the static analysis engine, the dynamic analysis engine stores the results of its analysis in database  316  in the record associated with the application being tested (and/or includes the results in report  312  as applicable). In some embodiments, the dynamic analysis engine also forms a verdict with respect to the application (e.g., “safe,” “suspicious,” or “malicious”). As one example, the verdict can be “malicious” if even one “malicious” action is taken by the application (e.g., an attempt to contact a known malicious domain is made, or an attempt to exfiltrate sensitive information is observed). As another example, points can be assigned to actions taken (e.g., based on severity if found; based on how reliable the action is for predicting malice; etc.) and a verdict can be assigned by dynamic analysis engine  310  (or coordinator  304 , if applicable) based on the number of points associated with the dynamic analysis results. In some embodiments, a final verdict associated with the sample is made based on a combination of report  308  and report  312  (e.g., by coordinator  304 ). 
     V. Inline Malware Detection 
     Returning to the environment of  FIG. 1 , millions of new malware samples may be generated each month (e.g., by nefarious individuals such as the operator of system  120 , whether by making subtle changes to existing malware or by authoring new malware). Accordingly, there will exist many malware samples for which security platform  122  (at least initially) has no signature. Further, even where security platform  122  has generated signatures for newly created malware, resource constraints prevent data appliances, such as data appliance  102 , from having/using a list of all known signatures (e.g., as stored on platform  122 ) at any given time. 
     Sometimes malware, such as malware  130 , will successfully penetrate network  140 . One reason for this is where data appliance  102  operates on a “first-time allow” principle. Suppose that when data appliance  102  does not have a signature for a sample (e.g., sample  130 ) and submits it to security platform  122  for analysis, it takes security platform  122  approximately five minutes to return a verdict (e.g., “benign,” “malicious,” “unknown,” etc.). Instead of blocking communications between system  120  and client device  104  during that five minute time period, under a first-time allow principle, the communication is allowed. When a verdict is returned (e.g., five minutes later), data appliance  102  can use the verdict (e.g., “malicious”) to block subsequent transmissions of malware  130  to network  140 , can block communications between system  120  and network  140 , etc. In various embodiments, if a second copy of sample  130  arrives at data appliance  102  during the period data appliance  102  is awaiting a verdict from security platform  122 , the second copy (and any subsequent copies) of sample  130  will be held by system  120  pending a response from security platform  122 . 
     Unfortunately, during the five minutes that data appliance  102  awaits a verdict from security platform  122 , a user of client device  104  could have executed malware  130 , potentially compromising client device  104  or other nodes in network  140 . As mentioned above, in various embodiments, data appliance  102  includes a malware analysis module  112 . One task that malware analysis module  112  can perform is inline malware detection. In particular, and as will be described in more detail below, as a file (such as sample  130 ) passes through data appliance  102 , machine learning techniques can be applied to perform efficient analysis of the file on data appliance  102  (e.g., in parallel with other processing performed on the file by data appliance  102 ) and an initial maliciousness verdict can be determined by data appliance  102  (e.g., while awaiting a verdict from security platform  122 ). 
     Various difficulties can arise in implementing such analysis on a resource constrained appliance such as data appliance  102 . One critical resource on appliance  102  is session memory. A session is a network transfer of information, including the files that appliance  102  is to analyze in accordance with techniques described herein. A single appliance might have millions of concurrent sessions, and the memory available to persist during a given session is extremely limited. A first difficulty in performing inline analysis on a data appliance such as data appliance  102  is that, due to such memory constraints, data appliance  102  will typically not be able to process an entire file at once, but instead receive a sequence of packets which it needs to process, packet by packet. A machine learning approach used by data appliance  102  will accordingly need to accommodate packet streams in various embodiments. A second difficulty is that in some cases, data appliance  102  will be unable to determine where an end of a given file being processed occurs (e.g., the end of sample  130  in a stream). A machine learning approach used by data appliance  102  will accordingly need to be able to make a verdict about a given file potentially midstream (e.g., halfway through receipt/processing of sample  130  or otherwise prior to the actual file end) in various embodiments. 
     A. Machine Learning Models 
     As will be described in more detail below, in various embodiments, security platform  122  provides a set of machine learning models to data appliance  102  for data appliance  102  to use in conjunction with inline malware detection. The models incorporate features (e.g., n-grams or other features) determined by security platform  122  as corresponding to malicious files. Two example types of such models include linear classification models and non-linear classification models. Examples of linear classification models that can be used by data appliance  102  include logistic regression and linear support vector machines. An example of a non-linear classification model that can be used by data appliance  102  includes a gradient boosting tree (e.g., eXtreme Gradient Boosting (XGBoost)). The non-linear model is more accurate (and is better able to detect obfuscated/disguised malware), but the linear model uses considerably fewer resources on appliance  102  (and is more suitable for efficiently analyzing JavaScript or similar files). 
     As will be described in more detail below, which type of classification model is used for a given file being analyzed can be based on a filetype associated with the file (and determined, e.g., by a magic number). 
     1. Additional Detail on the Threat Engine 
     In various embodiments, data appliance  102  includes a threat engine  244 . The threat engine incorporates both protocol decoding and threat signature matching during a respective decoder stage and pattern match stage. Results of the two stages are merged by a detector stage. 
     When data appliance  102  receives a packet, data appliance  102  performs a session match to determine to which session the packet belongs (allowing data appliance  102  to support concurrent sessions). Each session has a session state which implicates a particular protocol decoder (e.g., a web browsing decoder, an FTP decoder, or an SMTP decoder). When a file is transmitted as part of a session, the applicable protocol decoder can make use of an appropriate file-specific decoder (e.g., a PE file decoder, a JavaScript decoder, or a PDF decoder). 
     Portions of an example embodiment of threat engine  244  are shown in  FIG. 4 . For a given session, decoder  402  walks the traffic bytestream, following the corresponding protocol and marking contexts. One example of a context is an end-of-file context (e.g., encountering &lt;/script&gt; while processing a JavaScript file). Decoder  402  can mark the end-of-file context in the packet, which can then be used to trigger execution of the appropriate model using the file&#39;s observed features. In some cases (e.g., FTP traffic), explicit protocol-level tags may not be present for decoder  402  to identify/mark context with. As will be described in more detail below, in various embodiments, decoder  402  can use other information (e.g., file size as reported in a header) to determine when feature extraction of a file should end (e.g., the overlay section begins) and execution using an appropriate model should be commenced. 
     Decoder  402  comprises two parts. The first part of decoder  402  is a virtual machine portion ( 404 ) which can be implemented as a state machine using a state machine language. The second part of decoder  402  is a set of tokens  406  (e.g., deterministic finite automaton (DFA) or regular expressions) for triggering state machine transitions and actions when matched in traffic. Threat engine  244  also includes a threat pattern matcher  408  (e.g., using regular expressions) that performs pattern matching (e.g., against threat patterns). As one example, threat pattern matcher  408  can be provided (e.g., by security platform  122 ) with a table of strings (whether exact strings or wildcard strings) to match against, and corresponding actions to take in the event a string match is found. Detector  410  processes outputs provided by decoder  402  and threat pattern matcher  408  to take various actions. 
     2. N-Grams 
     The data in a session can be broken into a sequence of n-grams—a series of byte strings. As an example, suppose a portion of hexadecimal data in a session is: “1023 ae42f6f28762aab.” The 2-grams in the sequence are all the pairs of adjacent characters, such as: “1023,” “23ae,” “ae42,” “42f6,” etc. In various embodiments, threat engine  244  is configured to analyze files using 8-grams. Other n-grams can also be used, such as 7-grams or 4-grams. In the example string above, “1023ae42f6f28762” is an 8-gram, “23ae42f6f28762aa” is an 8-gram, etc. The total number of different 8-grams possible in a byte sequence is 2 64  (18,446,744,073,709,551,616). Searching for all possible 8-grams in a byte sequence would readily exceed the resources of data appliance  102 . Instead, and as will be described in more detail below, a significantly reduced set of 8-grams is provided by security platform  122  to data appliance  102  for use by threat engine  244 . 
     As session packets corresponding to a file are received by threat engine  244 , threat pattern matcher  408  parses the packets for matches against strings in a table (e.g., by performing regular expression and/or exact string matches). A list of matches (e.g., with each instance of a match identified by a corresponding pattern ID) and at what offset each match occurred is generated. Actions on those matches are taken in the order of the offset (e.g., from lower to higher). For a given match (i.e., corresponding to a particular pattern ID), a set of one or more actions to take is specified (e.g., via an action table that maps actions to pattern IDs). 
     The set of 8-grams provided by security platform  122  can be added (e.g., as exact string matches) as additions to the table of matches that threat pattern matcher  408  is already performing (e.g., heuristic matches looking for specific indicia of malware, such as where a JavaScript file accesses a password store, or a PE file calls the Local Security Authority Subsystem Service (LSASS) API). One advantage of this approach is that, instead of performing multiple passes through the packet (e.g., first evaluating for heuristic matches, and then evaluating for 8-gram matches), the 8-grams can be searched for in parallel with other searches performed by threat pattern matcher  408 . 
     As will be described in more detail below, 8-gram matches are used by both linear and non-linear classification models in various embodiments. Example actions that can be specified for n-gram matches include incrementing a weighted counter (e.g., for a linear classifier) and saving the match in a feature vector (e.g., for a non-linear classifier). Which action is taken can be specified based on the filetype associated with the packet (which determines which type of model is used). 
     3. Selecting a Model 
     In some cases, a given filetype is specified within the file&#39;s header (e.g., as a magic number appearing in the first seven bytes of the file itself). In such a scenario, threat engine  244  can select an appropriate model corresponding to the specified file type (e.g., based on a table provided by security platform  122  that enumerates filetypes and corresponding models). In other cases, such as JavaScript, the magic number or other filetype identifier (if present in the header at all) may not be probative of which classification model should be used. As one example, JavaScript would have a filetype of “textfile.” To identify filetypes such as JavaScript, decoder  402  can be used to perform deterministic finite state automaton (DFA) pattern matching and apply heuristics (e.g., identifying &lt;script&gt; and other indicators that the file is JavaScript). The determined filetype and/or selected classification model are saved in the session state. The filetype associated with a session can be updated as the session progresses, as applicable. For example, in a stream of text, when a &lt;script&gt; tag is encountered, the JavaScript filetype can be assigned for the session. When a corresponding &lt;/script&gt; is encountered, the filetype can be changed (e.g., back to plaintext). 
     4. Linear Classification Models 
     One way to represent a linear model is by using the following linear equation: 
     
       
         
           
             
               
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     where P is the total number of features, x i  is the ith feature, β i  is the coefficient (weight) of feature x i , and C is a threshold constant. In this example, C is the threshold for a verdict of maliciousness, meaning that if a summation for a given file is less than C, the file is assigned a verdict of benign, and if the summation is equal to or greater than C, the file is assigned a verdict of malicious. 
     One approach to using a linear classification model by data appliance  102  is as follows. A single float (d) is used to track the score of the incoming file, and a hash table is used to store observed n-grams and corresponding coefficients (i.e., x i  and β i ). For each incoming packet, each of the n-gram features (e.g., as provided by security platform  122 ) is checked against. Whenever a match is found of a feature (x i ) in the hash table, the single float (β i ) which matches that feature in the hash table is added (e.g., to d). When the end of the file is reached, a comparison of the single float (d) against the threshold value (C) is performed to determine a verdict for the file. 
     For n-gram counting, feature x i  is equal to the number of times the ith n-gram is observed. Suppose the ith n-gram is observed for a particular file four times. 4*β i  can be rewritten as β i +β i +β i +β i . Instead of counting how many times (i.e., 4 times) the ith n-gram is observed and then multiplying by β i , an alternate approach is to add β i  each time the ith n-gram is observed. Furthermore, suppose that the jth n-gram is observed for the file three times. 3*β i  can similarly be written as β j +β j +β j , each time adding β j  instead of counting how many times β j  was observed and then adding at the end. 
     To find Σ(β i x i ), each of β i x i , β j x j , . . . (where . . . corresponds to all of the other features/weights) is added. This can be rewritten as β i +β i +β i +β j +β j +β j +β j + . . . . Because addition is cumulative, addition of the values can be added in any order (e.g., β i +β j +β i +β j +β i +β i +β j + etc.) and accumulated into a single float. Here, suppose that a float (d) starts at 0.0. Each time feature x i  is observed, β i  can be added to float d, and each time x j  is observed, β j  can be added to float d. This approach allows a 4 byte float to be used as the entire per session memory, and is contrasted with an approach in which the per session memory is proportional to the number of features, where the entire feature vector is stored in memory so that it can be multiplied by the weight vector. Using an example of 4 bytes*1,000 4 Kbyte features, 4K would be needed for storage (compared to the single 4 byte float), which is 1,000 times more expensive. 
     5. Non-Linear Classification Models 
     A variety of non-linear classification approaches can be used in conjunction with the techniques described herein. One example of a non-linear classification model is a gradient boosting tree. In this example, a feature vector is initialized to all-zero vectors. Unfortunately, for non-linear models (unlike linear models), the entire set of features for which presence is being detected (e.g., 1,000 features) is persisted for the entire duration of the session. While this is less efficient than in the linear approach, some efficiency can still be gained by down-sampling the features to be one byte (0-255) rather than a full 4 byte float (as might be used on a device that is not memory constrained). 
     As data appliance  102  scans through the file, each time a feature is observed, the value of that feature is increased by one in the feature vector. Once the end of file is reached (or termination of feature observation otherwise occurs), the constructed feature vector is fed into a gradient boosting tree model (e.g., received from security platform  122 ). As will be described in more detail below, the non-linear classification model can be built using both n-gram (e.g., 8-gram) and non n-gram features. One example of a non n-gram feature is the purported size of the file (which can be read as a value out of a packet containing the file&#39;s header). Any file data appearing after the purported end of the file (e.g., as based on the file size specified in the header) is referred to as an overlay. In addition to serving as a feature, the purported file length can be used as a proxy for how long the file is expected to be. The non-linear classifier can be run against the file&#39;s packet stream until the purported file length is reached, and then a verdict can be formed for the file irrespective of whether or not the end of file was in fact reached. That a given file includes an overlay is also an example of a feature that can be used as part of the non-linear classification model. In various embodiments, the overlay portion of the file is not analyzed, again—analysis can be performed prior to the actual end of file. In other embodiments, feature extraction occurs, and a maliciousness verdict is not formed until the actual end of file is reached. 
     In an example embodiment, the tree model comprises 5,000 binary trees. Every node on each tree contains a feature and a corresponding threshold. An example of a portion of a tree is depicted in  FIG. 5 . In the example shown in  FIG. 5 , if the value for a feature (e.g., feature F4) is less than its threshold (e.g., 30), the left branch is taken ( 502 ). If the value for the feature is equal to or greater than the threshold, then the right branch is taken ( 504 ). The tree is walked until a leaf node is reached (e.g., node  506 ), which has an associated value (e.g., 0.7). The values of each leaf reached (for each of the trees) are summed (rather than multiplied) to get a final score to calculate the verdict. If the score is below a threshold, the file can be considered benign, and if it is at or above the threshold, the file can be considered malicious. The lack of multiplication in obtaining the final score helps make use of the model more efficient in the resource constrained environment of data appliance  102 . 
     In various embodiments, the trees themselves are fixed on data appliance  102  (until an updated model is received) and can be stored in a shared memory that can be accessed by multiple sessions at the same time. The per session cost is the cost of storing the session&#39;s feature vector, which can be zeroed out once analysis of the session is completed. 
     6. Example Process 
       FIG. 6  illustrates an example of a process for performing inline malware detection on a data appliance. In various embodiments, process  600  is performed by data appliance  102 , and in particular by threat engine  244 . Threat engine  244  can be implemented using a script (or set of scripts) authored in an appropriate scripting language (e.g., Python). Process  600  can also be performed on an endpoint, such as client device  110  (e.g., by an endpoint protection application executing on client device  110 ). 
     Process  600  begins at  602  when an indication is received by appliance  102  that a file is being transmitted as part of a session. As one example of the processing performed at  602 , for a given session, an associated protocol decoder can call or otherwise make use of an appropriate file-specific decoder when the start of a file is detected by the protocol decoder. As explained above, the filetype is determined (e.g., by decoder  402 ) and associated with the session (e.g., so that subsequent filetype analysis need not be performed until the filetype changes or the file packets cease being transmitted). 
     At  604 , n-gram analysis is performed on a sequence of received packets. As explained above, the n-gram analysis can be performed inline with other analyses being performed on the session by appliance  102 . For example, while appliance  102  is performing analysis on a particular packet (e.g., to check for the presence of particular heuristics), it can also determine whether any 8-grams in the packet match 8-grams provided by security platform  122 . During the processing performed at  604 , when an n-gram match is found, the corresponding pattern ID is used to map the condition to an action based on filetype. The action either increments a weighted counter (e.g., where the filetype is associated with a linear classifier) or updates a feature vector to account for the match (e.g., where the filetype is associated with a non-linear classifier). 
     The n-gram analysis continues, packet by packet, until either an end-of-file condition or a checkpoint is reached. At that point ( 606 ), the appropriate model is used to determine a verdict for the file (i.e., comparing the final value obtained using the model against a maliciousness threshold). As mentioned above, the models incorporate n-gram features and can also incorporate other features (e.g., in the case of the non-linear classifier). 
     Finally, at  608 , an action is taken in response to the determination made at  606 . One example of a responsive action is terminating the session. Another example of a responsive action is allowing the session to continue, but preventing the file from being transmitted (and instead, being placed in a quarantine area). In various embodiments, appliance  102  is configured to share its verdicts (whether benign verdicts, malicious verdicts, or both) with security platform  122 . When security platform  122  completes its independent analysis of the file, it can use the verdict reported by appliance  102  for a variety of purposes, including assessing the performance of the model that formed the verdict. 
     An example threat signature for a sample is shown in  FIG. 7B . In particular, for a sample having a SHA-256 hash of “4d73f42438fb5a857915219cdfa9cbb4ce3f771ffed93a1b0528931e4813f8,” the first value in each pair corresponds to the feature, and the second value corresponds to a count. In the example shown in  FIG. 7B , the features comprising numbers (e.g., feature “3905”) correspond to n-gram features, and the features comprising “J” and a number (e.g., feature “J18”) correspond to non n-gram features. 
     In an example embodiment, security platform  122  is configured to target a specific false positive rate (e.g., 0.001) when generating models for use by appliances such as data appliance  102 . Accordingly, in some cases (e.g., one out of every one thousand files), data appliance  102  may incorrectly determine that a benign file is malicious when performing inline analysis using a model in accordance with techniques described herein. In such a scenario, if security platform  122  subsequently determines that the file is in fact benign, it can be added to a whitelist so that it is not subsequently flagged as being malicious (e.g., by another appliance). 
     One approach to whitelisting is for security platform  122  to instruct appliance  102  to add the file to a whitelist stored at appliance  102 . Another approach is for security platform  122  to instruct whitelist system  154  of false positives and for whitelist system  154  in turn to keep appliances such as appliance  102  up to date with false positive information. As previously mentioned, one problem with appliances such as appliance  102  is that they are resource constrained. One approach to minimizing resources used in maintaining a whitelist at an appliance is to maintain the whitelist using a Least Recently Used (LRU) cache. The whitelist can comprise file hashes, and can also be based on other elements, such as feature vectors or hashes of feature vectors. 
     VI. Building Models 
     Returning to the environment depicted in  FIG. 1 , as previously explained, security platform  122  is configured to perform static and dynamic analysis on samples that it receives. Security platform  122  can receive samples for analysis from a variety of sources. As previously mentioned, one example type of sample source is a data appliance (e.g., data appliances  102 ,  136 , and  148 ). Other sources (e.g., one or more third party providers of samples, such as other security appliance vendors, security researchers, etc.) can also be used as applicable. As will be described in more detail below, security platform  122  can use the corpus of samples that it receives to build models (e.g., which can then be used by security appliance  102  in accordance with embodiments of the techniques described herein). 
     In various embodiments, static analysis engine  306  is configured to perform feature extraction on samples that it receives (e.g., while also performing other static analysis functions as described above). An example process for performing feature extraction (e.g., by security platform  122 ) is depicted in  FIG. 8A . Process  800  begins at  802  when static analysis of a sample is commenced. During feature extraction ( 804 ), all 8-grams (or other applicable n-grams in embodiments where 8-grams are not used) are extracted out of the sample being processed (e.g., sample  130  in  FIG. 3 ). In particular, a histogram of the 8-grams in the sample being analyzed is extracted (e.g., into a hash table), which indicates the number of times a given 8-gram was observed in the sample being processed. One benefit of extracting 8-grams during feature analysis by static analysis engine  306  is that potential privacy and contractual problems in using samples obtained from third parties (e.g., in constructing models) can be mitigated, as the original file cannot be reconstructed from the resulting histogram. The extracted histogram is stored at  806 . 
     In various embodiments, static analysis engine  306  stores the extracted histogram (e.g., represented using a hash table) for a given sample in storage  142  (e.g., a Hadoop cluster) along with the histograms extracted from other samples. The data in Hadoop is compressed and when operations are performed on the Hadoop data, the needed data is uncompressed on the fly. An example hash table (represented in JSON) for a file is shown in  FIG. 7A . Line  702  indicates the SHA-256 hash of the file. Line  704  indicates the UNIX time at which sample  130  arrives at security platform  122 . Line  706  indicates a count of n-grams in the overlay section (e.g., ‘d00fbf4e088bc366’:1 represents that one instance of ‘d00fbf4e088bc366’ was found in the overlay section). Line  708  indicates a count of each of the 8-grams present in the file. Line  710  indicates that the file has an overlay. Line  712  indicates that the filetype of the file is “.exe.” Line  714  indicates the UNIX time at which security platform  122  finished processing sample  130 . Line  716  indicates a count of each of the non 8-gram features the file hit. Finally, line  718  indicates that the file was determined (e.g., by security platform  122 ) to be malicious. 
     In an example embodiment, the set of 8-gram histograms stored in the Hadoop cluster grows by approximately three terabytes of 8-gram histogram data per day. The histograms will correspond to both malicious and benign samples (which will be labeled as such, e.g., based on results of other static and dynamic analyses performed by security platform  122  as described above). 
     A histogram of 8-grams extracted from a sample being analyzed will be approximately 10% larger than the file itself, and a typical sample will have a histogram comprising approximately 1 million different 8-grams. The total number of different possible 8-grams is 2 64 . As mentioned above, in contrast, the classification models sent by security platform  122  (e.g., as part of a subscription) to devices such as data appliance  102 , in various embodiments, comprise only a few thousand features (e.g., 1,000 features). One example way to reduce the set of potentially up to 2 64  features to the most important 1,000 features for use in a model is to use a mutual information technique. Other approaches can also be used as applicable (e.g., Chi-squared score). The four needed parameters include the number of malicious samples having a given feature, the number of benign samples having the given features, the total number of malicious samples, and the total number of benign samples. One benefit of mutual information is that it can efficiently be used on very large data sets. In Hadoop, the mutual information approach can be performed in a single pass (i.e., through all the 8-gram histograms stored in the Hadoop cluster dataset for a given filetype) by distributing the task across multiple mappers, each of which is responsible for handling a specific feature. Those features having the highest mutual information can be selected as the set of features most indicative of maliciousness and/or most indicative of benignness, as applicable. The resulting 1,000 features can then be used to build models (e.g., linear classification models and non-linear classification models) as applicable. For example, to build a linear classification model, model builder  152  (implemented using a set of open source tools and/or scripts authored in an appropriate language such as python) saves the top 1,000 features and applicable weights as the set of n-gram features for appliance  102  to check against (e.g., as described in Section V.A.4 above). 
     In some embodiments, the non-linear classification model is also built by model builder  152  using the top 1,000 (or other desired number of) features. In other embodiments, the non-linear classification model is constructed predominantly using the top features (e.g., 950) but also incorporates other, non n-gram features (e.g., 50 such features) that can also be detected during packet-by-packet feature extraction and analysis. Some examples of non n-gram features that can be incorporated into the non-linear classification model include: (1) the size of the header, (2) the presence or absence of a checksum in the file, (3) number of sections in the file, (4) the purported length of the file (as indicated in the header of the PE file), (5) whether the file includes an overlay portion, and (6) whether the file requires the Windows EFI Subsystem to execute the PE. 
     In some embodiments, rather than using mutual information to select the top 1,000 features, a larger set of features (an overgenerated set of features) is determined. As an example, the top 5,000 features can initially be selected using mutual information. That set of 5,000 can then be used as input to a traditional feature selection technique (e.g., bagging) which might not scale well to very large datasets (e.g., the entire Hadoop dataset), but be more effective on a reduced set (e.g., 5,000 features). The traditional feature selection technique can be used to select the final 1,000 features from the set of 5,000 features identified using mutual information. 
     Once the final 1,000 features are selected, an example way to construct the non-linear model is to use an open source tool such as scikit-learn or XGBoost. As applicable, parameter tuning can be performed, such as by using cross-validation. 
     An example process for generating a model is depicted in  FIG. 8B . In various embodiments, process  850  is performed by security platform  122 . Process  850  begins at  852  when a set of extracted features (e.g., including n-gram features) is received. One example way the set of features can be received is by reading features stored as a result of process  800 . At  854 , a reduced set of features is determined from the features received at  852 . As described above, an example way of determining a reduced set of features is by using mutual information. Other approaches (e.g., Chi-squared score) can also be used. Further, as also described above, a combination of techniques can also be used at  852 / 854 , such as selecting an initial set of features using mutual information and refining the initial set using bagging or another appropriate technique. Finally, as also described above, once the features are selected (e.g., at  854 ), appropriate models are built at  856  (e.g., using open source or other tools, and as applicable, performing parameter tuning). Models (e.g., generated by model builder  152  using process  850 ) can be sent (e.g., as part of a subscription service) to data appliance  102  and other applicable recipients (e.g., data appliances  136  and  148 ). 
     In various embodiments, model builder  152  generates models (e.g., linear and non-linear classification models) on a daily (or other applicable) basis. By performing process  850  or otherwise periodically generating models, security platform  122  can help ensure that the models used by appliances such as appliance  102  detect the most current types of malware threats (e.g., those most recently deployed by nefarious individuals). 
     Whenever a newly-generated model is determined to be better than an existing model (e.g., as determined based on a set of quality assessment metrics exceeding a threshold), updated models can be transmitted to data appliances such as data appliance  102 . In some cases, such updates adjust weights assigned to features. Such updates can be readily deployed to and adopted by appliances (e.g., as real-time updates). In other cases, such updates adjust the features themselves. Such updates can be more complicated to deploy because they may require patches to components of the appliance, such as the decoder. One benefit of using overtraining during model generation is that the model can take into account whether the decoder is capable of detecting particular features or not. 
     In various embodiments, appliances are required (e.g., by security platform  122 ) to deploy updates to models as they are received. In other embodiments, appliances are allowed to selectively deploy updates (at least for a period of time). As one example, when a new model is received by appliance  102 , the existing model and new model can both be run in parallel on appliance  102  for a period of time (e.g., with the existing model being used in production and the new model reporting on actions that it would take without actually taking them). An administrator of the appliance is able to indicate whether the existing model or the new model should be used to process traffic on the appliance (e.g., based on which model performs better). In various embodiments, appliance  102  provides telemetry back to security platform  122  that indicates information such as which model(s) are running on appliance  102  and how effective the model(s) are (e.g., false positive statistical information). 
     Although the foregoing embodiments have been described in some detail for purposes of clarity of understanding, the invention is not limited to the details provided. There are many alternative ways of implementing the invention. The disclosed embodiments are illustrative and not restrictive.