System and method for analysis of a memory dump associated with a potentially malicious content suspect

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 13/801,545 filed Mar. 13, 2013, now U.S. Pat. No. 9,355,247, issued May 31, 2016 and entitled “File Extraction from Memory Dump for Malicious Content Analysis,” the entire contents of all of which are incorporated by reference herein.

FIELD

Embodiments of the present invention relate generally to malicious content detection. More particularly, embodiments of the invention relate to performing a malicious content analysis based on memory dump of a malware suspect.

BACKGROUND

Malicious software, or malware for short, may include any program or file that is harmful by design to a computer. Malware includes computer viruses, worms, Trojan horses, adware, spyware, and any programming that gathers information about a computer or its user or otherwise operates without permission. The owners of the computers are often unaware that these programs have been added to their computers and are often similarly unaware of their function.

Malicious network content is a type of malware distributed over a network via websites, e.g., servers operating on a network according to an hypertext transfer protocol (HTTP) standard or other well-known standard. Malicious network content distributed in this manner may be actively downloaded and installed on a computer, without the approval or knowledge of its user, simply by the computer accessing the web site hosting the malicious network content (the “malicious web site”). Malicious network content may be embedded within objects associated with web pages hosted by the malicious web site. Malicious network content may also enter a computer on receipt or opening of email. For example, email may contain an attachment, such as a PDF document, with embedded malicious executable programs. Furthermore, malicious content may exist in files contained in a computer memory or storage device, having infected those files through any of a variety of attack vectors.

Various processes and devices have been employed to prevent the problems associated with malicious content. For example, computers often run antivirus scanning software that scans a particular computer for viruses and other forms of malware. The scanning typically involves automatic detection of a match between content stored on the computer (or attached media) and a library or database of signatures of known malware. The scanning may be initiated manually or based on a schedule specified by a user or system administrator associated with the particular computer. Unfortunately, by the time malware is detected by the scanning software, some damage on the computer or loss of privacy may have already occurred, and the malware may have propagated from the infected computer to other computers. Additionally, it may take days or weeks for new signatures to be manually created, the scanning signature library updated and received for use by the scanning software, and the new signatures employed in new scans.

Moreover, anti-virus scanning utilities may have limited effectiveness to protect against all exploits by polymorphic malware. Polymorphic malware has the capability to mutate to defeat the signature match process while keeping its original malicious capabilities intact. Signatures generated to identify one form of a polymorphic virus may not match against a mutated form. Thus polymorphic malware is often referred to as a family of virus rather than a single virus, and improved anti-virus techniques to identify such malware families is desirable.

Another type of malware detection solution employs virtual environments to replay content within a sandbox established by virtual machines (VMs). Such solutions monitor the behavior of content during execution to detect anomalies that may signal the presence of malware. One such system offered by FireEye, Inc., the assignee of the present patent application, employs a two-phase malware detection approach to detect malware contained in network traffic monitored in real-time. In a first or “static” phase, a heuristic is applied to network traffic to identify and filter packets that appear suspicious in that they exhibit characteristics associated with malware. In a second or “dynamic” phase, the suspicious packets (and typically only the suspicious packets) are replayed within one or more virtual machines. For example, if a user is trying to download a file over a network, the file is extracted from the network traffic and analyzed in the virtual machine. The results of the analysis aids in determining whether the file is malicious. The two-phase malware detection solution may detect numerous types of malware and, even malware missed by other commercially available approaches. Through verification, the two-phase malware detection solution may also achieve a significant reduction of false positives relative to such other commercially available approaches. Dealing with false positives in malware detection may needlessly slow or interfere with download of network content or receipt of email, for example. This two-phase approach has even proven successful against many types of polymorphic malware and other forms of advanced persistent threats.

Some malware may have embedded malicious content therein that has been encrypted. The malicious content may only be decrypted and extracted during the execution of the malware or when the malware is being accessed. For example, a file such as a portable document format (PDF) file may include embedded code, which when the file is being accessed (e.g., opened by an Abode Acrobat™ reader) will access a remote Web site to download malicious content and store the same at a storage of the local device. The code may be encrypted within the PDF file that a typical malware detection process cannot decrypt without knowing the corresponding decryption method. Similarly, a malicious content suspect may include an authorized file embedded and encrypted therein, which is extracted and decrypted only during an execution of the malicious content suspect. It is difficult to track down where the extracted material has been stored and to determine whether the encrypted content is malicious.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention are illustrated by way of example and not limitation in the figures of the accompanying drawings in which like references indicate similar elements.

FIG. 1 is a block diagram illustrating a malicious content detection system according to one embodiment of the invention.

FIGS. 2A and 2B are flow diagrams illustrating a method for malicious content detection according to some embodiments of the invention.

FIG. 3 is a block diagram illustrating a malicious content detection system according to another embodiment of the invention.

FIG. 4 is a flow diagram illustrating a method for malicious content detection according to another embodiment of the invention.

FIG. 5 is a block diagram of a computer network system deploying a malicious content detection system according to one embodiment of the invention.

FIG. 6 is a block diagram illustrating an example of a data processing system which may be used with one embodiment of the invention.

DETAILED DESCRIPTION

Various embodiments and aspects of the inventions will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. The following description and drawings are illustrative of the invention and are not to be construed as limiting the invention. Numerous specific details are described to provide a thorough understanding of various embodiments of the present invention. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the present inventions.

Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in conjunction with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification do not necessarily all refer to the same embodiment.

Techniques for malicious content detection using memory dump are described herein. According to some embodiments, a malicious content suspect (e.g., suspicious content) is executed in a virtual machine (VM) as a malicious content detection environment (e.g., sandboxed environment). During the execution of the malicious content suspect, a memory dump is captured. The memory dump includes any information in a memory of the VM associated with the malicious content suspect during the execution of the malicious content suspect. The information captured by the memory dump may include the code image of the malicious content suspect, content of a memory region that has been allocated by the malicious content suspect (e.g., heaps, threads, context), data accessed by the malicious content suspect, embedded content extracted or downloaded during the execution such as a file or another executable binary, as well as information identifying certain activities performed by the malicious content suspect. The memory dump is then analyzed to determine whether the malicious content suspect is indeed malicious. The memory dump may be stored in a dump file and analyzed subsequently (e.g., offline) for malicious content detection.

In addition, some content extracted or downloaded by the malicious content suspect during the execution may be stored as one or more files in a storage location of a storage device associated with the VM (e.g., a VM disk file). Given the nature of the VM, typically it is difficult to access those files since they are protected by the VM architecture. According to one embodiment, a file extracting mechanism is to mount at least the storage location of the storage device that the malicious content suspect has accessed into a temporary directory. The files stored by the malicious content suspect may then be identified, for example, based on the memory dump or an event log that records the events (e.g., file open, file creation) triggered by the malicious content suspect and monitored by a monitoring module running within the VM. Those files, as well as the dump file, may be accessed from the temporary directory for malicious content detection. As a result, any encrypted or embedded content that is only decrypted or extracted during the execution of the malicious content suspect can be captured and analyzed.

FIG. 1 is a block diagram illustrating a malicious content detection system according to one embodiment of the invention. Referring to FIG. 1, system 100 includes controller 101 to manage or control one or more virtual machines (VMs) 102 (also referred to as a sandboxed operating environment or simply a sandbox), where content associated with VMs 102 are stored in storage device 109 in a form of VM disk files 110.

Controller 101 may be implemented as part of a VM monitor or manager (VMM), also referred to as a hypervisor for managing or monitoring VMs, which may be hosted by a host operating system (OS). VM 102 may be hosted by a guest OS. The host OS and the guest OS may be the same type of operating systems or different types of operating systems (e.g., Windows™, Linux™, Unix™, Mac OS™, iOS™, etc.) or different versions thereof. A VM is a simulation of a machine (abstract or real) that is usually different from the target machine (where it is being simulated on). Virtual machines may be based on specifications of a hypothetical computer or emulate the computer architecture and functions of a real world computer. A virtual machine referred to herein can be any type of virtual machines, such as, for example, hardware emulation, full virtualization, para-virtualization, and operating system-level virtualization virtual machines.

According to one embodiment, when malicious content suspect 106 is received for a dynamic content analysis (as opposed to be a static content analysis described below), a scheduler 140 of controller 101 is configured to identify and select a VM, in this example VM 102, from a VM pool 145 that has been configured to closely simulate a target operating environment (e.g., particular version of an OS with particular versions of certain software installed therein) in which malicious content suspect 106 is to be analyzed. The scheduler 140 then launches VM 102 in which monitoring module 105 is running within VM 102 and configured to monitor activities and behavior of malicious content suspect 106.

In addition, monitoring module 105 maintains a persistent communication channel with analysis module 103 of controller 101 to communicate certain events or activities of malicious content suspect 106 during the execution. In response to detecting certain predetermined events triggered by malicious content suspect 106, monitoring module 105 is configured to send a message via the communication channel to analysis module 103, where the message may be recorded as part of event log 108. The message includes information identifying an event triggered by malicious content suspect 106. Event log 108 records events that have been selectively monitored and detected by monitoring module 103, such as, for example, file creation or file open events. Content of the event log 108 may be stored in a persistent storage as part of event log file(s) 112 of VM disk file 110 associated with VM 102. The recorded events may be analyzed by analysis module 103 based on a set of rules or policies (not shown) to determine whether malicious content suspect 106 is likely malicious (e.g., high probability of malicious) and/or should be declared as malicious.

In one embodiment, in response to some predetermined events (e.g., file creation, registry access, DLL loading, process execution, power management such as sleep) triggered by malicious content suspect 106, monitoring module 105 sends a message describing the event(s) via a communication channel to controller 101, which may be recorded as part of event log 108. Event log 108 may be further cached in a persistent storage as part of event log file(s) 112. In addition, monitoring module 105 invoke memory dump module 125 to capture and generate memory dump 126 that captures various information about execution of malicious content suspect 106. A memory dump refers to a snapshot of a memory region associated with or allocated to an application, in this example, malicious content suspect 106. For example, monitoring module 105 can invoking memory dump module 125 by launching memory dump module 125 and passing a process identifier (ID) of malicious content suspect 106 as a parameter to memory dump module 125. Memory dump module 125 may be a part of a memory dump utility associated with a guest OS of VM 102, such as a symbolic debugger. For example, for Windows™ operating system, memory dump module 125 may be part of the NT symbolic debugger (NTSD) utility provided by Microsoft Corporation of Redmond, Wash. Memory dump 126 can then be stored within the associated VM disk file as part of dump file 128.

NTSD is a console debugger that allows a user to analyze user-mode memory and constructs on target applications that are either console or graphical Windows™ applications. When users use NTSD to debug an application that has failed, they can obtain a stack trace or display invalid parameters. NTSD can also be used to observe the behavior of an application that is functioning properly. By using NTSD, a user can display and execute program code, set breakpoints, and examine and change values in memory. NTSD can disassemble binary code and display assembly instructions. NTSD can also enable a user to analyze source code directly.

Furthermore, memory dump 126 and/or dump file 128 can be analyzed by memory dump parser 127 (or a memory dump interpreter) for malicious content detection. In one embodiment, memory dump parser 127 may be a graphical interface debugger associated with the guest OS of VM 302. For example, for the Windows™ operating system, memory dump parser 127 may be part of WINDBG utility provided by Microsoft Corporation. WINDBG is a graphical interface debugger that supports full source-level debugging for Windows™ kernel, kernel-mode drivers, and system services, as well as user-mode applications and drivers. WINDBG can monitor variables, CPU registers, and parameters passed in function calls. It can also be used to review a memory dump file (e.g., crash dump file). By using WINDBG, a user can view source code, set breakpoints, and view variables, stack traces, and memory.

FIG. 2A is a flow diagram illustrating a method for malicious content detection according to one embodiment of the invention. Method 200 may be performed by system 100 of FIG. 1, which may be implemented as processing logic in software, hardware, or a combination of both. Referring to FIG. 2A, at block 201, processing logic executes a malicious content suspect in a virtual machine. At block 202, processing logic monitors the behavior of the malicious content suspect to determine one or more predetermined events triggered by the malicious content suspect. In response to detection of one or more predetermined events, at block 203, processing logic generate a memory dump to capture operational information (e.g., data, code, and activities) of the malicious content suspect. At block 204, the memory dump is stored in a dump file for subsequent malicious content analysis.

FIG. 2B is a flow diagram illustrating a method for malicious content detection according to another embodiment of the invention. Method 250 may be performed by system 100 of FIG. 1, which may be implemented as processing logic in software, hardware, or a combination of both. Alternatively, method 250 may be performed (e.g., offline) by a dedicated content analysis system such as one deployed in the cloud. Referring to FIG. 2B, at block 251, processing logic receives a dump file containing a memory dump of a malicious content suspect. At block 252, processing logic parses the dump file to interpret the dump file. The interpretation can be performed by a trained analyst and/or an automated process. At block 253, processing logic generates an analysis report indicating whether the malicious content suspect should be declared as malicious or having a higher probability of malicious. At block 254, processing logic optionally presents the analysis report via a user interface such as a Web interface.

FIG. 3 is a block diagram illustrating a malicious content detection system according to another embodiment of the invention. Referring to FIG. 3, in addition to analysis module 103 and optional memory dump parser 127, system 300 further includes a file extractor 301 hosted by controller 101. In one embodiment, file extractor 301 is configured to extract files, such as event log file 112, dump file 128, and/or malicious suspect files 303 from VM disk file 110 associated with VM 102. Malicious suspect files 303 may be the embedded and/or encrypted by malicious content suspect 106 that has been extracted and/or decrypted by malicious content suspect 106 during its execution.

Typically, when a suspect is executed for malicious content detection, a VM is launched and the suspect is executed within the VM. After the execution, the VM will be terminated. The corresponding VM disk file may also be deleted. Thus, it is very important to retrieve the files generated during the execution from the VM disk file. Given the nature of a VM, it cannot be simply accessed as ordinary file accesses.

In one embodiment, file extractor 301 mounts at least a portion of VM disk 110 as VM disk mount 302 into a temporary or predetermined directory, such that the files stored therein (e.g., event log file 112, dump file 128, and suspect files 303) may be accessed and analyzed by analysis module 103. In one embodiment, file extractor 301 takes a snapshot of at least the relevant portion of VM disk file 110 and mounts the snapshot into a memory of the host OS as part of VM disk mount 302. The files can then be accessed (e.g., copied to another directory or storage device). For example, suspect files 303 that have been extracted or decrypted by malicious content suspect 106 can be provided as an attachment to an analysis report to an administrator of a corporate client. Suspect files 303 may be identified by examining event log file 112 and/or dump file 128.

FIG. 4 is a flow diagram illustrating a method for malicious content detection according to another embodiment of the invention. Method 400 may be performed by system 300 of FIG. 3, which may be implemented as processing logic in software, hardware, or a combination thereof. Referring to FIG. 4, at block 401, processing logic executes a malicious content suspect within a VM that simulates a target operating environment of the malicious content suspect. At block 402, the execution or behavior of the malicious content suspect is monitored during the execution. For example, certain predetermined events, such as file creation or file modification events, may be monitored and recorded in an event log. In response to detection of one or more predetermined events, at block 403, one or more files accessed by the malicious content suspect are identified and optionally a memory dump of the malicious content suspect is captured and stored in a dump file. At block 404, a snapshot of at least a relevant portion of a VM disk file associated with the VM is taken and mounted onto a temporary directory. Thereafter, at block 405, the identified files, the dump file, and/or event log file(s) may be accessed via the temporary directory.

FIG. 5 is a block diagram of an illustrative computer network system 800 having a malicious content detection system 850 in accordance with a further illustrative embodiment. The malicious content detection system 850 may represent any of the malicious content detection systems described above, such as, for example, detection system 100 of FIG. 1. The malicious content detection system 850 is illustrated with a server device 810 and a client device 830, each coupled for communication via a communication network 820. In various embodiments, there may be multiple server devices and multiple client devices sending and receiving data to/from each other, and the same device can serve as either a server or a client in separate communication sessions. Although FIG. 5 depicts data transmitted from the server device 810 to the client device 830, either device can transmit and receive data from the other.

Note that throughout this application, network content is utilized as an example of content for malicious content detection purposes; however, other types of content can also be applied. Network content may include any data transmitted over a network (i.e., network data). Network data may include text, software, images, audio, or other digital data. An example of network content includes web content, or any network data that may be transmitted using a Hypertext Transfer Protocol (HTTP), Hypertext Markup Language (HTML) protocol, or be transmitted in a manner suitable for display on a Web browser software application. Another example of network content includes email messages, which may be transmitted using an email protocol such as Simple Mail Transfer Protocol (SMTP), Post Office Protocol version 3 (POP3), or Internet Message Access Protocol (IMAP4). A further example of network content includes Instant Messages, which may be transmitted using an Instant Messaging protocol such as Session Initiation Protocol (SIP) or Extensible Messaging and Presence Protocol (XMPP). In addition, network content may include any network data that is transferred using other data transfer protocols, such as File Transfer Protocol (FTP).

The malicious network content detection system 850 may monitor exchanges of network content (e.g., Web content) in real-time rather than intercepting and holding the network content until such time as it can determine whether the network content includes malicious network content. The malicious network content detection system 825 may be configured to inspect exchanges of network content over the communication network 820, identify suspicious network content, and analyze the suspicious network content using a virtual machine to detect malicious network content. In this way, the malicious network content detection system 850 may be computationally efficient and scalable as data traffic volume and the number of computing devices communicating over the communication network 820 increase. Therefore, the malicious network content detection system 825 may not become a bottleneck in the computer network system 800.

The communication network 820 may include a public computer network such as the Internet, in which case a firewall 825 may be interposed between the communication network 820 and the client device 830. Alternatively, the communication network may be a private computer network such as a wireless telecommunication network, wide area network, or local area network, or a combination of networks. Though the communication network 820 may include any type of network and be used to communicate different types of data, communications of web data may be discussed below for purposes of example.

The malicious network content detection system 825 is shown as coupled with the network 820 by a network tap 840 (e.g., a data/packet capturing device). The network tap 840 may include a digital network tap configured to monitor network data and provide a copy of the network data to the malicious network content detection system 825. Network data may comprise signals and data that are transmitted over the communication network 820 including data flows from the server device 810 to the client device 830. In one example, the network tap 840 monitors and copies the network data without an appreciable decline in performance of the server device 810, the client device 830, or the communication network 820. The network tap 840 may copy any portion of the network data, for example, any number of data packets from the network data. In embodiments where the malicious content detection system 850 is implemented as an dedicated appliance or a dedicated computer system, the network tap 840 may include an assembly integrated into the appliance or computer system that includes network ports, network interface card and related logic (not shown) for connecting to the communication network 860 to non-disruptively “tap” traffic thereon and provide a copy of the traffic to the heuristic module 850. In other embodiments, the network tap 840 can be integrated into a firewall, router, switch or other network device (not shown) or can be a standalone component, such as an appropriate commercially available network tap. In virtual environments, a virtual tap (vTAP) can be used to copy traffic from virtual networks.

The network tap 840 may also capture metadata from the network data. The metadata may be associated with the server device 810 and/or the client device 830. For example, the metadata may identify the server device 810 and/or the client device 830. In some embodiments, the server device 810 transmits metadata which is captured by the tap 815. In other embodiments, a heuristic module 860 (described herein) may determine the server device 810 and the client device 830 by analyzing data packets within the network data in order to generate the metadata. The term, “content,” as used herein may be construed to include the intercepted network data and/or the metadata unless the context requires otherwise.

The malicious network content detection system 825 may include a heuristic module 860, a heuristics database 862, a scheduler 870, a virtual machine pool 880, an analysis engine 882 and a reporting module 884. In some embodiments, the network tap 840 may be contained within the malicious network content detection system 850.

The heuristic module 860 receives the copy of the network data from the network tap 840 and applies heuristics to the data to determine if the network data might contain suspicious network content. The heuristics applied by the heuristic module 860 may be based on data and/or rules stored in the heuristics database 862. The heuristic module 860 may examine the image of the captured content without executing or opening the captured content. For example, the heuristic module 860 may examine the metadata or attributes of the captured content and/or the code image (e.g., a binary image of an executable) to determine whether a certain portion of the captured content matches a predetermined pattern or signature that is associated with a particular type of malicious content. In one example, the heuristic module 860 flags network data as suspicious after applying a heuristic analysis. This detection process is also referred to as a static malicious content detection. The suspicious network data may then be provided to the scheduler 870. In some embodiments, the suspicious network data is provided directly to the scheduler 870 with or without buffering or organizing one or more data flows.

When a characteristic of the packet, such as a sequence of characters or keyword, is identified that meets the conditions of a heuristic, a suspicious characteristic of the network content is identified. The identified characteristic may be stored for reference and analysis. In some embodiments, the entire packet may be inspected (e.g., using deep packet inspection techniques) and multiple characteristics may be identified before proceeding to the next step. In some embodiments, the characteristic may be determined as a result of an analysis across multiple packets comprising the network content. A score related to a probability that the suspicious characteristic identified indicates malicious network content is determined.

The heuristic module 860 may also provide a priority level for the packet and/or the features present in the packet. The scheduler 870 may then load and configure a virtual machine from the virtual machine pool 880 in an order related to the priority level, and dispatch the virtual machine to the analysis engine 882 to process the suspicious network content.

The heuristic module 860 may provide the packet containing the suspicious network content to the scheduler 870, along with a list of the features present in the packet and the malicious probability scores associated with each of those features. Alternatively, the heuristic module 860 may provide a pointer to the packet containing the suspicious network content to the scheduler 870 such that the scheduler 870 may access the packet via a memory shared with the heuristic module 860. In another embodiment, the heuristic module 860 may provide identification information regarding the packet to the scheduler 870 such that the scheduler 870, replayer 805, or virtual machine may query the heuristic module 860 for data regarding the packet as needed.

The scheduler 870 may identify the client device 830 and retrieve a virtual machine associated with the client device 830. A virtual machine may itself be executable software that is configured to mimic the performance of a device (e.g., the client device 830). The virtual machine may be retrieved from the virtual machine pool 880. Furthermore, the scheduler 870 may identify, for example, a Web browser running on the client device 830, and retrieve a virtual machine associated with the web browser.

In some embodiments, the heuristic module 860 transmits the metadata identifying the client device 830 to the scheduler 870. In other embodiments, the scheduler 870 receives one or more data packets of the network data from the heuristic module 860 and analyzes the one or more data packets to identify the client device 830. In yet other embodiments, the metadata may be received from the network tap 840.

The scheduler 870 may retrieve and configure the virtual machine to mimic the pertinent performance characteristics of the client device 830. In one example, the scheduler 870 configures the characteristics of the virtual machine to mimic only those features of the client device 830 that are affected by the network data copied by the network tap 840. The scheduler 870 may determine the features of the client device 830 that are affected by the network data by receiving and analyzing the network data from the network tap 840. Such features of the client device 830 may include ports that are to receive the network data, select device drivers that are to respond to the network data, and any other devices coupled to or contained within the client device 830 that can respond to the network data. In other embodiments, the heuristic module 860 may determine the features of the client device 830 that are affected by the network data by receiving and analyzing the network data from the network tap 840. The heuristic module 850 may then transmit the features of the client device to the scheduler 870.

The virtual machine pool 880 may be configured to store one or more virtual machines. The virtual machine pool 880 may include software and/or a storage medium capable of storing software. In one example, the virtual machine pool 880 stores a single virtual machine that can be configured by the scheduler 870 to mimic the performance of any client device 830 on the communication network 820. The virtual machine pool 880 may store any number of distinct virtual machines that can be configured to simulate the performance of a wide variety of client devices 830.

The analysis engine 882 simulates the receipt and/or display of the network content from the server device 810 after the network content is received by the client device 110 to analyze the effects of the network content upon the client device 830. The analysis engine 882 may identify the effects of malware or malicious network content by analyzing the simulation of the effects of the network content upon the client device 830 that is carried out on the virtual machine. There may be multiple analysis engines 850 to simulate multiple streams of network content. The analysis engine 882 may be configured to monitor the virtual machine for indications that the suspicious network content is in fact malicious network content. Such indications may include unusual network transmissions, unusual changes in performance, and the like. This detection process is referred to as a dynamic malicious content detection.

The analysis engine 882 may flag the suspicious network content as malicious network content according to the observed behavior of the virtual machine. The reporting module 884 may issue alerts indicating the presence of malware, and using pointers and other reference information, identify the packets of the network content containing the malware. Additionally, the server device 810 may be added to a list of malicious network content providers, and future network transmissions originating from the server device 810 may be blocked from reaching their intended destinations, e.g., by firewall 825.

The computer network system 800 may also include a further communication network 890, which couples the malicious content detection system (MCDS) 850 with one or more other MCDS, of which MCDS 892 and MCDS 894 are shown, and a management system 896, which may be implemented as a Web server having a Web interface. The communication network 890 may, in some embodiments, be coupled for communication with or part of network 820. The management system 896 is responsible for managing the MCDS 850, 892, 894 and providing updates to their operation systems and software programs. Also, the management system 896 may cause malware signatures generated by any of the MCDS 850, 892, 894 to be shared with one or more of the other MCDS 850, 892, 894, for example, on a subscription basis. Moreover, the malicious content detection system as described in the foregoing embodiments may be incorporated into one or more of the MCDS 850, 892, 894, or into all of them, depending on the deployment. Also, the management system 896 itself or another dedicated computer station may incorporate the malicious content detection system in deployments where such detection is to be conducted at a centralized resource.

Further information regarding an embodiment of a malicious content detection system can be had with reference to U.S. Pat. No. 8,171,553, the disclosure of which being incorporated herein by reference in its entirety.

As described above, the detection or analysis performed by the heuristic module 860 may be referred to as static detection or static analysis, which may generate a first score (e.g., a static detection score) according to a first scoring scheme or algorithm. The detection or analysis performed by the analysis engine 882 is referred to as dynamic detection or dynamic analysis, which may generate a second score (e.g., a dynamic detection score) according to a second scoring scheme or algorithm. The first and second scores may be combined, according to a predetermined algorithm, to derive a final score indicating the probability that a malicious content suspect is indeed malicious.

Furthermore, detection systems 850 and 892-894 may deployed in a variety of distribution ways. For example, detection system 850 may be deployed as a detection appliance at a client site to detect any suspicious content, for example, at a local area network (LAN) of the client. In addition, any of MCDS 892 and MCDS 894 may also be deployed as dedicated data analysis systems. Systems 850 and 892-894 may be configured and managed by a management system 896 over network 890, which may be a LAN, a wide area network (WAN) such as the Internet, or a combination of both. Management system 896 may be implemented as a Web server having a Web interface to allow an administrator of a client (e.g., corporation entity) to log in to manage detection systems 850 and 892-894. For example, an administrator may able to activate or deactivate certain functionalities of malicious content detection systems 850 and 892-894 or alternatively, to distribute software updates such as malicious content definition files (e.g., malicious signatures or patterns) or rules, etc. Furthermore, a user can submit via a Web interface suspicious content to be analyzed, for example, by dedicated data analysis systems 892-894. As described above, malicious content detection includes static detection and dynamic detection. Such static and dynamic detections can be distributed amongst different systems over a network. For example, static detection may be performed by detection system 850 at a client site, while dynamic detection of the same content can be offloaded to the cloud, for example, by any of detection systems 892-894. Other configurations may exist.

FIG. 6 is a block diagram illustrating an example of a data processing system which may be used with one embodiment of the invention. For example, system 900 may represents any of data processing systems described above performing any of the processes or methods described above. System 900 may represent a desktop, a tablet, a server, a mobile phone, a media player, a personal digital assistant (PDA), a personal communicator, a gaming device, a network router or hub, a wireless access point (AP) or repeater, a set-top box, or a combination thereof.

Referring to FIG. 6, in one embodiment, system 900 includes processor 901 and peripheral interface 902, also referred to herein as a chipset, to couple various components to processor 901 including memory 903 and devices 905-908 via a bus or an interconnect. Processor 901 may represent a single processor or multiple processors with a single processor core or multiple processor cores included therein. Processor 901 may represent one or more general-purpose processors such as a microprocessor, a central processing unit (CPU), or the like. More particularly, processor 901 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processor 901 may also be one or more special-purpose processors such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a network processor, a graphics processor, a network processor, a communications processor, a cryptographic processor, a co-processor, an embedded processor, or any other type of logic capable of processing instructions. Processor 901 is configured to execute instructions for performing the operations and steps discussed herein.

Peripheral interface 902 may include memory control hub (MCH) and input output control hub (ICH). Peripheral interface 902 may include a memory controller (not shown) that communicates with a memory 903. Peripheral interface 902 may also include a graphics interface that communicates with graphics subsystem 904, which may include a display controller and/or a display device. Peripheral interface 902 may communicate with graphics device 904 via an accelerated graphics port (AGP), a peripheral component interconnect (PCI) express bus, or other types of interconnects.

An MCH is sometimes referred to as a Northbridge and an ICH is sometimes referred to as a Southbridge. As used herein, the terms MCH, ICH, Northbridge and Southbridge are intended to be interpreted broadly to cover various chips who functions include passing interrupt signals toward a processor. In some embodiments, the MCH may be integrated with processor 901. In such a configuration, peripheral interface 902 operates as an interface chip performing some functions of the MCH and ICH. Furthermore, a graphics accelerator may be integrated within the MCH or processor 901.

Memory 903 may include one or more volatile storage (or memory) devices such as random access memory (RAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other types of storage devices. Memory 903 may store information including sequences of instructions that are executed by processor 901, or any other device. For example, executable code and/or data of a variety of operating systems, device drivers, firmware (e.g., input output basic system or BIOS), and/or applications can be loaded in memory 903 and executed by processor 901. An operating system can be any kind of operating systems, such as, for example, Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple, Android® from Google®, Linux®, Unix®, or other real-time or embedded operating systems such as VxWorks.

Peripheral interface 902 may provide an interface to IO devices such as devices 905-908, including wireless transceiver(s) 905, input device(s) 906, audio IO device(s) 907, and other IO devices 908. Wireless transceiver 905 may be a WiFi transceiver, an infrared transceiver, a Bluetooth transceiver, a WiMax transceiver, a wireless cellular telephony transceiver, a satellite transceiver (e.g., a global positioning system (GPS) transceiver) or a combination thereof. Input device(s) 906 may include a mouse, a touch pad, a touch sensitive screen (which may be integrated with display device 904), a pointer device such as a stylus, and/or a keyboard (e.g., physical keyboard or a virtual keyboard displayed as part of a touch sensitive screen). For example, input device 906 may include a touch screen controller coupled to a touch screen. The touch screen and touch screen controller can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch screen.

Audio IO 907 may include a speaker and/or a microphone to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and/or telephony functions. Other optional devices 908 may include a storage device (e.g., a hard drive, a flash memory device), universal serial bus (USB) port(s), parallel port(s), serial port(s), a printer, a network interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s) (e.g., a motion sensor, a light sensor, a proximity sensor, etc.), or a combination thereof. Optional devices 908 may further include an imaging processing subsystem (e.g., a camera), which may include an optical sensor, such as a charged coupled device (CCD) or a complementary metal-oxide semiconductor (CMOS) optical sensor, utilized to facilitate camera functions, such as recording photographs and video clips.

Note that while FIG. 6 illustrates various components of a data processing system, it is not intended to represent any particular architecture or manner of interconnecting the components; as such details are not germane to embodiments of the present invention. It will also be appreciated that network computers, handheld computers, mobile phones, and other data processing systems which have fewer components or perhaps more components may also be used with embodiments of the invention.

Some portions of the preceding detailed descriptions have been presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the ways used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations are those requiring physical manipulations of physical quantities.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as those set forth in the claims below, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

The techniques shown in the figures can be implemented using code and data stored and executed on one or more electronic devices. Such electronic devices store and communicate (internally and/or with other electronic devices over a network) code and data using computer-readable media, such as non-transitory computer-readable storage media (e.g., magnetic disks; optical disks; random access memory; read only memory; flash memory devices; phase-change memory) and transitory computer-readable transmission media (e.g., electrical, optical, acoustical or other form of propagated signals—such as carrier waves, infrared signals, digital signals).

The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), firmware, software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially.

In the foregoing specification, embodiments of the invention have been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications may be made thereto without departing from the broader spirit and scope of the invention as set forth in the following claims. The specification and drawings are, accordingly, to be regarded in an illustrative sense rather than a restrictive sense.