Patent Publication Number: US-9888016-B1

Title: System and method for detecting phishing using password prediction

Description:
FIELD OF THE INVENTION 
     Embodiments of the present invention relate generally to malicious content detection. More particularly, embodiments of the invention relate to detecting phishing activity based on predicting a password for decrypting an electronic attachment provided as part of a communication message for malicious content detection. 
     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 a 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. 
     In some situations, malicious content comes in a form of encrypted attachment to an email. In order to perform a malicious content analysis on the attachment, it has to be decrypted first. Conventional malware detection systems cannot perform malware detection without a necessary password because the content has been encrypted. 
    
    
     
       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 network system for email malware detection system according to one embodiment of the invention. 
         FIG. 2  is a block diagram, partially in flow chart form, illustrating an email malware detection system according to one embodiment of the invention. 
         FIG. 3  is a flow diagram illustrating a method for predicting a password for decrypting suspicious content for malware detection according to one embodiment. 
         FIG. 4  is a flow diagram illustrating a method for predicting a password for decrypting suspicious content for malware detection according to another embodiment. 
         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. Also, the term “email” generally denotes a communication message being digital data with a particular format such as one or more packet(s), frame(s), or any other series of bits having a prescribed format, which may include, but not limited or restricted to an electronic mail message, an instant message (IM), or another type of communication message. 
     Aspects of the invention are directed to detecting phishing activity in the form of communication messages, which include malicious content within easily decrypted attachments and are generated to leverage social relationships of trust between a targeted recipient and an apparent sender. The social relationship and apparent encryption of a message is desired to encourage the sender to decrypt the attachment and cause malware to be uploaded and installed on the recipient&#39;s computer system or electronic device. Illustrative techniques for detecting phishing activity involves predicting a password for decrypting an attachment for the purpose of malicious content detection are described herein. 
     Often, malware writers attach encrypted attachments to a communication message generally referred to as an “email” (e.g., an electronic mail message or an instant message, etc.) with a view to enticing a particular recipient to open and decrypt the attachment, thereby releasing a malicious executable or other malware within the recipient&#39;s computer system or device. The malware writers often rely on a social relationship of trust between the apparent (though usually not actual) “sender” of the email and the recipient to make it appear that it is safe to open the attachment. For example, the relationship may be familial or a work relationship. Accordingly, the encryption of the attachment is not intended to actually protect the attachment content, but rather to use the encryption to lull the recipient into believing the attachment is safe to open. In addition, malware authors often provide the password explicitly within the body of the email itself or at least provide hints or clues to the recipient to make the password obvious from the content so as to facilitate decryption by the recipient. To detect malware within the “faux” encrypted attachment by a malware detection system, embodiments of the invention take advantage of this tendency (or trick) of malware authors to use the included or “hinted at” password within a malware detection system to decrypt the attachment and then detect the embedded malware. After decryption, a malware detection system equipped in this way may monitor the behavior the malware, and generate signatures for detection of malware in other email traffic. 
     According to one embodiment, when an email having an attachment is received, the attachment is examined to determine whether the attachment has been encrypted. If the attachment has been encrypted, a list of default passwords is used in an attempt to decrypt the attachment. The list of default passwords may be those commonly used by ordinary users in the world, which may be collected and distributed periodically. If the attachment can be decrypted using the default passwords, a content analysis is performed on the decrypted content to determine whether the attached content likely contains malicious content. 
     If the attachment cannot be decrypted using any of the default passwords, according to one embodiment, a password predictor is invoked to parse the email to locate any possible passwords hints within various portions of the email (e.g. body, subject line, address line, etc.) and attempt to determine or predict one or more password candidates. The password candidates may be determined based on certain content or password patterns (e.g., text phrases) or certain metadata (e.g., domain, addresses) of the email. The password candidates are then used in an attempt to decrypt the encrypted attachment. If the encrypted attachment can be decrypted using any of the password candidates, a content analysis (e.g., static analysis and/or dynamic analysis) is performed on the decrypted attachment. As a result, at least some encrypted content can be analyzed for malware detection. Behavior of execution of the decrypted attachment is then monitored and new malware signatures may be generated for future detection. Alternatively, content of the email may be scanned and analyzed prior to applying the list of default passwords to predict the password. Furthermore, the above mentioned multiple password prediction approaches may be performed individually or in combination, in series or in parallel with no particular order or sequence. 
       FIG. 1  is a block diagram illustrating a network system for email malware detection system according to one embodiment of the invention. Referring to  FIG. 1 , system  100  includes an email malware detection system or EMDS  101  (also referred to as an email malware protection system or EMPS) that may be deployed as at various locations of various local area networks (LANs), e.g., of a corporate entity. EMDS  101  may be configured to monitor and/or intercept any email traffic amongst clients  102 A- 102 B and  103 A- 103 B over network  104  and to detect whether an email contains malicious content (e.g., a malicious executable as an attachment to an email). For example, EMDS  101  may be deployed as, as a standalone malware detection system, part of a firewall of a local network or alternatively, EMDS  101  may be implemented as part of a network gateway, router, switch, and/or an access point. If an email is determined to be a malicious email, it may be quarantined and may not be delivered to the intended recipient(s). Clients  102 - 103  may represent any computing nodes, such as, for example, servers, desktops, laptops, tablets, mobile devices, etc. Network  104  may be a wide area network (WAN), a LAN, or a combination thereof. 
     According to one embodiment, in response to an email having an attachment received from an email sender (e.g., clients  102 ) to be delivered to a recipient (e.g., clients  103 ), EMDS  101  is configured to determine whether the attachment has been encrypted by a password. If the attachment is not encrypted, the attachment is then extracted from the email and a content analysis may be performed on the extracted attachment, for example, by dynamic analysis module  112  and/or static analysis module  113  for dynamic content analysis (also referred to a behavioral analysis) and/or static analysis, respectively. 
     If the attachment has been encrypted, a list of default passwords  111  is used in an attempt to decrypt the attachment. The list of default passwords  111  may be those commonly used by ordinary users in the world and collected based on prior analysis over a period of time. If the attachment can be decrypted using the default passwords  111 , the content analysis is performed, for example, by dynamic analysis module  112  and/or static analysis module  113 , on the decrypted content to determine whether the attached content likely contains malicious content. 
     If the attachment cannot be decrypted using the default passwords  111 , according to one embodiment, a password predictor  110  is invoked to parse the email to locate or identify any possible passwords hints within the email and attempt to determine or predict one or more password candidates. As described above, a sender of the email may provide obvious hints of a password that can be used to decrypt the encrypted attachment to lull the recipient of the email to believe that the attachment is safe to decrypt, giving certain information (e.g., relationship of the sender and recipient) obtained from the email. The purpose of the sender is to convince the recipient to decrypt the attachment using a password provided or hinted by the sender, such that malicious content can be dispatched. Accordingly, an embodiment of the invention is to take advantage of such tendency to predict or determine the password and to decrypt the attachment, such that a malicious content analysis can be performed on the decrypted attachment. 
     According to one embodiment, the password candidates may be determined based on certain content or password patterns (e.g., text phrases) or certain metadata (e.g., domain, addresses) of the email. The password candidates are then used in an attempt to decrypt the encrypted attachment. If the encrypted attachment can be decrypted using any of the password candidates, the content analysis (e.g., static analysis and/or dynamic analysis) is performed on the decrypted attachment. As a result, at least some encrypted content can be analyzed for malware detection. Once the attachment has been determined not to contain malicious content, the email, as well as the attachment, is then forwarded, for example, via the associated communication (e.g. email, IM, etc.) server  105 , to the intended recipient(s)  103 A- 103 B. Otherwise, the email and the attachment may be prevented from being delivered to the intended recipient. Instead, an alert may be generated and sent to an administrator of the local network and/or the intended recipient. Alternatively, only the email is delivered without delivering the attachment and a warning message is displayed to alert the intended recipient(s). 
     Note that the configuration as shown in  FIG. 1  is shown for illustration purposes only. EMDS  101  may be implemented as part of communication server  105 . Alternatively, EMDS  101  may be implemented in a distributed fashion, such as for example, in the cloud (e.g., the Internet). Similarly, dynamic analysis module  112  and/or static analysis module  113  may also be deployed in the network. The static analysis and the dynamic analysis may be performed in sequence or in parallel. Also note that throughout this application, techniques have been described to be utilized for encrypted attachment received via an email. However, it is not so limited; the techniques described throughout may also be applied to other situations, such as, file transport protocol (FTP) download of encrypted files, or Web download of encrypted content, etc., where the hints of a password may be discovered or identified based on the network traffic with the particular site or sites (e.g., domain name, IP addresses, uniform resource locator or URL, download or network traffic history) from which the encrypted content is received. 
       FIG. 2  is a block diagram, partially in flow chart form, illustrating an email malware detection system according to one embodiment of the invention. System  200  may be implemented as part of EMDS  101  of  FIG. 1 . Referring to  FIG. 2 , in one embodiment, when email  201  having email content  202  and encrypted attachment  203  is received, for example, at an EMDS associated with a local network or an email server, the encrypted attachment  203  may be extracted from email  201 . Attachment processing module  206  is configured to apply a list of default passwords  111  to attempt to decrypt encrypted attachment  203 . The default passwords  111  may be the commonly used passwords by the ordinary users, which may be determined or collected over a period of time. The default passwords  111  may be periodically updated from a management server over a network based on the ongoing network traffic and/or malware detection processes, for example, performed by many EMDS systems in the cloud. 
     If encrypted attachment  203  can be decrypted to become decrypted content  207  using default passwords  111 , a content analysis is performed on decrypted content  207 . In one embodiment, a static content analysis is performed on decrypted content  207  by static analysis module  113 , for example, based on heuristics to generate a static malicious indicator or score  208 . In addition, a dynamic or behavioral content analysis is performed on decrypted content  207  by dynamic analysis module  112 , for example, by replaying decrypted content  207  in an isolated operating environment (e.g., virtual machine or sandboxed environment) and observing behaviors of decrypted content  207  to generate a dynamic malicious indicator or score  209 . The indicators or scores  208 - 209  are then utilized to determine whether decrypted content  207  is mostly likely malicious. 
     If attachment processing module  206  cannot decrypt the encrypted attachment  203  using default passwords  111 , according to one embodiment, attachment processing module  206  invokes password predictor  110  to parse email  201  in an attempt to identify any password candidates based on email content  202  and/or email metadata or attributes, for example, in view of a set of password patterns  210 , which may be collected and distributed periodically. An email sender often puts a password for the attachment in the email or uses a password that is closely related to the content or attribute of the email. In one embodiment, password predictor  110  is configured to identify certain commonly used phrases in the email, and based on the identified commonly used phrases, password predictor  110  is configured to identify and extract a password candidate from the nearby content (e.g., texts within a predetermined proximity of a particular phrase. 
     In one embodiment, if password predictor  110  identifies a phrase  204  from email content  202  that matches a predetermined pattern or template as part of password patterns  210  (in this example, “the password is asd34fjd” that matches a predetermined pattern or template of “the password is”), password predictor  110  may extract the nearby content (in this example, “asd34fjd” that immediately follows the phrase of “the password is”) as a password candidate  205 . Password candidate  205  is then utilized to decrypt the encrypted content  203  and a content analysis is performed if the encrypted content  203  can be decrypted. 
     According to some embodiments, password patterns  210  may further include other patterns that may also be utilized to identify password candidates, such as, for example, “password,” “pass,” “p/w,” and “here is the password.” In one embodiment, up to a predetermined number (e.g., five) of words before or after the predetermined patterns may be utilized as potential password candidates. Some common words such as pronouns, adjectives, adverbs, and verbs may be excluded from the phrase during the prediction of passwords. Some words between some annotations, such as, for example, “{ },” “[ ],” “( ),” single quotes, and double quotes, may be identified as potential password candidates. Furthermore, certain words related to a sender of the email, such as a domain name, may be utilized as at least the hints to predict passwords. Some email metadata or attributes, such as, for example, the FROM, TO, and/or SUBJECT fields of an email, may also be utilized. Certain information of the URLs of the Web download may also be utilized as password candidates. Note that any of the above information may be combined to predict the passwords. 
     It will be appreciated that the above variety of password approaches can be utilized individually or in combination, in serious or in parallel with any order or sequence. For example, the password prediction based on email content may be performed first prior to applying a list of default passwords. Alternatively, the password prediction operations based on email content, default passwords, and email metadata may be performed in parallel. Also note that password predictor  110  may be implemented in software (e.g., application, device driver, as part of operating system), hardware (e.g., integrated circuit or a processor having machine-executable code running therein), or a combination of thereof. 
       FIG. 3  is a flow diagram illustrating a method for predicting a password for decrypting suspicious content for malware detection according to one embodiment. Method  300  may be performed by processing logic which may include software, hardware, or a combination thereof. For example, method  300  may be performed by system  200  of  FIG. 2 . Referring to  FIG. 3 , at block  301 , an email having an encrypted attachment (e.g., ZIP file) is received. At block  302 , processing logic predicts a password to decrypt the encrypted attachment based on email content. A password may be obtained from a list of default passwords or predicted based on the content and/or metadata of the email as described above. At block  303 , the predicted password is used to decrypt the attachment. If successful, at block  304 , a static content analysis is performed and at block  305 , a dynamic content analysis is performed on the decrypted content to determine whether the attachment contains malicious content. 
       FIG. 4  is a flow diagram illustrating a method for predicting a password for decrypting suspicious content for malware detection according to another embodiment. Method  400  may be performed as part of operations involved in block  302  of  FIG. 3 . Referring to  FIG. 4 , at block  401 , processing logic determines whether an attachment of an email has been encrypted. If the attachment has not been encrypted, a content analysis can be directly performed on the attachment. Otherwise, at block  402 , processing logic attempts to decrypt the attachment using a list of one or more default passwords. If the default passwords cannot decrypt the attachment, at block  403 , processing logic parses the email, including content and metadata of the email, to determine one or more password candidates. At block  404 , the password candidates are used to decrypt the attachment. Note that, as described above, processing logic may perform password prediction based on content and/or metadata of the email prior to applying a list of default passwords. Alternatively, processing logic may utilize some of all of the above approaches in parallel. 
       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 . In one embodiment, malicious content detection system  850  includes password predictor  110 . As described above, password predictor  110  is configured to use some or all of the techniques described above to predict or determine a password or passwords of an encrypted attachment of an email using certain password “hints” provided by a sender of the email, where the attachment is intended to be decrypted by a recipient of the email using the obvious password provided by the sender. Password predictor  110  is configured to scan the content of the email to discover the intended password(s) to attempt to decrypt the encrypted attachment. Alternatively, password predictor  110  is configured to determine a password based on email metadata (e.g., domain name of the sender) or using a list of commonly used default passwords. 
     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  850  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  increases. Therefore, the malicious network content detection system  850  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  850  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  850 . 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  820  to non-disruptively “tap” traffic thereon and provide a copy of the traffic to the heuristic module  860 . 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  840 . 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  850  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 , 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  860  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  882  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 10 devices such as devices  905 - 908 , including wireless transceiver(s)  905 , input device(s)  906 , audio 10 device(s)  907 , and other 10 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&#39;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 medium (e.g., magnetic disks; optical disks; random access memory; read only memory; flash memory devices; phase-change memory) and transitory computer-readable transmission medium (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.