Systems and methods for curating file clusters for security analyses

The disclosed computer-implemented method for curating file clusters for security analyzes may include (1) identifying a suspicious file that exists on at least one computing system within a computing community, (2) clustering a set of files that includes the suspicious file into a file cluster based at least in part on at least one characteristic shared by the set of files, (3) prioritizing at least one file included in the file cluster based at least in part on a contextual value of the file relative to the file cluster, (4) providing, for presentation to a security analyst, a graphical representation of the file cluster that highlights the prioritized file relative to the file cluster, and then (5) performing at least one security action on the suspicious file based at least in part on feedback received from the security analyst. Various other methods, systems, and computer-readable media are also disclosed.

BACKGROUND

Computer security systems often monitor computing devices for potential security threats. For example, a computing device may implement a traditional computer security system to protect against potential security threats. In this example, as the computing device attempts to download or open a new file, the traditional computer security system may determine whether that new file includes and/or represents malware. Unfortunately, while this traditional computer security system may be able to accurately classify well known malicious and/or clean files, the traditional computer security system may have difficulty classifying unknown files (e.g., zero-day threats and/or files encountered on only one computer within the computer security system's user base).

To increase the accuracy of such threat classifications, some computer security systems may involve and/or rely on human-driven security decisions. For example, a traditional computer security system may detect an unfamiliar file on a computing device. In this example, rather than attempting to classify the unfamiliar file entirely on its own, the traditional computer security system may turn to a human security analyst for the final decision as to whether the unfamiliar file should be classified as malicious, clean, or unknown. Unfortunately, while the human security analyst may have certain unprogrammable insight into and/or skill for making such security decisions, the traditional computer security system may fail to provide sufficient information about the unfamiliar file to enable the human security analyst to make a truly informed decision on how to classify and/or address the file's threat risk.

The instant disclosure, therefore, identifies and addresses a need for additional and improved systems and methods for curating file clusters for security analyses (especially those involving human-driven security decisions).

SUMMARY

As will be described in greater detail below, the instant disclosure describes various systems and methods for curating file clusters for security analyses by clustering a set of files in connection with a security event and then providing, for presentation to a security analyst, a graphical representation of the file cluster that highlights certain files based at least in part on the contextual value of the files.

In some examples, a computer-implemented method for curating file clusters for security analyses may include (1) identifying a suspicious file that exists on at least one computing system within a computing community, (2) clustering a set of files that includes the suspicious file into a file cluster based at least in part on at least one characteristic shared by the set of files, (3) prioritizing at least one file included in the file cluster based at least in part on a contextual value of the file relative to the file cluster, (4) providing, for presentation to a security analyst, a graphical representation of the file cluster that highlights the prioritized file relative to the file cluster, and then (5) performing at least one security action on the suspicious file based at least in part on feedback received from the security analyst in connection with the graphical representation of the file cluster provided for presentation to the security analyst.

Examples of the security action include, without limitation, classifying the suspicious file or the file cluster as malicious, classifying the suspicious file or the file cluster as non-malicious, directing the computing system within the computing community to quarantine the suspicious file or the file cluster, directing at least one additional computing system within the computing community to quarantine the suspicious file or the file cluster, preventing at least one other computing system within the computing community from accessing the suspicious file or the file cluster, variations of one or more of the same, combinations of one or more of the same, or any other suitable security action.

In one example, the method may also include detecting at least one security event in connection with the suspicious file. In this example, the method may further include determining that the file is suspicious based at least in part on the detected security event.

In one example, the method may also include measuring a degree of similarity among the set of files. In this example, the method may further include determining that the degree of similarity among the set of files is above a similarity threshold. Additionally or alternatively, the method may include clustering the set of files into the file cluster due at least in part to the degree of similarity among the files being above the similarity threshold.

The method may also include calculating the degree of similarity among the set of files based at least in part on various factors. Examples of such factors include, without limitation, whether the set of files co-exist with one another on a certain amount of computing systems within the computing community, whether the set of files are typically created or modified within a certain amount of time of one another on computing systems within the computing community, whether the set of files have filenames whose similarity exceeds a certain threshold, whether the set of files are signed by a common entity, whether the set of files exhibit common behavioral patterns, whether the set of files have at least one library linkage dependency in common, variations of one or more of the same, combinations of one or more of the same, or any other suitable factors.

In one example, the method may also include ranking the file higher than at least one other file included in the file cluster due at least in part to the contextual value of the file. In this example, the method may further include organizing the graphical representation of the file cluster as an ordered list that corresponds to the ranking.

In one example, the method may also include identifying a file type of the file included in the file cluster. In this example, the method may further include determining that the file type of the file is a contextually meaningful file type. Additionally or alternatively, the method may include prioritizing the file included in the file cluster due at least in part to the file type of the file being a contextually meaningful file type (such as an executable .EXE file type or a device driver .SYS file type).

In one example, the method may also include determining that the file has a known reputation. For example, the method may include determining that the file has a good reputation based at least in part on a reputation score assigned to the file being above a certain threshold. In another example, the method may include determining that the file has a bad reputation based at least in part on a reputation score assigned to the file being below a certain threshold. Additionally or alternatively, the method may further include prioritizing the file included in the file cluster due at least in part to the known reputation of the file.

In one example, the method may also include determining that a filename of the file includes natural language that exceeds a certain length. In this example, the method may further include prioritizing the file included in the file cluster due at least in part to the filename of the file including the natural language that exceeds the certain length.

In one example, the method may also include formulating a summary of the file cluster that highlights certain contextually valuable features of the file cluster. Examples of such features include, without limitation, a file included in the file cluster that has a contextually meaningful file type, a file included in the file cluster whose reputation is highest relative to the file cluster, a file included in the file cluster whose reputation is lowest relative to the file cluster, a file included in the file cluster whose filename is most descriptive, variations of one or more of the same, combinations of one or more of the same, or any other suitable contextually valuable features.

Additionally or alternatively, the method may include generating a graphical representation of the file cluster that portrays the summary of the file cluster. The summary of the file cluster may include a histogram that illustrates reputations of the set of files clustered into the file cluster, a histogram that illustrates hygiene levels of computing systems that include the set of files within the computing community, a count of certain file types included in the file cluster, a histogram that illustrates a number of times that the suspicious file has been downloaded from at least one online source, variations of one or more of the same, combinations of one or more of the same, or any other suitable contextually valuable features.

In one example, the method may also include formatting the graphical representation of the file cluster as a collapsible/expandable list. Additionally or alternatively, the method may include determining that the set of files represent portions of a single software package and then clustering the set of files into the file cluster due at least in part to the set of files representing portions of the single software package.

In some examples, a system for implementing the above-described method may include (1) a clustering module, stored in memory, that (A) identifies a suspicious file that exists on at least one computing system within a computing community and (B) clusters a set of files that includes the suspicious file into a file cluster based at least in part on at least one characteristic shared by the set of files, (2) a prioritization module, stored in memory, that prioritizes at least one file included in the file cluster based at least in part on a contextual value of the file relative to the file cluster, (3) a presentation module, stored in memory, that provides, for presentation to a security analyst, a graphical representation of the file cluster that highlights the prioritized file relative to the file cluster, (4) a security module, stored in memory, that performs at least one security action on the suspicious file based at least in part on feedback received from the security analyst in connection with the graphical representation of the file cluster provided for presentation to the security analyst, and (5) at least one physical processor configured to execute the clustering module, the prioritization module, the presentation module, and the security module.

In some examples, the above-described method may be encoded as computer-readable instructions on a non-transitory computer-readable medium. For example, a computer-readable medium may include one or more computer-executable instructions that, when executed by at least one processor of a computing device, causes the computing device to (1) identify a suspicious file that exists on at least one computing system within a computing community, (2) cluster a set of files that includes the suspicious file into a file cluster based at least in part on at least one characteristic shared by the set of files, (3) prioritize at least one file included in the file cluster based at least in part on a contextual value of the file relative to the file cluster, (4) provide, for presentation to a security analyst, a graphical representation of the file cluster that highlights the prioritized file relative to the file cluster, and then (5) perform at least one security action on the suspicious file based at least in part on feedback received from the security analyst in connection with the graphical representation of the file cluster provided for presentation to the security analyst.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present disclosure is generally directed to systems and methods for curating file clusters for security analyses. As will be explained in greater detail below, by curating a file cluster for a security analysis based at least in part on the contextual value of the files included in the file cluster, the systems and methods described herein may summarize the file cluster and/or highlight certain prioritized files and/or features of the file cluster. Moreover, by summarizing the file cluster and/or highlighting certain prioritized files and/or features in this way, the systems and methods described herein may be able to provide a security analyst with sufficient information with which to make an informed decision on how to classify and/or address any security events triggered in connection with a suspicious file included in the file cluster.

The following will provide, with reference toFIGS. 1 and 2, detailed descriptions of exemplary systems for curating file clusters for security analyses. Detailed descriptions of corresponding computer-implemented methods will be provided in connection withFIG. 3. Detailed descriptions of an exemplary graphical representation of a file cluster will be provided in connection withFIG. 4. In addition, detailed descriptions of an exemplary computing system and network architecture capable of implementing one or more of the embodiments described herein will be provided in connection withFIGS. 5 and 6, respectively.

FIG. 1is a block diagram of an exemplary system100for curating file clusters for security analyses. As illustrated in this figure, exemplary system100may include one or more modules102for performing one or more tasks. For example, and as will be explained in greater detail below, exemplary system100may include a clustering module104that identifies a suspicious file that exists on at least one computing system within a computing community and clusters a set of files that includes the suspicious file into a file cluster based at least in part on at least one characteristic shared by the set of files. Exemplary system100may also include prioritization module106that prioritizes at least one file included in the file cluster based at least in part on a contextual value of the file relative to the file cluster.

In addition, and as will be described in greater detail below, exemplary system100may include a presentation module108that provides, for presentation to a security analyst, a graphical representation of the file cluster that highlights the prioritized file relative to the file cluster. Exemplary system100may further include a security module110that performs at least one security action on the suspicious file based at least in part on feedback received from the security analyst in connection with the graphical representation of the file cluster provided for presentation to the security analyst. Although illustrated as separate elements, one or more of modules102inFIG. 1may represent portions of a single module or application (such as SYMANTEC ENDPOINT SECURITY ADVANCED THREAT PROTECTION (SES-ATP)).

Exemplary system100inFIG. 1may be implemented in a variety of ways. For example, all or a portion of exemplary system100may represent portions of exemplary system200inFIG. 2. As shown inFIG. 2, system200may include a network204that facilitates communication between a server206and a computing community216. In this example, computing community216may include and/or represent computing devices202(1)-(N).

As shown inFIG. 2, server206may be programmed with one or more of modules102. In this example, server206may include and/or identify a file cluster208that includes files210(1)-(N). Additionally or alternatively, server206may detect and/or raise a security alert212in connection with one of files210(1)-(N).

In one example, server206may generate a graphical representation214of file cluster208. In this example, graphical representation214may summarize file cluster208by highlighting certain prioritized files and/or information based at least in part on their contextual value.

In one example, one or more of computing devices202(1)-(N) may be programmed with one or more of modules102(although not explicitly illustrated as such inFIG. 2). Additionally or alternatively, one or more of computing devices202(1)-(N) may include, stored, and/or access file202(1). Moreover, one or more of computing devices202(1)-(N) may include, stored, and/or access any portion or all of file cluster208(although not explicitly illustrated as such inFIG. 2).

In one embodiment, one or more of modules102fromFIG. 1may, when executed by at least one processor of computing devices202(1)-(N) and/or server206, enable computing devices202(1)-(N) and/or server206to curate file clusters for security analyses. For example, and as will be described in greater detail below, one or more of modules102may cause one or more of computing devices202(1)-(N) and/or server206to (1) identify suspicious file210(1) that exists on computing system202(1) within computing community216, (2) cluster files210(1)-(N) into file cluster208based at least in part on at least one characteristic shared by files210(1)-(N), (3) prioritize at least one file included in file cluster208based at least in part on a contextual value of the file relative to file cluster208, (4) provide, for presentation to a security analyst, graphical representation214of file cluster208that highlights the prioritized file relative to file cluster208, and then (5) perform at least one security action on file210(1) based at least in part on feedback received from the security analyst in connection with graphical representation214of file cluster208provided for presentation to the security analyst.

Computing devices202(1)-(N) generally represent any type or form of computing device capable of reading computer-executable instructions. Examples of computing devices202(1)-(N) include, without limitation, laptops, tablets, desktops, servers, cellular phones, Personal Digital Assistants (PDAs), multimedia players, embedded systems, wearable devices (e.g., smart watches, smart glasses, etc.), gaming consoles, combinations of one or more of the same, exemplary computing system510inFIG. 5, or any other suitable computing devices.

Computing community216generally represents any collection of computing devices that are protected by a common security service and/or provide information to a common security service. In one example, computing community216may include computing devices202(1)-(N). In this example, computing community216may include and/or represent a user base of a computer security service and/or product. In other words, computing devices202(1)-(N) may implement and/or rely on the same computing security service and/or product for protection against security threats. Examples of computing community216include, without limitation, a user base of a SYMANTEC security service and/or product, a user base of a MICROSOFT security service and/or product, a user base of a MCAFEE or INTEL security service and/or product, a user base of an ACCELOPS security service and/or product, a user base of a CISCO security service and/or product, variations of one or more of the same, combinations of one or more of the same, or any other suitable computing community.

Server206generally represents any type or form of computing device capable of curating file clusters for file analyses. Examples of server206include, without limitation, security servers, application servers, web servers, storage servers, and/or database servers configured to run certain software applications and/or provide various security, web, storage, and/or database services. In one example, server206may belong to a security service provider and/or vendor responsible for protecting computing community216against security threats. Although illustrated as a single device, server206may represent a plurality of servers working in conjunction with one another to protect computing community216against security threats.

Network204generally represents any medium or architecture capable of facilitating communication or data transfer. Examples of network204include, without limitation, an intranet, a Wide Area Network (WAN), a Local Area Network (LAN), a Personal Area Network (PAN), the Internet, a Power Line Communications (PLC) network, a cellular network (e.g., a Global System for Mobile Communications (GSM) network), exemplary network architecture600inFIG. 6, or the like. Network204may facilitate communication or data transfer using wireless or wired connections. In one embodiment, network204may facilitate communication among computing devices202(1)-(N) and server206.

File cluster208generally represents any collection of files grouped and/or united together based at least in part on characteristics shared by the files. In one example, file cluster208may include and/or represent a set of files whose degree of similarity relative to one another is above a certain threshold. This degree of similarity may depend on certain factors and/or characteristics of the files. Examples of such factors and/or characteristics include, without limitation, whether a set of files co-exist with one another on a certain amount of computing systems within a computing community, whether a set of files represent portions of the same software package, whether a set of files are typically created or modified within a certain amount of time of one another on computing systems within a computing community, whether a set of files have filenames whose similarity exceeds a similarity threshold, whether a set of files are signed by a common entity, whether a set of files exhibit common behavioral patterns, whether a set of files have at least one library linkage dependency in common, variations of one or more of the same, combinations of one or more of the same, or any other suitable factors and/or characteristics.

Security alert212generally represents any type or form of alert, warning, and/or block triggered and/or raised in connection with or in response to a security event detected within a computing community. In one example, security alert212may have been triggered and/or raised in connection with and/or in response to the detection of file210(1) within computing community216. Additionally or alternatively, security alert212may represent a single consolidated alert that resulted from the consolidation of various related and/or redundant alerts raised in connection with files210(1)-(N) and/or file cluster208.

Graphical representation214generally represents any type or form of image, graphic, text, and/or visual display that summarizes, prioritizes, and/or highlights certain features of a file cluster. In one example, graphical representation214may include and/or represent a list of notable files included in file cluster208. In this example, the list may be ranked at least in part on the contextual value of the files relative to file cluster208. In other words, the list may identify files ranked in order of descending importance and/or meaningfulness to a security analyst responsible for deciding whether to classify any portion or all of file cluster208as potentially malicious. Graphical representation214may include, represent, and/or identify various information about file cluster208. Examples of such information include, without limitation, a file included in the file cluster that has a contextually meaningful file type, a file included in the file cluster whose reputation is highest relative to the file cluster, a file included in the file cluster whose reputation is lowest relative to the file cluster, a file included in the file cluster whose filename is most descriptive, a histogram that illustrates reputations of the set of files clustered into the file cluster, a histogram that illustrates hygiene levels of computing systems that include the set of files within the computing community, a count of certain file types included in the file cluster, a histogram that illustrates a number of times that the suspicious file has been downloaded from at least one online source, variations of one or more of the same, combinations of one or more of the same, or any other suitable information about a file cluster.

FIG. 3is a flow diagram of an exemplary computer-implemented method300for curating file clusters for security analyses. The steps shown inFIG. 3may be performed by any suitable computer-executable code and/or computing system. In some embodiments, the steps shown inFIG. 3may be performed by one or more of the components of system100inFIG. 1, system200inFIG. 2, computing system510inFIG. 5, and/or portions of exemplary network architecture600inFIG. 6.

As illustrated inFIG. 3, at step302one or more of the systems described herein may identify a suspicious file that exists on at least one computing system within a computing community. For example, clustering module104may, as part of server206and/or one or more of computing devices202(1)-(N) inFIG. 2, identify file210(1) on computing device202(1) within computing community216. Clustering module104may then determine that file210(1) is suspicious in one way or another. Although file210(1) is shown only on computing device202(1) within computing community216inFIG. 2, file210(1) may additionally or alternatively exist on various other computing devices within computing community216.

Various characteristics and/or features of file210(1) may lead clustering module104to the determination that file210(1) is suspicious. For example, file210(1) may trigger and/or cause a security event (such as security alert212). As another example, file210(1) may be unfamiliar, unrecognizable, and/or new to a computer security system that monitors computing community216. Additionally or alternatively, file210(1) may have yet to be assigned a reputation score that indicates the reputation of file210(1). Moreover, file210(1) may exist on only one of computing devices202(1)-(N) within computing community216.

The systems described herein may perform step302in a variety of ways and/or contexts. In some examples, clustering module104may monitor computing devices202(1)-(N) within computing community216for potential security threats. While monitoring computing devices202(1)-(N) in this way, clustering module104may detect a download of file210(1) by computing device202(1). Clustering module104may then collect certain information about file210(1) from computing device202(1) and/or any network devices within network204that facilitated the download of file210(1). Clustering module104may determine that file210(1) is suspicious based at least in part on such information about file210(1).

In some examples, clustering module104may detect at least one security event in connection with file210(1). In one example, clustering module104may receive security alert212from a security agent running on computing device202(1). For example, computing device202(1) may download file210(1) from the Internet. In response to this download of file210(1), the security agent running on computing device202(1) may raise security alert212. The security agent may then notify clustering module104of security alert212and/or send security alert212to clustering module104. Additionally or alternatively, clustering module104may determine that file210(1) is suspicious based at least in part on security alert212.

Returning toFIG. 3, at step304one or more of the systems described herein may cluster a set of files that includes the suspicious file into a file cluster based at least in part on at least one characteristic shared by the set of files. For example, clustering module104may, as part of server206and/or one or more of computing devices202(1)-(N) inFIG. 2, cluster files210(1)-(N) into file cluster208based at least in part on at least one characteristic shared by files210(1)-(N). Although all of files210(1)-(N) are shown only on server206inFIG. 2, all of files210(1)-(N) may additionally or alternatively exist on one or more computing devices within computing community216.

The systems described herein may perform step304in a variety of ways and/or contexts. In some examples, clustering module104may cluster files210(1)-(N) into file cluster208due at least in part to the degree of similarity among files210(1)-(N). In one example, clustering module104may measure the degree of similarity among files210(1)-(N). For example, clustering module104may calculate the degree of similarity among files210(1)-(N) based at least in part on certain factors and/or characteristics of files210(1)-(N). This degree of similarity may be calculated using any type or form of algorithm and/or formula.

Upon measuring the degree of similarity among files210(1)-(N), clustering module104may determine that the degree of similarity among files210(1)-(N) is above a similarity threshold. The term “similarity threshold,” as used herein, generally refers to any type or form of number, percentage, and/or metric used as a reference point for determining whether the degree of similarity among a set of files warrants clustering those files into a file cluster. Clustering module104may then cluster files210(1)-(N) into file cluster208due at least in part to the degree of similarity among files210(1)-(N) being above the similarity threshold.

In one example, clustering module104may cluster files210(1)-(N) into file cluster208based at least in part on files210(1)-(N) representing portions of a single software package. For example, clustering module104may examine and/or analyze certain characteristics of files210(1)-(N) (including any of those described above in connection with calculating the degree of similarity). In this example, clustering module104may determine that files210(1)-(N) represent portions of the same software package based at least in part on this examination and/or analysis. In other words, clustering module104may determine that files210(1)-(N) belong to, originate from, and/or were spawned by the same software package. Clustering module104may then cluster files210(1)-(N) into file cluster208due at least in part to files210(1)-(N) representing portions of the same software package.

Returning toFIG. 3, at step306one or more of the systems described herein may prioritize at least one file included in the file cluster based at least in part on a contextual value of the file relative to the file cluster. For example, prioritization module106may, as part of server206and/or one or more of computing devices202(1)-(N) inFIG. 2, prioritize file210(N) included in file cluster208based at least in part on a contextual value of file210(N) relative to file cluster208. The term “contextual value,” as used herein in connection with a file, generally refers to the importance, significance, and/or advantage that information about the file may provide to a security analyst responsible for making a decision on how to classify and/or remediate a file cluster that includes the file and/or another file with which the file is clustered.

The systems described herein may perform step306in a variety of ways and/or contexts. In some examples, prioritization module106may prioritize files210(1)-(N) included in file cluster208based at least in part on a set of rules. For example, a set of rules may dictate that prioritization module106prioritize files included in file clusters based at least in part on (1) the contextual value of the file types of the files, (2) the reputation scores assigned to the files, and/or (3) whether the filenames of the files include natural language that exceeds a certain length. In this example, prioritization module106may prioritize file210(N) over one or more other files included in file cluster208in accordance with this set of rules.

In one example, prioritization module106may prioritize file210(N) by ranking file210(N) higher than other less contextually valuable files included in file cluster208. For example, prioritization module106may identify a file type of file210(N). In this example, prioritization module106may determine that the file type of file210(N) is contextually meaningful. Prioritization module106may then prioritize file210(1) by ranking file210(1) higher than one or more other files included in file cluster208due at least in part to the file type of file210(1) being more contextually meaningful than the file type(s) of the other files.

The term “contextually meaningful,” as used herein in connection with a file type, generally refers to the importance, significance, and/or advantage that information about a file of that file type may provide to a security analyst responsible for making a decision on how to classify and/or address a file cluster that includes the file of that file type and/or another file with which the file of that file type is clustered. Examples of contextually meaningful file types include, without limitation, executable .EXE file types, device driver .SYS file types, variations of one or more of the same, combinations of one or more of the same, or any other contextually meaningful file types.

As a specific example, a set of rules may dictate that executable .EXE files be prioritized above device driver .SYS files. This set of rules may further dictate that device driver .SYS files be prioritized above dynamic-link library .DLL files. In this example, prioritization module106may determine that file210(N) is an executable .EXE file, file210(2) (not explicitly illustrated inFIG. 2) is a device driver .SYS file, and/or file210(3) (not explicitly illustrated inFIG. 2) is a dynamic-link library .DLL file. Prioritization module106may then prioritize file210(N) over files210(2)-(3) and file210(2) over file210(3) in accordance with this set of rules.

In some examples, prioritization module106may prioritize file210(N) based at least in part on the known reputation of file210(N). For example, prioritization module106may determine that file210(N) has a known reputation (as opposed to having no known reputation and/or no known reputation score). In one example, prioritization module106may determine that file210(N) has a good reputation since the reputation score assigned to file210(N) is above a certain threshold. In another example, prioritization module106may determine that file210(N) has a bad reputation since the reputation score assigned to file210(N) is below a certain threshold.

Either way, a file that has a known good or bad reputation may be considered more contextually valuable and/or meaningful to a security analyst than a file that has no known reputation or a reputation score that corresponds to neither a good reputation nor a bad reputation. As a result, prioritization module106may prioritize file210(N) over one or more other files included in file cluster208due at least in part to the known reputation (whether good or bad) of file210(N).

As a specific example, a set of rules may dictate that files with bad reputations be prioritized above files with good reputations. This set of rules may further dictate that good reputations be prioritized above files with unknown reputations and/or low-confidence reputations. In this example, prioritization module106may determine that file210(N) has a very bad reputation, file210(2) (not explicitly illustrated inFIG. 2) has a fairly good reputation, and/or file210(3) (not explicitly illustrated inFIG. 2) has an unknown reputation or a low-confidence reputation. Prioritization module106may then prioritize file210(N) over files210(2)-(3) and file210(2) over file210(3) in accordance with this set of rules.

In some examples, prioritization module106may prioritize file210(N) based at least in part on the filename, file path, and/or directory of file210(N). For example, prioritization module106may determine that file210(N) has a filename, file path, and/or directory that includes natural language. In this example, prioritization module106may determine that the natural language exceeds a certain length (e.g., a certain number of characters in the natural language) and/or level of descriptiveness. Prioritization module106may then prioritize file210(N) over one or more other files included in file cluster208due at least in part to the length and/or descriptiveness of the natural language included in the filename, file path, and/or directory of file210(N). The term “natural language,” as used herein, generally refers to any type or form of character string that includes and/or represents human-spoken and/or human-written language and/or wording.

As a specific example, a set of rules may dictate that files with filenames that include natural language of a certain length and/or descriptiveness be prioritized above files without such filenames. For example, prioritization module106may determine that file210(N) has a very descriptive filename that explicitly spells out the name of a software package and file210(2) has a filename that does not include any intelligible natural language. In this example, file210(N) may belong to, originate from, and/or were spawned by the software package whose name is spelled out in the filename. Prioritization module106may then prioritize file210(N) over files210(2) in accordance with this set of rules.

Returning toFIG. 3, at step308one or more of the systems described herein may provide, for presentation to a security analyst, a graphical representation of the file cluster that highlights the prioritized file relative to the file cluster. For example, presentation module108may, as part of server206and/or one or more of computing devices202(1)-(N) inFIG. 2, provide graphical representation214of file cluster208for presentation to a human security analyst. In this example, the security analyst may be an employee and/or representative of a security service provider and/or vendor responsible for protecting computing community216against potential security threats. Additionally or alternatively, the security analyst may be tasked with making the final decision on how to classify and/or address file210(1) and/or file cluster208.

The systems described herein may perform step308in a variety of ways and/or contexts. In some examples, presentation module108may provide graphical representation214of file cluster208to the security analyst via a monitor and/or display connected to or associated with server206. For example, the security analyst may be operating server206. In this example, presentation module108may direct the monitor and/or display to present graphical presentation214of file cluster208. By directing the monitor and/or display to present graphical presentation214in this way, presentation module108may ensure that the security analyst is able to view and/or review graphical representation214to determine how to classify and/or address file210(1) and/or file cluster208.

In one example, presentation module108may provide graphical representation214of file cluster208to the security analyst via a separate computing device (not necessarily illustrated inFIG. 2) operated by and/or in possession of the security analyst. For example, the security analyst may carry and/or operate a mobile device. In this example, presentation module108may send graphical representation214of file cluster208to the mobile device carried and/or operated by the security analyst. Additionally or alternatively, presentation module108may direct the mobile device to present graphical representation214of file cluster208to the security analyst via a display and/or monitor connected to and/or integrated in the mobile device. By directing the monitor and/or display to present graphical presentation214in this way, presentation module108may ensure that the security analyst is able to view and/or review graphical representation214to determine how to classify and/or address file210(1) and/or file cluster208.

In one example, presentation module108may generate graphical representation214to reflect and/or correspond to the prioritization of file cluster208. For example, presentation module108may create and/or formulate a summary of file cluster208that highlights certain contextually valuable and/or meaningful features of file cluster208. Examples of such features include, without limitation, a file that has a contextually meaningful file type, a file with the highest reputation in the file cluster, a file with the lowest reputation in the file cluster, a file with the most descriptive filename in the file cluster, variations of one or more of the same, combinations of one or more of the same, or any other suitable contextually valuable features. Presentation module108may generate graphical representation214such that graphical representation214portrays the summary that highlights those features of file cluster208.

In some examples, presentation module108may organize graphical representation214of file cluster208in accordance with a set of rules that dictate the prioritization of files included in file clusters. For example, presentation module108may organize graphical representation214of file cluster208as an ordered and/or ranked list that corresponds to the prioritization and/or ranking of files210(1)-(N) relative to file cluster208. In this example, the ordered and/or ranked list may show and/or present a representation of at least some of files210(1)-(N) in order of importance, significance, value, and/or meaningfulness from the perspective of the security analyst.

In some examples, presentation module108may format at least a portion of graphical representation214of file cluster208as a collapsible and/or expandable list. For example, presentation module108may configure and/or design graphical representation214to show and/or display only the summary of file cluster208when a collapsible/expandable portion of graphical representation214is collapsed. However, when the collapsible/expandable portion of graphical representation214is expanded, graphical representation214may show and/or display certain individual files (e.g., contextually meaningful files) included in file cluster208.

In some examples, presentation module108may consolidate and/or combine certain security alerts triggered in connection with files210(1)-(N) into a single security alert. For example, clustering module104may detect various security events that triggered and/or resulted in various security alerts in connection with files210(1)-(N). To reduce redundancy and/or maximize the efficiency of graphical representation214, presentation module108may consolidate and/or combine those security alerts into security alert212rather than incorporating and/or illustrating all of those security alerts in graphical representation214.

As a specific example, presentation module108may generate graphical representation214inFIG. 4and then provide the same for presentation to the security analyst. As illustrated inFIG. 4, graphical representation214may include and/or identify a security event detected in connection with a file of interest (in this example, “Security Alert212raised in connection with the detection of File210(1) within Computing Community216”), the file of interest itself (in this example, “File210(1)”), a file cluster that includes the file of interest (in this example, “File Cluster208”), certain notable files included in the file cluster along with the reputation scores of those notable files (in this example, “SuspiciousProgram.EXE (−100),” “SuspiciousDriver.SYS (−80),” “CleanFile.DLL (+100),” “DirtyFile.DLL (−100),” and “UnknownFile.DLL (0)”), a file path that is common to many, if not all, of the files included in the file cluster (in this example, “C:\ProgramData\DangerousSoftarePackage\”), and a reputation histogram that illustrates the frequency of different reputation scores assigned to files included in the file cluster. Graphical representation214may also include and/or identify various other features of file cluster208that are not explicitly illustrated inFIG. 4.

Returning toFIG. 3, at step310one or more of the systems described herein may perform at least one security action on the suspicious file based at least in part on feedback received from the security analyst in connection with the graphical representation of the file cluster provided for presentation to the security analyst. For example, security module110may, as part of server206and/or one or more of computing devices202(1)-(N) inFIG. 2, perform a least one security action on file210(1) based at least in part on feedback received from the security analyst in connection with graphical representation214of file cluster208.

Examples of such a security action include, without limitation, classifying the suspicious file or the file cluster as malicious, classifying the suspicious file or the file cluster as non-malicious, directing the computing system within the computing community to quarantine the suspicious file or the file cluster, directing at least one additional computing system within the computing community to quarantine the suspicious file or the file cluster, preventing at least one other computing system within the computing community from accessing the suspicious file or the file cluster, variations of one or more of the same, combinations of one or more of the same, or any other suitable security action.

The systems described herein may perform step310in a variety of ways and/or contexts. In one example, security module110may perform the security action on all of file cluster208in response to the feedback received from the security analyst. For example, security module110may receive feedback from the security analyst that indicates file210(1) is likely malicious. Additionally or alternatively, security module110may receive feedback from the security analyst that instructs security module110to classify all of file cluster208as malicious. In either case, security module110may classify all of file cluster208as malicious based at least in part on the feedback received from the security analyst (regardless of whether the feedback is directed to file210(1) alone or all of file cluster208).

In another example, security module110may perform the security action only on the suspicious file and/or the file of interest in response to the feedback received from the security analyst. For example, security module110may receive feedback from the security analyst that indicates file210(1) is likely malicious. Additionally or alternatively, security module110may receive feedback from the security analyst that instructs security module110to classify file210(1) as malicious. In either case, security module110may classify file210(1) as malicious (without necessarily classifying the rest of file cluster208) based at least in part on the feedback received from the security analyst.

In some examples, the systems and methods described herein may prioritize and/or rank certain file clusters over other file clusters. In one example, prioritization module106may prioritize and/or rank file cluster208over at least one other cluster (not illustrated inFIG. 2) based at least in part on a set of rules. For example, a set of rules may dictate that prioritization module106prioritize and/or rank clusters based at least in part on (1) the average suspicion and/or reputation score of the individual files included in the clusters, (2) the minimum suspicion and/or reputation score of any individual file included in the clusters, (3) the average importance of the computing devices that have encountered any files included in the clusters (or on which such files exist), and/or (4) the most important computing device that has encountered any files included in the clusters (or on which such files exist).

In this example, prioritization module106may calculate an overall suspicion score for the clusters using any type or form of algorithm and/or formula. For example, prioritization module106may calculate overall suspicion scores for both file cluster208and the other cluster by applying the above factors to an algorithm and/or formula. In this example, the resulting suspicion score for file cluster208may be higher and/or greater than the resulting suspicion score for the other file cluster, thereby indicating and/or suggesting that file cluster208is more suspicious than the other file cluster. As a result, prioritization module106may prioritize and/or rank file cluster208over the other file cluster.

Since, in this example, prioritization module106has prioritized and/or ranked file cluster208over the other file cluster, presentation module106may provide graphical representation214of file cluster208for presentation to the security analyst prior to providing a graphical representation of the other file cluster to the security analyst. In another example, presentation module106may provide graphical representation214of file cluster208above and/or before a graphical representation of the other file cluster in a display presented to the security analyst.

Additionally or alternatively, presentation module106may generate graphical representation214such that graphical representation214identifies and/or portrays the overall suspicion score of file cluster208. Similarly, presentation module106may generate the graphical representation of the other file cluster such that the graphical representation identifies and/or portrays the overall suspicion score of the other file cluster.

As explained above in connection withFIGS. 1-4, a computer security system may turn to and/or rely at least in part on a human security analyst to make a final decision on how to classify and/or address certain new, unfamiliar, and/or questionable files encountered within a computing community. In one example, the security analyst's ability to successfully classify and/or address a security event and/or file may depend at least in part on the information and/or context surrounding the security event and/or file. To place and/or frame the security event and/or file in the most meaningful context for classification purposes, the computer security system may collect additional information and/or context about the security event and/or file.

This additional information and/or context may include and/or identify whether the file co-exists with other files on a certain amount of computing systems within a computing community, whether the file and the other files are typically created or modified within a certain amount of time of one another on the computing systems within the computing community, whether the file and the other files have similar but unusual filenames, whether the file and the other files are signed by a common entity, whether the file and the other files exhibit common behavioral patterns, whether the file and the other files have at least one library linkage dependency in common, and/or whether the file and the other files represents portions of the same software package.

Upon collecting this additional information and/or context, the computer security system may generate a graphical representation of the security event and/or file in view of this additional information and/or context. In one example, the graphical representation of the security event and/or file may be organized and/or prioritized based on a set of rules intended to maximize the contextual value presented to the security analyst. This set of rules may call for the graphical representation to (1) prioritize .EXE and .SYS files over .DLL files, (2) bias files with either very good or very bad reputations above files whose reputation is uncertain, and (3) highlight files with long descriptive filenames in natural language over short non-descriptive filenames in non-natural language.

As a specific example, a graphical representation of the security event and/or file may display up to 10 closely related files ranked by the above rules. The graphical representation may also include a summary of various high-level statistics about the security event, file, and/or additional information or context. For example, the graphical representation may illustrate (1) a histogram of reputation scores assigned to various files related to the file of interest, (2) a histogram of machine hygiene scores assigned to the computing devices that have encountered any or all of those files, (3) a count of all the different file types of those files, and/or (4) a histogram of the number of files downloaded from each of the top Uniform Resource Locators (URLs) that host the file of interest.

The computer security system may also consolidate and/or group together file-related security alerts to avoid and/or reduce redundancy. In one example, various closely related security alerts may be grouped together as a list at the top of the graphical representation. The computer security system may then present the graphical representation to the security analyst so that the security analyst is able to make a final decision on how to classify and/or address the file and/or the other related files. Additionally or alternatively, the computer security system may perform any appropriate remediation on the file and/or the other related files in connection with the final decision made by the security analyst.

In certain embodiments, exemplary computing system510may also include one or more components or elements in addition to processor514and system memory516. For example, as illustrated inFIG. 5, computing system510may include a memory controller518, an Input/Output (I/O) controller520, and a communication interface522, each of which may be interconnected via a communication infrastructure512. Communication infrastructure512generally represents any type or form of infrastructure capable of facilitating communication between one or more components of a computing device. Examples of communication infrastructure512include, without limitation, a communication bus (such as an Industry Standard Architecture (ISA), Peripheral Component Interconnect (PCI), PCI Express (PCIe), or similar bus) and a network.

In certain embodiments, communication interface522may also represent a host adapter configured to facilitate communication between computing system510and one or more additional network or storage devices via an external bus or communications channel. Examples of host adapters include, without limitation, Small Computer System Interface (SCSI) host adapters, Universal Serial Bus (USB) host adapters, Institute of Electrical and Electronics Engineers (IEEE) 1394 host adapters, Advanced Technology Attachment (ATA), Parallel ATA (PATA), Serial ATA (SATA), and External SATA (eSATA) host adapters, Fibre Channel interface adapters, Ethernet adapters, or the like. Communication interface522may also allow computing system510to engage in distributed or remote computing. For example, communication interface522may receive instructions from a remote device or send instructions to a remote device for execution.

As illustrated inFIG. 5, exemplary computing system510may also include at least one input device528coupled to communication infrastructure512via an input interface530. Input device528generally represents any type or form of input device capable of providing input, either computer or human generated, to exemplary computing system510. Examples of input device528include, without limitation, a keyboard, a pointing device, a speech recognition device, or any other input device.

FIG. 6is a block diagram of an exemplary network architecture600in which client systems610,620, and630and servers640and645may be coupled to a network650. As detailed above, all or a portion of network architecture600may perform and/or be a means for performing, either alone or in combination with other elements, one or more of the steps disclosed herein (such as one or more of the steps illustrated inFIG. 3). All or a portion of network architecture600may also be used to perform and/or be a means for performing other steps and features set forth in the instant disclosure.

Client systems610,620, and630generally represent any type or form of computing device or system, such as exemplary computing system510inFIG. 5. Similarly, servers640and645generally represent computing devices or systems, such as application servers or database servers, configured to provide various database services and/or run certain software applications. Network650generally represents any telecommunication or computer network including, for example, an intranet, a WAN, a LAN, a PAN, or the Internet. In one example, client systems610,620, and/or630and/or servers640and/or645may include all or a portion of system100fromFIG. 1.

As illustrated inFIG. 6, one or more storage devices660(1)-(N) may be directly attached to server640. Similarly, one or more storage devices670(1)-(N) may be directly attached to server645. Storage devices660(1)-(N) and storage devices670(1)-(N) generally represent any type or form of storage device or medium capable of storing data and/or other computer-readable instructions. In certain embodiments, storage devices660(1)-(N) and storage devices670(1)-(N) may represent Network-Attached Storage (NAS) devices configured to communicate with servers640and645using various protocols, such as Network File System (NFS), Server Message Block (SMB), or Common Internet File System (CIFS).

In addition, one or more of the modules described herein may transform data, physical devices, and/or representations of physical devices from one form to another. For example, one or more of the modules recited herein may receive data about files to be transformed into a file cluster, transform the data into the file cluster, output a result of the transformation to a graphical representation of the file cluster, use the result of the transformation to determine whether the files constitute and/or include malware, and store the result of the transformation for future use and/or reference. Additionally or alternatively, one or more of the modules recited herein may transform a processor, volatile memory, non-volatile memory, and/or any other portion of a physical computing device from one form to another by executing on the computing device, storing data on the computing device, and/or otherwise interacting with the computing device.