Juniper Networks, Inc.

Juniper Neworks, Inc.

Jacob Asher Langton

Daniel J Quinlan

Kyle Adams

Zhenxin Zhan

Declan Conlon

A device may receive an instruction to classify software. The device may identify a group of one or more user interfaces associated with the software based on receiving the instruction to classify the software. The device may determine a group of one or more user interface signatures associated with the group of one or more user interfaces. A user interface signature may include information, associated with a user interface in the group of one or more user interfaces, that may be used to classify the software. The device may generate information that identifies a classification of the software based on the group of one or more user interface signatures and based on known signature information. The known signature information may include information that corresponds to a correct software classification. The device may output the information that identifies the classification of the software.

Classification of software based on user interface elements

A device may identify a plurality of files for a multi-file malware analysis. The device may execute the plurality of files in a malware testing environment. The device may monitor the malware testing environment for behavior indicative of malware. The device may detect the behavior indicative of malware. The device may perform a first multi-file malware analysis or a second multi-file malware analysis based on detecting the behavior indicative of malware. The first multi-file malware analysis may include a partitioning technique that partitions the plurality of files into two or more segments of files to identify a file, included in the plurality of files, that includes malware. The second multi-file malware analysis may include a scoring technique that modifies a plurality of malware scores, corresponding to the plurality of files, to identify the file, included in the plurality of files, that includes malware.

Multi-file malware analysis

A device may receive an instruction to automatically install a program using a click area prediction model. The click area prediction model may be associated with predicting a click area of a user interface that, when selected, causes a program installation procedure to proceed. The device may identify an installation user interface associated with installing the program. The device may determine a group of regions included in the installation user interface. The device may identify sets of features associated with the group of regions. The device may determine, based on the sets of features and the click area prediction model, a group of scores associated with the group of regions. The device may identify a particular region as a predicted click area based on the group of scores. The device may select the predicted click area to attempt to cause the program installation procedure to proceed.

Automated software installation using a click area prediction model

A device may determine that a file of a client device is a malicious file. The device may obtain remote access to the client device using a connection tool. The connection tool may provide access and control of the client device. The remote access may include access to a file location of the malicious file. The device may determine file information associated with the malicious file using the remote access to the client device. The device may select one or more remediation actions based on the file information. The device may cause the one or more remediation actions to be executed using the remote access to the client device.

Remote remediation of malicious files

A device may identify a set of features associated with the unknown object. The device may determine, based on inputting the set of features into a threat prediction model associated with a set of security functions, a set of predicted threat scores. The device may determine, based on the set of predicted threat scores, a set of predicted utility values. The device may determine a set of costs corresponding to the set of security functions. The device may determine a set of predicted efficiencies, associated with the set of security functions, based on the set of predicted utility values and the set of costs. The device may identify, based on the set of predicted efficiencies, a particular security function, and may cause the particular security function to be executed on the unknown object. The device may determine whether another security function is to be executed on the unknown object.

Dynamically optimizing performance of a security appliance

A device may receive a trigger to determine whether a malicious file is operating on a client device. The device may determine a network activity profile associated with the malicious file based on receiving the trigger to determine whether the malicious file is operating on the client device. The network activity profile may include information regarding network activity associated with the malicious file when the malicious file is executed in a testing environment. The device may monitor network activity associated with the client device. The device may determine that the network activity associated with the client device matches the network activity profile associated with the malicious file based on monitoring the network activity associated with the client device. The device may provide information indicating that the network activity associated with the client device matches the network activity profile associated with the malicious file.

Detecting a malicious file infection via sandboxing

A device may receive a file to be analyzed in a sandbox environment, and may determine configuration information for configuring the sandbox environment. The configuration information may be determined based on at least one of: file information associated with the file to be analyzed, or client device information associated with a client device for which the file is intended. The device may configure the sandbox environment using the configuration information. The configuration information may identify a system configuration for the sandbox environment. The device may analyze the file in the sandbox environment based on configuring the sandbox environment using the configuration information.

Configuring a sandbox environment for malware testing

A device may receive or generate a message for routing to a destination on a communication channel. The communication channel may have been established between a source and the destination. The device may perform a first determination of policy information related to at least one of the message, the destination, or the source of the message. The policy information may describe an action for a network device to perform. The device may associate a policy token with the message. The policy token may describe or identify the policy information. The device may provide the message with the associated policy token to the network device on the communication channel to cause the network device to perform a second determination of the policy information based on the policy token, to perform the action described by the policy information, and to provide the message on the communication channel.

Providing policy information on an existing communication channel

A device may receive an object. The device may determine object information for the object. The device may cause an internet search, based on the object information, to be performed to determine Internet search results. The object information may be provided as one or more Internet search queries for the Internet search. The device may receive the Internet search results based on causing the Internet search to be performed. The Internet search results may be related to the object information. The device may analyze the Internet search results to determine Internet-based object information. The device may store or provide the Internet-based object information to permit a determination as to whether the object is malicious.

Determining internet-based object information using public internet search

A device may identify exfiltration information to be used to detect data exfiltration. The exfiltration information may be associated with a file being tested to determine whether the file exfiltrates data. The exfiltration information may include a resource identifier that identifies a resource to be used to detect the data exfiltration. The device may determine that the resource, to be used to detect the data exfiltration, has been accessed. The device may identify, based on determining that the resource has been accessed, the file associated with the exfiltration information. The device may perform an action, associated with the file, to counteract the data exfiltration based on determining that the resource has been accessed and based on identifying the file.

Detecting data exfiltration as the data exfiltration occurs or after the data exfiltration occurs

A device may detect or emulate a sequence of keystrokes to be used to detect a keystroke logger application. The device may determine a sequence of characters associated with the sequence of keystrokes. The sequence of characters may correspond to the sequence of keystrokes or a portion of the sequence of keystrokes. The device may search a memory for the sequence of characters. The device may determine that the sequence of characters is stored in the memory based on searching the memory for the sequence of characters. The device may perform an action to counteract the keystroke logger application based on determining that the sequence of characters is stored in the memory.

Detecting keylogging

A device may determine a first set of hash values corresponding to a first set of files stored by a plurality of client devices. The device may analyze information associated with the first set of hash values to determine a second set of hash values corresponding to a second set of files to be analyzed. The second set of hash values may be different from the first set of hash values. The device may prioritize the second set of hash values to form a prioritized set of hash values corresponding to a prioritized set of files, of the second set of files, to be analyzed. The device may request the prioritized set of files from one or more client devices of the plurality of client devices. The device may receive the prioritized set of files, and may cause the prioritized set of files to be analyzed.

Detecting suspicious files resident on a network

A device may receive a password-protected file to be accessed for analysis. The device may identify a contextual term, associated with the password-protected file, to be used as a password to attempt to access the password-protected file. The contextual term may be identified based on at least one of: metadata associated with the password-protected file, metadata associated with a source from which the password-protected file is received, or text associated with the source from which the password-protected file is received. The device may apply the contextual term as the password to attempt to access the password-protected file.

Analyzing a password-protected file for malware

A device may analyze a first file for malware. The device may determine that the first file causes a second file to be downloaded. The device may store linkage information that identifies a relationship between the first file and the second file based on determining that the first file causes the second file to be downloaded. The device may analyze the second file for malware. The device may determine a first malware score for the first file based on analyzing the second file for malware and based on the linkage information. The device may determine a second malware score for the second file based on analyzing the first file for malware and based on the linkage information.

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Oakland, CA, United States of America

Jacob Asher Langton