Abstract:
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.
Abstract:
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.
Abstract:
A security platform may determine mapped attribute information associated with a plurality of host identifiers. The mapped attribute information may include information that identifies a set of related attributes. The security platform may determine, based on the mapped attribute information, that a host device is associated with at least two host identifiers of the plurality of host identifiers. The security platform may aggregate, based on the at two least host identifiers, threat information as aggregated threat information associated with the host device. The security platform may classify the host device as an infected device or a suspicious device based on the aggregated threat information.
Abstract:
A device may detect an attack. The device may receive, from a client device, a request for a resource. The device may determine, based on detecting the attack, a computationally expensive problem to be provided to the client device, where the computationally expensive problem requires a computation by the client device to solve the computationally expensive problem. The device may instruct the client device to provide a solution to the computationally expensive problem. The device may receive, from the client device, the solution to the computationally expensive problem. The device may selectively provide the client device with access to the resource based on the solution.
Abstract:
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.
Abstract:
A security device may receive actual behavior information associated with an object. The actual behavior information may identify a first set of behaviors associated with executing the object in a live environment. The security device may determine test behavior information associated with the object. The test behavior information may identify a second set of behaviors associated with testing the object in a test environment. The security device may compare the first set of behaviors and the second set of behaviors to determine a difference between the first set of behaviors and the second set of behaviors. The security device may identify whether the object is an evasive malicious object based on the difference between the first set of behaviors and the second set of behaviors. The security device may provide an indication of whether the object is an evasive malicious object.
Abstract:
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.
Abstract:
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.
Abstract:
This disclosure describes techniques for proactively identifying possible attackers based on a profile of a device. For example, a device includes one or more processors and network interface cards to receive, from a remote device, network traffic directed to one or more computing devices protected by the device, determine, based on content of the network traffic, a first set of data points for the device, send a response to the remote device to ascertain a second set of data points for the device, and receive, from the remote device, at least a portion of the second set of data points. The device also includes a security module operable by the processors to determine a maliciousness rating, and selectively manage, based on the maliciousness rating, additional network traffic directed to the one or more computing devices protected by the security device and received from the remote device.
Abstract:
This disclosure describes a global attacker database that utilizes device fingerprinting to uniquely identify devices. For example, a device includes one or more processors and network interface cards to receive network traffic directed to one or more computing devices protected by the device, send, to the remote device, a request for data points of the remote device, wherein the data points include characteristics associated with the remote device, and receive at least a portion of the requested data points. The device also includes a fingerprint module to compare the received portion of the data points to sets of data points associated with known attacker devices, and determine, based on the comparison, whether a first set of data points of a first known attacker device satisfies a similarity threshold. The device also includes an security module to selectively manage, based on the determination, additional network traffic directed to the computing devices.