Abstract: 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.

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 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.

Abstract: A client device may provide, to a host device, a request to access a website associated with a host domain. The client device may receive, based on the request, verification code that identifies a verification domain and a resource, associated with the verification domain, to be requested to verify a public key certificate. The verification domain may be different from the host domain. The client device may execute the verification code, and may request the resource from the verification domain based on executing the verification code. The client device may determine whether the requested resource was received, and may selectively perform a first action or a second action based on determining whether the requested resource was received. The first action may indicate that the public key certificate is not valid, and the second action may indicate that the public key certificate is valid.

Abstract: Embodiments of the present disclosure are directed to extracting the liquid from a container (such as a bottle of wine) in varying amounts at a desired serving temperature while preserving the liquid for later consumption. Various embodiments may be built within an appliance so the consumer experience is fully automated. Embodiments may include a variety of wine preservation techniques including the use of inert gases that prevent the oxidation of wine.

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 trigger to determine whether one or more client devices, of a set of client devices, are infected by a malicious file. The device may generate file identification information associated with the malicious file based on receiving the trigger to determine whether the one or more client devices are infected by the malicious file. The device may obtain remote access to the one or more client devices using a connection tool based on receiving the trigger to determine whether the one or more client devices are infected by the malicious file. The device may obtain information, associated with the one or more client devices, using the remote access. The device may provide information indicating whether the one or more client devices are infected by the malicious file based on the file identification information and the information associated with the one or more client devices.

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: 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.

Abstract: Systems and methods that utilize local storage associated with web browsers to store image data as an alphanumeric string. An expiration date may be associated with the image data such that it expires either after predetermined period of time or at a certain date. By storing image data in local storage, as a user navigates to a website, images are loaded from local storage and displayed in the web browser rather than fetching the image data from a remote source.

Abstract: A device may detect a suspicious activity. The device may automatically obtain a suspect object from a client device that is associated with the suspicious activity and based on detecting the suspicious activity. The suspect object may be an object that is possibly associated with the suspicious activity. The device may determine that the suspect object is malicious. The device may perform an action based on determining that the suspect object is malicious.

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 method, computer readable medium, and system for automatically determining resource dependency includes automatically identifying with an application processing device one or more dependencies between two or more modules in an application. The application processing device determines one or more ordered lists of executing the modules based on the identified one or more dependencies. The application processing device provides the determined one or more ordered lists of executing the modules in the application.

Abstract: A device may receive a trigger to determine whether one or more client devices, of a set of client devices, are infected by a malicious file. The device may generate file identification information associated with the malicious file based on receiving the trigger to determine whether the one or more client devices are infected by the malicious file. The device may obtain remote access to the one or more client devices using a connection tool based on receiving the trigger to determine whether the one or more client devices are infected by the malicious file. The device may obtain information, associated with the one or more client devices, using the remote access. The device may provide information indicating whether the one or more client devices are infected by the malicious file based on the file identification information and the information associated with the one or more client devices.

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 an indication of whether the object is an evasive malicious object.

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: 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 receive usage information, associated with a group of client networks, including particular usage information associated with a particular client network. The device may receive threat information, associated with the group of client networks, including particular threat information associated with the particular client network. The device may determine a baseline based on the usage information. The device may determine a normalization function, associated with the particular client network, based on the baseline and the particular usage information. The device may determine normalized threat information, associated with the particular client network, based on the normalization function and the particular threat information. The device may determine overall normalized threat information associated with the group of client networks. The device may compare the normalized threat information and the overall normalized threat information.

Abstract: 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.