Abstract: Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second sub score and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.

Abstract: Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second sub score and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.

Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second subscore and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.

Abstract: An apparatus includes a memory of a mobile compute device, and a hardware processor of the mobile compute device. The hardware processor is configured to implement an operating system and an authentication module. The operating system is configured to receive a first authentication identifier, and is also configured to authorize use of the mobile compute device based on the first authentication identifier meeting a first criterion. The authentication module is configured to, in response to the operating system authorizing use of the mobile compute device, disable at least one function of the mobile compute device and request a second authentication identifier. The authentication module is also configured to receive the second authentication identifier. The authentication module is also configured to enable the at least one function in response to the second authentication identifier meeting a second criterion.

Abstract: A non-transitory processor-readable medium storing code representing instructions to cause a processor to perform a process includes code to cause the processor to receive a set of indications of allowed behavior associated with an application. The processor is also caused to initiate an instance of the application within a sandbox environment. The processor is further caused to receive, from a monitor module associated with the sandbox environment, a set of indications of actual behavior of the instance of the application in response to initiating the instance of the application within the sandbox environment. The processor is also caused to send an indication associated with an anomalous behavior if at least one indication from the set of indications of actual behavior does not correspond to an indication from the set of indications of allowed behavior.

Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second subscore and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.

Abstract: A non-transitory processor-readable medium storing code representing instructions to cause a processor to perform a process includes code to cause the processor to receive a set of indications of allowed behavior associated with an application. The processor is also caused to initiate an instance of the application within a sandbox environment. The processor is further caused to receive, from a monitor module associated with the sandbox environment, a set of indications of actual behavior of the instance of the application in response to initiating the instance of the application within the sandbox environment. The processor is also caused to send an indication associated with an anomalous behavior if at least one indication from the set of indications of actual behavior does not correspond to an indication from the set of indications of allowed behavior.

Abstract: In some embodiments, an apparatus includes a memory and a processor operatively coupled to the memory. The processor is configured to identify a feature vector for a potentially malicious file and provide the feature vector as an input to a trained neural network autoencoder to produce a modified feature vector. The processor is configured to generate an output vector by introducing Gaussian noise into the modified feature vector to ensure a Gaussian distribution for the output vector within a set of modified feature vectors. The processor is configured to provide the output vector as an input to a trained neural network decoder associated with the trained neural network autoencoder to produce an identifier of a class associated with the set of modified feature vectors. The processor is configured to perform a remedial action on the potentially malicious file based on the potentially malicious file being associated with the class.

Abstract: Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second subscore and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.

Abstract: A non-transitory processor-readable medium storing code representing instructions to cause a processor to perform a process includes code to cause the processor to receive a set of indications of allowed behavior associated with an application. The processor is also caused to initiate an instance of the application within a sandbox environment. The processor is further caused to receive, from a monitor module associated with the sandbox environment, a set of indications of actual behavior of the instance of the application in response to initiating the instance of the application within the sandbox environment. The processor is also caused to send an indication associated with an anomalous behavior if at least one indication from the set of indications of actual behavior does not correspond to an indication from the set of indications of allowed behavior.

Abstract: An apparatus includes a memory of a mobile compute device, and a hardware processor of the mobile compute device. The hardware processor is configured to implement an operating system and an authentication module. The operating system is configured to receive a first authentication identifier, and is also configured to authorize use of the mobile compute device based on the first authentication identifier meeting a first criterion. The authentication module is configured to, in response to the operating system authorizing use of the mobile compute device, disable at least one function of the mobile compute device and request a second authentication identifier. The authentication module is also configured to receive the second authentication identifier. The authentication module is also configured to enable the at least one function in response to the second authentication identifier meeting a second criterion.

Abstract: In some embodiments, a method includes receiving a plurality of descriptors via a network. Each descriptor includes at least one of a descriptor component or a keyword. The method further includes storing the plurality of descriptors in a database, and generating a database index of the plurality of descriptors based on at least one of the descriptor component or the keyword for each descriptor of the plurality of descriptors. The method further includes storing the database index in the database. The method further includes receiving a file component extracted from a file and identifying, based on the file component, a set of descriptors from the plurality of descriptors. The method further includes inferring, based on the set of descriptors, a measure of likelihood of a functionality associated with the file, and transmitting an indication of the measure to a user.

Abstract: An apparatus can include a processor that can extract, from an input binary file, an image data structure, and can scale the image data structure to a predetermined size, and/or modify the image data structure to represent a grayscale image. The processor can calculate a modified pixel value for each pixel in the image data structure, and can define a binary vector based on the modified pixel value for each pixel in the image data structure. The processor can also identify a set of nearest neighbor binary vectors for the binary vector based on a comparison between the binary vector and a set of reference binary vectors stored in a malware detection database. The processor can then determine a malware status of the input binary file based on the set of nearest neighbor binary vectors satisfying a similarity criterion associated with a known malware image from a known malware file.

Abstract: In some implementations, a processor can compute a relationship graph to be visually rendered on a display. The processor can define a set of graphical nodes, and can randomly select a first subset of graphical nodes from the set of graphical nodes. The first subset of graphical nodes is less than the set of graphical nodes of the relationship graph. The processor can update a position for each graphical node from the first subset of graphical nodes based on a repulsion value between that graphical node from the first subset of graphical nodes and each graphical node from a second subset of graphical nodes from the set of graphical nodes. The processor can then render each graphical node from the set of graphical nodes based on the updated position for each graphical node from the first subset of graphical nodes.

Abstract: An apparatus includes a database configured to store a collection of files. The apparatus also includes a counter module configured to calculate a frequency of a data feature in the collection of files. The apparatus also includes a signature generation module operatively coupled to the counter module. The signature generation module is configured to generate a malware signature based on the frequency of the data feature in the collection of files. The malware signature includes an indication of one or more criterion for the data feature, and the malware signature is associated with a malware. The apparatus also includes a communication module configured to receive a target file, and a detection module operatively coupled to the communication module. The detection module is configured to classify the target file as the malware when the target file meets the one or more criterion of the malware signature.

Abstract: A non-transitory processor-readable medium storing code representing instructions to cause a processor to perform a process includes code to cause the processor to receive a set of indications of allowed behavior associated with an application. The processor is also caused to initiate an instance of the application within a sandbox environment. The processor is further caused to receive, from a monitor module associated with the sandbox environment, a set of indications of actual behavior of the instance of the application in response to initiating the instance of the application within the sandbox environment. The processor is also caused to send an indication associated with an anomalous behavior if at least one indication from the set of indications of actual behavior does not correspond to an indication from the set of indications of allowed behavior.

Abstract: In some embodiments, a processor can receive an input string associated with a potentially malicious artifact and convert each character in the input string into a vector of values to define a character matrix. The processor can apply a convolution matrix to a first window of the character matrix to define a first subscore, apply the convolution matrix to a second window of the character matrix to define a second subscore and combine the first subscore and the second subscore to define a score for the convolution matrix. The processor can provide the score for the convolution matrix as an input to a machine learning threat model, identify the potentially malicious artifact as malicious based on an output of the machine learning threat model, and perform a remedial action on the potentially malicious artifact based on identifying the potentially malicious artifact as malicious.