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 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: 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: 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: 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.