Abstract: An apparatus for detecting malicious files includes a memory and a processor communicatively coupled to the memory. The processor receives multiple potentially malicious files. A first potentially malicious file has a first file format, and a second potentially malicious file has a second file format different than the first file format. The processor extracts a first set of strings from the first potentially malicious file, and extracts a second set of strings from the second potentially malicious file. First and second feature vectors are defined based on lengths of each string from the associated set of strings. The processor provides the first feature vector as an input to a machine learning model to produce a maliciousness classification of the first potentially malicious file, and provides the second feature vector as an input to the machine learning model to produce a maliciousness classification of the second potentially malicious file.

Abstract: In other embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a structured file for which a machine learning model has made a malicious content classification. The code further includes code to remove a portion of the structured file to define a modified structured file that follows a format associated with a type of the structured file. The code further includes code to extract a set of features from the modified structured file. The code further includes code to provide the set of features as an input to the machine learning model to produce an output. The code further includes code to identify an impact of the portion of the structured file on the malicious content classification of the structured file based on the output.

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 method includes processing at least a portion of a received file into a first set of fragments and analyzing each fragment from the first set of fragments using a machine learning model to identify within each fragment first information potentially relevant to whether the file is malicious. The method includes forming a second set of fragments by combining adjacent fragments from the first set of fragments and analyzing each fragment from the second set of fragments using the machine learning model to identify second information potentially relevant to whether the file is malicious. The method includes identifying the file as malicious based on the first information within at least one fragment from the first set of fragments and the second information within at least one fragment from the second set of fragments. The method includes performing a remedial action based on identifying the file as malicious.

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: 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: 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: 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: 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.