Ca, Inc.

Keith Kenemer

Ryan Curtin

The disclosed computer-implemented method for executing decision trees may include (i) executing a security classification decision tree that classifies an input data item, (ii) gathering, simultaneously using a gather instruction, values for both a current threshold at a parent node of the security classification decision tree and a subsequent threshold at a child node of the parent node, (iii) gathering, simultaneously using the gather instruction, values for both a current measurement at the parent node and a subsequent measurement at the child node, (iv) comparing, simultaneously using a comparison instruction, the current threshold at the parent node with the current measurement at the parent node and the subsequent threshold at the child node with the subsequent measurement at the child node, and (v) performing a security action to protect the computing device. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for executing decision trees

Methods and systems are provided for detecting malware. One example method generally includes receiving a reference dataset comprising an aggregation of probability distributions of a plurality of intra-file patterns for a plurality of files of at least a first class and applying a logical query to the reference dataset to generate a template distribution with probability distributions of the plurality of intra-file patterns calculated according to one or more logical operators in the logical query. The method further includes detecting a likely presence of malware in a computer file by indicating one or more areas in the computer file based on at least a portion of the calculated probability distributions of the plurality of intra-file patterns in the template distribution.

Automated scoring of intra-sample sections for malware detection

The disclosed computer-implemented method for improving performance of cascade classifiers for protecting against computer malware may include receiving a training dataset usable to train a cascade classifier of a machine-learning classification system. A sample to add to the training dataset may be received. A weight for the sample may be calculated. The training dataset may be modified using the sample and the weight. A weighted training for the cascade classifier of the machine-learning classification system may be performed using the modified training dataset. Computer malware may be identified using the cascade classifier. In response to identifying the computer malware, a security action may be performed to protect the one or more computing devices from the computer malware. Various other methods, systems, and computer-readable media are also disclosed.

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Roswell, GA, United States of America

Keith Kenemer