Capital One Services, LLC

Boshika Tara

Sarvani Kare

Patrick Sofo

Stephen Fletcher

Farshid Marbouti

Vannia Gonzalez Macias

Urvish Patel

Methods and systems are described herein for detecting anomalous access to system resources. An anomaly detection system may access system events from one or more computing devices and may generate entries from the system events. Each entry may include a corresponding timestamp indicating a time when a corresponding system event occurred, a corresponding user identifier indicating a user account within a computing environment associated with the corresponding system event, a corresponding location identifier indicating a location within the computing environment, and a corresponding action identifier indicating an action that the user account performed with respect to the location or an object within the computing environment. The generated entries may be aggregated and input into an anomaly detection model to obtain anomalous activity identified by the model.

Anomaly detection in computing system events

In some implementations, a system may receive a shell script associated with a computing device. The system may generate a character frequency feature vector based on the shell script. The system may input text of the shell script to a convolutional neural network (CNN) branch of a trained deep learning model. The system may input the character frequency feature vector to a feedforward neural network (FNN) branch of the trained deep learning model. The system may determine using the trained deep learning model, respective probability scores for each of a plurality of obfuscation types for the shell script based on a combined output of the CNN branch and the FNN branch. The system may detect whether the shell script is obfuscated based on respective probability scores for each of the plurality of obfuscation types determined for the shell script.

Deep learning based detection of malicious shell scripts

In some implementations, a system may receive a shell script associated with a computing device. The system may generate a character frequency feature vector based on the shell script. The system may input text of the shell script to a convolutional neural network (CNN) branch of a trained deep learning model. The system may input the character frequency feature vector to a feedforward neural network (FNN) branch of the trained deep learning model. The system may determine using the trained deep learning model, a respective probability score for each of a plurality of obfuscation types for the shell script based on a combined output of the CNN branch and the FNN branch. The system may detect whether the shell script is obfuscated based on the respective probability score for each of the plurality of obfuscation types determined for the shell script.

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Pleasanton, CA, United States of America

Boshika Tara