Sentinel Labs Israel Ltd.

Sentinelone, Inc.

Gal Braun

Shlomi Salem

Eitan Shterenbaum

Pavel Ferencz

Amit Zohar

Roy Ronen

Assaf Nativ

Tal Maimon

Guy Jacoby

Dean Langsam

Ido Kotler

Tai Maimon

Systems, methods, and devices for cybersecurity are disclosed herein that can employ machine learning approaches with a better understanding of the complex relationships and sequencing associated with behavior-based data, and that can effectively apply machine learning for behavior-based analysis, malware detection, and identifying and classifying threats in real-time.

Classifying cybersecurity threats using machine learning on non-euclidean data

Disclosed herein are systems and methods for enabling the automatic detection of executable code from a stream of bytes. In some embodiments, the stream of bytes can be sourced from the hidden areas of files that traditional malware detection solutions ignore. In some embodiments, a machine learning model is trained to detect whether a particular stream of bytes is executable code. Other embodiments described herein disclose systems and methods for automatic feature extraction using a neural network. Given a new file, the systems and methods may preprocess the code to be inputted into a trained neural network. The neural network may be used as a 'feature generator' for a malware detection model. Other embodiments herein are directed to systems and methods for identifying, flagging, and/or detecting threat actors which attempt to obtain access to library functions independently.

Systems and methods for executable code detection, automatic feature extraction and position independent code detection

Growing community of inventors

Ness Ziona, Israel

Gal Braun