Malware

General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles …

Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery

The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machine Learning methods to discover meaningful distinctions between …

Random Forest of Tensors (RFoT)

Machine learning has become an invaluable tool in the fight against malware. Traditional supervised and unsupervised methods are not designed to capture the multi-dimensional details that are often present in cyber data. In contrast, tensor …