Malware

Can Feature Engineering Help Quantum Machine Learning for Malware Detection?

With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML models used in malware classification are supervised solutions. These …

Malware Antivirus Scan Pattern Mining via Tensor Decomposition

Accurate labeling is important for detecting malware and building reference datasets which can be used for evaluating machine learning (ML) based malware classification and clustering approaches. Labels obtained from Anti-Virus (AV) vendors (such as …

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 …