Malware continues to be one of the most dangerous and costly cyber threats to national security. As of last year, over 1.3 billion malware specimens have been documented, prompting the use of data-driven machine learning (ML) techniques for their …
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 …
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 …
Tensor decomposition is a powerful unsupervised Machine Learning method that enables the modeling of multi-dimensional data, including malware data. This thesis introduces a novel ensemble semi-supervised classification algorithm, named Random Forest …
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 …