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 …
Tensor decomposition is a powerful unsupervised machine learning method used to extract hidden patterns from large datasets. This presentation aims to illuminate the extensive applications and capabilities of tensors within the realm of cybersecurity. We offer a comprehensive overview by encapsulating a diverse array of capabilities, showcasing the cutting-edge employment of tensors in the detection of network and power grid anomalies,identification of SPAM e-mails, mitigation of credit card fraud, and detection of malware. Additionally, we delve into the utility of tensors for classifying malware families, pinpointing novel forms of malware, analyzing user behavior,and utilizing tensors for data privacy through federated learning techniques.
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 …