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.
Supervisory Control and Data Acquisition (SCADA) systems often serve as the nervous system for substations within power grids. These systems facilitate real-time monitoring, data acquisition, control of equipment, and ensure smooth and efficient …
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 unprecedented outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, continues to be a significant worldwide problem. As a result, a surge of new COVID-19 related research has followed suit. The growing number of …