Tensor decomposition is a powerful unsupervised machine learning technique capable of modeling multidimensional data, including that related to malware. This chapter discusses a method that employs tensor decomposition for malware analysis. We …
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.
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 …