malware families

Advanced Semi-supervised Tensor Decomposition Methods for Malware Characterization

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 …

Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection

Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed by prior work, however, are often not practicable due to the lack of …

Malware-DNA: Machine Learning for Malware Analysis that Treats Malware as Mutations in the Software Genome

Malware is one of the most dangerous and costly cyber threats to organizations, the public, and national security, and a crucial factor in modern warfare. The adoption of ML-based solutions against malware threats has been relatively slow despite the …

Malware-DNA: Machine Learning for Malware Analysis that Treats Malwares as Mutations in the Genome of the Software

Malware is one of the most dangerous and costly cyber threats to organizations, the public, and national security, and a crucial factor in modern warfare. The adoption of ML-based solutions against malware threats has been relatively slow despite the …