General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization

Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from observed user activity. These unsupervised m...

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Bibliographic Details
Published in:Digital threats (Print) 2022-04, Vol.4 (1)
Main Authors: Eren, Maksim Ekin, Moore, Juston Shane, Skau, Erik West, Moore, Elisabeth Ann, Bhattarai, Manish, Chennupati, Gopinath, Alexandrov, Boian S.
Format: Article
Language:English
Subjects:
anomaly detection
CPD
cyber security
data fusion
ensemble learning
GPU
malware
MATHEMATICS AND COMPUTING
non-negative tensor factorization
Poisson tensor factorization
unsupervised learning
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Summary:Distinguishing malicious anomalous activities from unusual but benign activities is a fundamental challenge for cyber defenders. Prior studies have shown that statistical user behavior analysis yields accurate detections by learning behavior profiles from observed user activity. These unsupervised models are able to generalize to unseen types of attacks by detecting deviations from normal behavior, without knowledge of specific attack signatures. However, approaches proposed to date based on probabilistic matrix factorization are limited by the information conveyed in a two-dimensional space. Non-negative tensor factorization, on the other hand, is a powerful unsupervised machine learning method that naturally models multi-dimensional data, capturing complex and multi-faceted details of behavior profiles. Herein, our new unsupervised statistical anomaly detection methodology matches or surpasses state-of-the-art supervised learning baselines across several challenging and diverse cyber application areas, including detection of compromised user credentials, botnets, spam e-mails, and fraudulent credit card transactions.
ISSN:2692-1626
2576-5337