General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization
release_uxt4l4g4pzayjhvxqmmgfksfpy
by
Maksim E. Eren, Juston Moore, Erik Skau, Elisabeth Baseman, Manish Bhattarai, Gopinath Chennupati, Boian Alexandrov
2022
Abstract
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. 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.
In application/xml+jats
format
Archived Files and Locations
application/pdf 3.5 MB
file_7c6g3zaehnh7zkybkotfeoyzt4
|
dl.acm.org (publisher) web.archive.org (webarchive) |
10.1145/3519602
access all versions, variants, and formats of this works (eg, pre-prints)
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar