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Title: Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams
In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose our method for both semi-supervised and supervised settings. By combining the semi-supervised and supervised algorithms, we present a self-supervised online learning algorithm in which the semi-supervised algorithm trains the supervised algorithm to improve its detection performance over time. The methods are comprehensively analyzed in terms of computational complexity, asymptotic optimality, and false alarm rate. The performances of the proposed algorithms are also evaluated using real-world cybersecurity datasets, that show a significant improvement over the state-of-the-art results.  more » « less
Award ID(s):
2040572
PAR ID:
10418523
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Electronics
Volume:
12
Issue:
9
ISSN:
2079-9292
Page Range / eLocation ID:
1971
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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