Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams

• Main Authors: Doshi, K. (Author), Mozaffari, M. (Author), Yilmaz, Y. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: anomaly detection; change detection; online learning; self-supervised learning; sequential analysis
• Online Access: View Fulltext in Publisher; View in Scopus

 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. © 2023 by the authors.