Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams
Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams
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DOI：
10.3390/electronics12091971
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发表时间：
2023-04
期刊：
影响因子：
2.9
通讯作者：
Mahsa Mozaffari;Keval Doshi;Yasin Yılmaz
中科院分区：
文献类型：
--
作者：
Mahsa Mozaffari;Keval Doshi;Yasin Yılmaz
在本文中，我们解决了实时检测和学习高维数据流异常的问题。在数据驱动的基础上，提出了一种适合于实时、准确检测异常的在线多元异常检测方法。我们提出了半监督和监督设置的方法。通过结合半监督算法和监督算法，我们提出了一种自监督在线学习算法，其中半监督算法训练监督算法以提高其检测性能。从计算复杂度、渐近最优性和虚警率等方面对这些方法进行了综合分析。所提出的算法的性能也使用现实世界的网络安全数据集进行了评估，显示出比最先进的结果有显着改进。
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.