An on-line anomaly detection method based on LMS algorithm

Ziyu Wang, Jiahai Yang, Fuliang Li
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引用次数: 4

Abstract

Anomaly detection has been a hot topic in recent years due to its capability of detecting zero attacks. In this paper, we propose a new on-line anomaly detection method based on LMS algorithm. The basic idea of the LMS-based detector is to predict IGTE using IGFE, given the high linear correlation between them. Using the artificial synthetic data, it is shown that the LMS-based detector possesses strong detection capability, and its false positive rate is within acceptable scope.
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基于LMS算法的在线异常检测方法
异常检测由于具有检测零攻击的能力而成为近年来研究的热点。本文提出了一种基于LMS算法的在线异常检测方法。基于lms的探测器的基本思想是利用IGFE预测IGTE,因为它们之间具有高度的线性相关性。利用人工合成数据表明,基于lms的检测器具有较强的检测能力,其假阳性率在可接受范围内。
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