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Robust algorithm for attack detection based on time‐varying hidden Markov model subject to outliers
Author(s) -
Lu Genghong,
Feng Dongqin,
Huang Biao
Publication year - 2020
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3163
Subject(s) - hidden markov model , outlier , expectation–maximization algorithm , computer science , algorithm , mixture model , hidden semi markov model , maximization , gaussian , markov chain , artificial intelligence , pattern recognition (psychology) , markov model , maximum likelihood , machine learning , variable order markov model , mathematics , statistics , mathematical optimization , physics , quantum mechanics
Summary The problem of robust attack detection and prediction for networked control systems in the presence of outliers is discussed in this article. The conventional hidden Markov model (HMM) is trained to learn the system behavior (ie, transitions between different operating modes) in the nominal process. The HMM with time‐varying transition probabilities is used to track the attack behavior in which the adversary triggers more hazard modes to hasten fatigue of control devices by injecting attack signals with random magnitude and frequency. For different operating modes, the observations are assumed to follow different multivariate Student's t distributions instead of Gaussian distributions and thus address the robust estimation problem. The expectation maximization algorithm is used to estimate parameters. Finally, simulations are conducted to verify the effectiveness of the proposed method.