Abnormal Detection Of Wireless Power Terminals In Untrusted Environment Based On Double Hidden Markov Model

The wireless power terminals are deployed in harsh public places and lack strict control, facing security problems. Thus, they are faced with security problems such as illegal and counterfeit terminal access, unlawful control of connected terminals, etc. The intrusion detection system based on machine learning and artificial intelligence significantly improve the terminal side's abnormal detection capacity. In this article, we aim at identifying the abnormal behavior of wireless power terminals based on a double Hidden Markov Model (HMM), which solves the computational complexity problem caused by high dimensions in intrusion detection systems using a single HMM. The lower-layer HMM is used to identify the discrete single network abnormal behavior. Simultaneously, the upper-layer can obtain more extended period attack behavior in multiple independent abnormal events identified by the low-level. The experiment results indicate that the intrusion detection system using proposed double HMM can effectively detect the terminal's abnormal behavior and identify the network attack behavior for an extended period.