HMM-Based Fast Detection of False Data Injections in Advanced Metering Infrastructure

Smart grids not only provide "intelligence" to the next generation power systems, but also potentially introduce vital security and privacy issues. Particularly, as a core part of the smart grids, advanced metering infrastructure (AMI) is suffering widespread disputes in terms of security and privacy concerns. This paper proposes a novel hidden Markov model (HMM) based method to detect false data injection attacks in AMI. In this method, a global-state HMM of the whole-house appliances is built and trained by sufficient historical meter data in an offline mode. Then, a new fast Viterbi algorithm is devised to decode the hidden states of the HMM. The decoded states are then verified via the partial sub-meter data in an online mode, by which false data can be detected. The effectiveness and efficiency of our method are verified by a public dataset AMPds with one- year real-time meter data.