Joint attack detection and secure state estimation of cyber-physical systems
For researchers and practitioners in cyber-physical systems security, this work provides a novel Bayesian approach to jointly detect attacks and estimate state under realistic attack models, though validation is limited to simulation case studies.
This paper addresses secure state estimation in cyber-physical systems under switching attacks and fake packet injection, proposing a hybrid Bernoulli filter (HBF) within a Bayesian framework. The filter jointly detects attacks and estimates the system state, with effectiveness demonstrated on two case studies including an IEEE power network.
This paper deals with secure state estimation of cyber-physical systems subject to switching (on/off) attack signals and injection of fake packets (via either packet substitution or insertion of extra packets). The random set paradigm is adopted in order to model, via Random Finite Sets (RFSs), the switching nature of both system attacks and the injection of fake measurements. The problem of detecting an attack on the system and jointly estimating its state, possibly in the presence of fake measurements, is then formulated and solved in the Bayesian framework for systems with and without direct feedthrough of the attack input to the output. This leads to the analytical derivation of a hybrid Bernoulli filter (HBF) that updates in real-time the joint posterior density of a Bernoulli attack RFS and of the state vector. A closed-form Gaussian-mixture implementation of the proposed hybrid Bernoulli filter is fully derived in the case of invertible direct feedthrough. Finally, the effectiveness of the developed tools for joint attack detection and secure state estimation is tested on two case-studies concerning a benchmark system for unknown input estimation and a standard IEEE power network application.