Harvest and Jam: Optimal Self-Sustainable Jamming Attacks against Remote State Estimation
For researchers in cyber-physical systems security, this provides a theoretical framework and algorithms for optimal jamming with energy harvesting, though the results are incremental over existing MDP-based attack strategies.
This paper addresses optimal power allocation for a self-sustainable jamming attacker against remote state estimation, aiming to maximize estimation error. It formulates the problem as an MDP, proves existence of optimal deterministic stationary policies, and develops converging algorithms for both perfect and unknown channel knowledge cases.
This paper considers the optimal power allocation of a jamming attacker against remote state estimation. The attacker is self-sustainable and can harvest energy from the environment to launch attacks. The objective is to carefully allocate its attack power to maximize the estimation error at the fusion center. Regarding the attacker's knowledge of the system, two cases are discussed: (i) perfect channel knowledge and (ii) unknown channel model. For both cases, we formulate the problem as a Markov decision process (MDP) and prove the existence of an optimal deterministic and stationary policy. Moreover, for both cases, we develop algorithms to compute the allocation policy and demonstrate that the proposed algorithms for both cases converge to the optimal policy as time goes to infinity. Additionally, the optimal policy exhibits certain structural properties that can be leveraged to accelerate both algorithms. Numerical examples are given to illustrate the main results.