Abstract
We consider the problem of network security under false data injection attacks over wireless sensor networks.To resist the attacks which can inject false data into communication channels according to a certain probability, we formulate the online attack detection problem as a partially observable Markov decision process problem and design a detector for each sensor based on the framework of model-free reinforcement learning. By numerical simulations, we illustrate the effectiveness of the proposed reinforcement learning algorithm and show the performance of the proposed detector compared with the typical detector in the existing works.
Highlights
Wireless sensor network (WSN) is a distributed sensor network whose terminals are sensors that can sense and inspect the outside world
Kurt et al [18] formulated the attack detection problem in the smart grid as a partially observable Markov decision process (POMDP) problem and proposed a detector using the framework of model-free reinforcement learning (RL) for POMDPs
We propose an online cyber-attack detection algorithm using the framework of model-free RL for POMDPs based on WSN
Summary
Wireless sensor network (WSN) is a distributed sensor network whose terminals are sensors that can sense and inspect the outside world. W. Jiang et al.: Reinforcement Learning-Based Detection for State Estimation Under False Data Injection χ 2 detector, the effectiveness of the summation detector is further verified for existing attack and the improved attack. Kurt et al [18] formulated the attack detection problem in the smart grid as a partially observable Markov decision process (POMDP) problem and proposed a detector using the framework of model-free reinforcement learning (RL) for POMDPs. Reinforcement learning method is based on the amplitude of the past innovations to train the detector (the larger norm of innovation, the higher probability of data being attacked), which has nothing to do with the statistical characteristics of innovation, that is, it does not require the gaussianity of innovation. We propose an online cyber-attack detection algorithm using the framework of model-free RL for POMDPs based on WSN. Sensor i sends its estimate xi(k) and innovation i(k) to its out-neighbors
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