Name
Abstract | ||
|---|---|---|
We consider an optimal denial-of-service (DoS) attack scheduling problem of N independent linear time-invariant processes, where sensors have limited computational capability. Sensors transmit measurements to the remote estimator via a communication channel that is exposed to DoS attackers. However, due to limited energy, an attacker can only attack a subset of sensors at each time step. To maximally degrade the estimation performance, a DoS attacker needs to determine which sensors to attack at each time step. In this context, a deep reinforcement learning (DRL) algorithm, which combines Q-learning with a deep neural network, is introduced to solve the Markov decision process (MDP). The DoS attack scheduling optimization problem is formulated as an MDP that is solved by the DRL algorithm. A numerical example is provided to illustrate the efficiency of the optimal DoS attack scheduling scheme using the DRL algorithm. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11432-020-3027-0 | Science China Information Sciences |
Keywords | DocType | Volume |
optimal denial-of-service attack, scheduling, optimization, limited energy, deep reinforcement learning | Journal | 65 |
Issue | ISSN | Citations |
6 | 1674-733X | 1 |
PageRank | References | Authors |
0.35 | 28 | 4 |