Name
Title | ||
|---|---|---|
Deep Reinforecement Learning Based Optimal Defense For Cyber-Physical System In Presence Of Unknown Cyber-Attack |
Abstract | ||
|---|---|---|
In this paper, the online optimal cyber-defense problem has been investigated for Cyber-Physical Systems (CPS) with unknown cyber-attacks. Firstly, a novel cyber state dynamics has been generated that can evaluate the real-time impacts from current cyber-attack and defense strategies effectively and dynamically. Next, adopting game theory technique, the idea optimal defense design can be obtained by using the full knowledge of cyber-state dynamics. To relax the requirement about cyberstate dynamics, a game-theoretical actor-critic neural network (NN) structure was developed to efficiently learn the optimal cyber defense strategy online. Moreover, to further improve the practicality of developed scheme, a novel deep reinforcement learning algorithm have been designed and implemented into actor-critic NN structure. Eventually, the numerical simulation demonstrate that proposed deep reinforcement learning based optimal defense strategy cannot only online defend the CPS even in presence of unknown cyber-attacks, and also learn the optimal defense policy more accurate and timely. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | Cyber-physical systems, cyber state dynamics, cyber-attack, deep reinforcement learning, game theory |
Field | DocType | Citations |
Cyber-attack,Computer simulation,Computer science,Cyber-physical system,Artificial intelligence,Game theory,Cyber defense,Reinforcement learning algorithm,Artificial neural network,Reinforcement learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |