Task offloading of cooperative intrusion detection system based on Deep Q Network in mobile edge computing

• Published in: Expert systems with applications 2022-11, Vol.206, p.117860, Article 117860
• Main Authors: Zhao, Xu, Huang, Guangqiu, Jiang, Jin, Gao, Lin, Li, Maozhen
• Format: Article
• Language: English
• Subjects: Deep Q Network; Edge computing; Intrusion detection; Reinforcement learning; Task offloading
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.117860

•A CIDS architecture applied to mobile edge computing is proposed.•A task offloading scheduling algorithm based on DQN is proposed.•The task scheduling process is modeled by Markov decision process.•The loss function and objective function are established based on DQN.•Memory playback is introduced to solve the dimensional disasters in Q-learning. Due to the performance and resource limitations of wireless devices at the edge of the network, the intrusion detection system deployed on the mobile edge network will cause severe packet loss when faced with large traffic. Based on this, a collaborative intrusion detection system (CIDS) architecture applied to mobile edge computing is proposed, which can offload part of the detection tasks to an intrusion detection system with better performance and resources on the edge server. On this basis, a task offloading scheduling algorithm based on Deep Q Network (DQN) is proposed. First, the time delay, energy consumption, and offloading decision models are established. Then, the task scheduling process is described as a Markov decision process and the relevant space and value function are established. Finally, the problem of excessive state and action space in Q-learning is solved by the Deep Q Network. Experiments have shown that our proposed scheme enables the system to have advantages over the comparative algorithms in terms of response time, energy consumption, and packet loss rate, etc..