Context-aware Adaptive Route Mutation Scheme: A Reinforcement Learning Approach
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Internet of Things Journal, 2021, PP, (99), pp. 1-1
- Issue Date:
- 2021-01-01
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
Moving Target Defense (MTD) is an emerging proactive defense technology, which can reduce the risk of vulnerabilities exploited by attacker. As a crucial component of MTD, route mutation (RM) faces a few fundamental problems defending against sophisticated Distributed Denial of Service (DDoS) attacks: 1) It’s unable to make optimal mutation selection due to insufficient learning in attack behaviors. 2) Because network situation is time-varying, RM also lacks self-adaptation in mutation parameters. In this paper, we propose a context-aware Q-learning algorithm for RM (CQ-RM) that can learn attack strategies to optimize the selection of mutated routes. We firstly integrate four representative attack strategies into a unified mathematical model and formalize multiple network constraints. Then, taking above network constraints into considerations, we model RM process as a Markov decision process (MDP). To look for the optimal policy of MDP, we develop a context estimation mechanism and further propose the CQ-RM scheme, which can adjust learning rate and mutation period adaptively. Correspondingly, the optimal convergence of CQ-RM is proved theoretically. Finally, extensive experimental results highlight the effectiveness of our method compared to representative solutions.
Please use this identifier to cite or link to this item: