QL vs. SARSA: Performance Evaluation for Intrusion Prevention Systems in Software-Defined IoT Networks

• Published in: International Wireless Communications and Mobile Computing Conference (Online) pp. 500 - 504
• Main Authors: Moreira, Christian Miranda, Kaddoum, Georges
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19.06.2023
• Subjects: 6LowPAN; Collaborative Security Framework; Deep learning; Intrusion Prevention; IoT; Performance evaluation; Q-learning; Rank Attack; Routing protocols; Security; Software defined networking; Software-Defined Network; Wireless communication
• ISSN: 2376-6506
• DOI: 10.1109/IWCMC58020.2023.10183144

The resource-constrained IPV6-based low power and lossy network (6LowPAN) is connected through the routing protocol for low power and lossy networks (RPL). This protocol is subject to a routing protocol attack called a rank attack (RA). This paper presents a performance evaluation where leveraging model-free reinforcement-learning (RL) algorithms helps the software-defined network (SDN) controller achieve a cost-efficient solution to prevent the harmful effects of RA. Experimental results demonstrate that the state action reward state action (SARSA) algorithm is more effective than the Q-learning (QL) algorithm, facilitating the implementation of intrusion prevention systems (IPSs) in software-defined 6LowPANs.