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

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.