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 m...

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Bibliographic Details
Published inInternational Wireless Communications and Mobile Computing Conference (Online) pp. 500 - 504
Main Authors Moreira, Christian Miranda, Kaddoum, Georges
Format Conference Proceeding
LanguageEnglish
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
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Summary: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.
ISSN:2376-6506
DOI:10.1109/IWCMC58020.2023.10183144