ML-Driven Attack Detection in RPL Networks: Exploring Attacker Position's Significance

Ghaleb, Baraq; Al-Dubai, Ahmed; Romdhani, Imed; Ahmad, Jawad; Aldhaheri, Talal; Kulkarni, Sonali

Abstract

The Routing Protocol for Low Power and Lossy Networks (RPL) plays a pivotal role in IoT communication, employing a rank-based topology to guide routing decisions. However, RPL is vulnerable to Decreased Rank Attacks, where malicious nodes illegitimately lower their ranks to manipulate routing paths. While exploring the applicability of machine learning (ML) techniques for attack detection holds promise, their effectiveness is often overlooked in the context of attacker position within the network. This study bridges this gap and delve into investigating the impact of attacker position on Decreased Rank attack detection using ML-based approaches. Our findings reveal that the success of attack detection is highly contingent on the attacker's proximity to the network root, highlighting the importance of considering network topology in attack mitigation strategies.

Citation

Ghaleb, B., Al-Dubai, A., Romdhani, I., Ahmad, J., Aldhaheri, T., & Kulkarni, S. (2024, January). ML-Driven Attack Detection in RPL Networks: Exploring Attacker Position's Significance. Presented at 2024 International Conference on Information Networking (ICOIN), Ho Chi Minh City, Vietnam

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 International Conference on Information Networking (ICOIN)
• Start Date: Jan 17, 2024
• End Date: Jan 19, 2024
• Acceptance Date: Dec 8, 2023
• Online Publication Date: Jul 3, 2024
• Publication Date: 2024
• Deposit Date: Jul 21, 2024
• Publicly Available Date: Jul 22, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Pages: 478-483
• Series ISSN: 1976-7684
• Book Title: 2024 International Conference on Information Networking (ICOIN)
• ISBN: 9798350330953
• DOI: https://doi.org/10.1109/icoin59985.2024.10572153