A trust-based intrusion detection system for mobile RPL based networks

Medjek, Faiza; Tandjaoui, Djamel; Romdhani, Imed; Djedjig, Nabil

Abstract

Successful deployment of Low power and Lossy Networks (LLNs) requires self-organising, self-configuring, security, and mobility support. However, these characteristics can be exploited to perform security attacks against the Routing Protocol for Low-Power and Lossy Networks (RPL). In this paper, we address the lack of strong identity and security mechanisms in RPL. We first demonstrate by simulation the impact of Sybil-Mobile attack, namely SybM, on RPL with respect to control overhead, packet delivery and energy consumption. Then, we introduce a new Intrusion Detection System (IDS) scheme for RPL, named Trust-based IDS (T-IDS). T-IDS is a distributed, cooperative and hierarchical trust-based IDS, which can detect novel intrusions by comparing network behavior deviations. In T-IDS, each node is considered as monitoring node and collaborates with his peers to detect intrusions and report them to a 6LoWPAN Border Router (6BR). In our solution, we introduced a new timer and minor extensions to RPL messages format to deal with mobility, identity and multicast issues. In addition, each node is equipped with a Trusted Platform Module co-processor to handle identification and off-load security related computation and storage.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017
• Start Date: Jun 21, 2017
• End Date: Jun 23, 2017
• Online Publication Date: Feb 1, 2018
• Publication Date: Feb 1, 2018
• Deposit Date: Apr 25, 2024
• Pages: 735-742
• Book Title: 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
• ISBN: 9781538630655
• DOI: https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2017.113
• Public URL: https://uwe-repository.worktribe.com/output/11912671