Abstract
The RPL-enabled Internet of Things (IoT) is susceptible to many attacks as these devices are unattended, resource-constrained and connected via an unreliable network. Deploying Intrusion Detection Systems (IDSs) in such a large and resource-constrained environment is a challenging task. The resource-constrained nature of many devices and nodes restricts what tasks those nodes can realistically expect to perform. In this paper, we investigate the use of a meta-heuristic-based optimization method, namely a Genetic Algorithm (GA), to discover optimal IDS placements and configurations for Low Power and Lossy Networks (LLNs). To the best of our knowledge, this is the first attempt to optimise IDS configurations for emerging and constrained networks while incorporating a much wider set of aspects than currently considered. The targets our approach seeks to optimise and balance are the detection rate, F1 score, coverage, feasibility cost and deployment cost. We propose a framework that takes into consideration these functional and non-functional constraints.
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Alshahrani, A., Clark, J.A. (2023). On Optimal Configuration of IDS for RPL Resource-Constrained Networks Using Evolutionary Algorithm. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 2. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 560. Springer, Cham. https://doi.org/10.1007/978-3-031-18458-1_35
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