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arXiv · 2606.03530

Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models

Elias Lunderbye·Sourasekhar Banerjee·Christian Rohner·Andreas Johnsson
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Abstract

AI-based intrusion detection systems (IDS) have shown promise in detecting attacks on IoT systems. In this work, we explore the use of foundation models to detect and identify attacks, with a specific focus on RPL-based IoT networks. We study multiple attack types, attack variations, and network configurations, and provide insights into the performance of foundation models for attack identification. Specifically, we fine-tune the MOMENT foundation model for multi-class attack identification. Our evaluation is based on a dataset containing RPL-related statistics collected under normal operation and under Blackhole, DIS flooding, Worst Parent, and Local Repair attacks, generated in a Cooja simulation environment. The initial results are promising. The approach achieves attack-detection performance comparable to state-of-the-art methods, while also demonstrating strong performance in distinguishing between different attack types.

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Elias Lunderbye, Sourasekhar Banerjee, Christian Rohner, Andreas Johnsson. 2026-06-02. Towards Intrusion Detection Systems for RPL-based IoT Networks using Foundation Models. https://arxiv.org/abs/2606.03530

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