Abstract: As the ubiquity of IoT devices in smart homes escalates, so does the vulnerability to cyber threats that exploit weaknesses in device security. Timely and accurate detection of attacks is critical to protect smart home networks. Intrusion Detection Systems (IDS) are a cornerstone in any layered security defense strategy. However, building such a system is challenging given smart home devices' resource constraints and behaviors' diversity. This paper presents an adaptative IDS based on a device-specific approach and SDN deployment. We categorize devices based on traffic profiles to enable specialized architectural design and dynamically assign the suitable detection model. We demonstrate the IDS efficiency, effectiveness, and adaptability by thoroughly benchmarking an ensemble of machine learning models, mainly tree ensemble models and extreme learning machine variants, on the up-to-date IoT CICIoT2023 security dataset. Our IDS multi-component device-aware architecture leverages software-defined networking and virtualized network functions for scalable deployment, with an edge computing design to meet strict latency requirements. The results reveal that our adaptive model selection ensures detection accuracy while maintaining low latency, aligning with the critical requirement of real-time accuracy and adaptability to smart home devices' traffic patterns.

Hassen Sallay, “Designing an Adaptive Effective Intrusion Detection System for Smart Home IoT” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150194

@article{Sallay2024,
title = {Designing an Adaptive Effective Intrusion Detection System for Smart Home IoT},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150194},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150194},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {1},
author = {Hassen Sallay}
}