The application of many IoT devices is making our world more convenient and efficient. However, it also makes a large number of cyber-attacks possible because most IoT devices have very limited resources and cannot perform ordinary intrusion detection systems. How to implement efficient and lightweight IDS in IoT environments is a critically important and challenging task. Several detection systems have been implemented on Raspberry Pi, but most of them are signature-based and only allow limited rules. In this study, a lightweight IDS based on machine learning is implemented on a Raspberry Pi. To make the system lightweight, a correlation-based feature selection algorithm is applied to significantly reduce the number of features and a lightweight classifier is utilized. The performance of our system is examined in detail and the experimental result indicates that our system is lightweight and has a much higher detection speed with almost no sacrifice of detection accuracy.
CITATION STYLE
Soe, Y. N., Feng, Y., Santosa, P. I., Hartanto, R., & Sakurai, K. (2020). Implementing Lightweight IoT-IDS on Raspberry Pi Using Correlation-Based Feature Selection and Its Performance Evaluation. In Advances in Intelligent Systems and Computing (Vol. 926, pp. 458–469). Springer Verlag. https://doi.org/10.1007/978-3-030-15032-7_39
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