IoT intrusion detection system based on machine learning and deep learning
Karrar Majid Jasim, Joolan Rokan Nayef · Iraqi Journal for Computers and Informatics · 2025
The proliferation of Internet of Things IoT devices has amplified cybersecurity challenges, necessitating robust Intrusion Detection Systems IDS to safeguard against threats such as botnets and Distributed Denial-of-Service DDoS attacks. This paper evaluates the performance of Machine Learning ML and Deep Learning DL models on two benchmark datasets, BoT-IoT and CIC-IDS2017, to develop efficient IDS. Among ML models, XGBoost demonstrated the best performance, achieving 99.99% accuracy on BoT-IoT and 99.91% on CIC-IDS2017 with superior computational efficiency. For DL, Convolutional Neural Networks CNNs achieved 99.99% accuracy on BoT-IoT and 99.61% on CIC-IDS2017 with preprocessing, highlighting the critical role of data preparation. These findings underline the effectiveness of advanced ML/DL models and preprocessing techniques in enhancing IoT security, providing a pathway for real-time, scalable intrusion detection in IoT environments.