Machine Learning and Deep Learning for Enhanced DDoS Detection in IoT-Based Intrusion Detection System
Kamir Kharoubi, Sarra Cherbal, Maroua Akkal · 2024
Securing Internet of Things (IoT) networks is critical due to their increasing prevalence and susceptibility to cyberattacks. Machine Learning (ML) and Deep Learning (DL)powered Intrusion Detection Systems (IDS) provide a promising solution to counter these threats. This study focuses on detecting Distributed Denial of Service (DDoS) attacks, ensuring IoT device availability and protecting critical services within IoT ecosystems. Five ML and DL models, including XGBoost, were trained and evaluated on the recent CICIoT2023 dataset to classify network traffic as benign or DDoS. Evaluation results show that XGBoost outperformed other models, achieving a 99. 96% accuracy in multi-classifying DDoS attack types with benign traffic and a rapid prediction time of 0.56 microseconds per traffic sample. These results highlight XGBoost's potential for real-time IoT security applications.