Enhancing IoT Security Using Ensemble Based Feature Selection
Abstract
The massive heterogeneity of Internet-of-Things (IoT) traffic makes intrusion-detection systems (IDS) especially vulnerable to the severe class imbalance that dominates real world attack logs. Using the recent CICIoT2023 benchmark, which captures 47 flow features for 33 attack types launched by 105 physical devices, we first show that two widely adopted baselines (XGBoost and RF trained on the full data) attain high overall accuracy over 99% yet fail on minority attacks.
Fields of Study & Subjects
Network Security and Intrusion Detection
Internet Traffic Analysis and Secure E-voting
Network Packet Processing and Optimization
Publication details
- Journal / source
- Advances in Artificial Intelligence and Machine Learning
- Volume
- 06
- Issue
- 01
- Pages
- 4909–4909
- Publication date
- 1 January 2026
- Language
- EN