Enhancing Feature Selection in IoT Intrusion Detection Using the Ensemble Stacking
Zhijian Zheng, Weilin Gai, Peng Zhang, Ming Quan Zhou · 2024
The Internet of Things (IoT) is increasingly vulnerable to security risks due to new network attacks. Deep learning-based intrusion detection systems (DL-IDS) have emerged as a key solution, but they face challenges like imbalanced datasets and lengthy training times in complex environments. While feature selection algorithms are commonly employed to mitigate these issues, mainstream methods can yield inconsistent results, failing to reflect data characteristics accurately and potentially introducing noise. To address this problem, we propose an ensemble stacking approach to combine multiple feature selection algorithms, thereby minimizing errors from individual approaches. Each feature selection method acts as a base learner to assess feature importance, while logistic regression is a meta-learner to integrate the outputs into a final result. Additionally, we developed a CNN-based intrusion detection model enhanced with BiLSTM and attention mechanisms to improve detection performance. Our approach was tested on the UNSW-NB15 and CIC-IDS2017 datasets, with results indicating a significant improvement in detection performance compared to using a single feature selection method.