Enhancing Intrusion Detection with CNN Attention Using NSL-KDD Dataset

Intrusion detection systems (IDS) are essential in cybersecurity to protect networks from online threats. This research addresses the urgent need for compact, highly adaptable Network Intrusion Detection Systems (NIDS) capable of identifying anomalies. Utilizing the NSL-KDD dataset, which includes 43 variables with labels “attack” and “level,” the study proposes a novel approach combining channel attention and convolutional neural networks (CNN). This dataset facilitates a comprehensive assessment of the proposed intrusion detection strategy, aiming to maintain operational efficiency while enhancing detection accuracy. Typically, NIDS analyzes both risky and normal behaviors using various techniques. Our CNN-based approach, integrated with channel attention, achieves an impressive accuracy rate of ${9 9 . 7 2 8 \%}$ on the NSLKDD dataset. This solution significantly outperforms previous methods such as ensemble learning, CNN, RBM (Boltzmann machine), ANN, hybrid auto-encoders with CNN, MCNN, and adaptive algorithms, demonstrating a substantial improvement in intrusion detection performance. The results underscore the effectiveness of our method in enhancing intrusion detection precision, marking a significant advancement in the field. Future efforts will focus on strengthening and expanding this approach to counteract evolving cyber threats and adapt to changing network conditions.