PCA-Enhanced Deep Learning Method for Network Intrusion Detection: A Multi-Label Classification Approach

Network Intrusion Detection Systems (NIDS) are a significant advance in addressing the ever-changing nature of information systems and preventing potential cyber-attacks. The complexity and volume of network traffic is making traditional methods ineffective. To addressing these challenges, a PCA-enhanced deep learning method is proposed for network intrusion detection incorporating multi label classification. By using Principle Component Analysis (PCA), reduces the high-dimensionality of network features that is more efficient for deep learning model training. This integration not only speeds up the training process but also enhances the models' generalization capabilities. Applying the proposed model to CSE-CIC-IDS2018 dataset, a remarkable 99.92% accuracy is achieved, which is a significant improvement in both detection accuracy and computational efficiency. The results demonstrate the effectiveness and robustness of the PCA-Enhanced DL technique in real-world network contexts.