Enhanced Hybrid Intrusion Detection System using PSO-Optimized CNN Architecture

Cyberattacks have grown into enduring threats, demanding advanced measures to secure vital data and systems. Although firewalls provide basic traffic filtering, they often fall short against advanced threats like zero-day exploits, leading to the adoption of Intrusion Detection System (IDS). By leveraging Machine Learning (ML), IDS can identify intricate attack patterns, though challenges such as unbalanced dataset and computational inefficiencies persist. Deep Learning (DL) addresses some ML drawbacks; however, it still encounters issues like vanishing gradients and prolonged training durations. Techniques like normalization during preprocessing help balance datasets and enhance model efficiency. Convolutional Neural Networks (CNN) excel in extracting spatial features from datasets like “Data Generation and Knowledge Sharing”. Nevertheless, optimization algorithms are essential to fine-tune performance and prevent convergence challenges. Updating CNN weights in pivotal for adaptive learning and employing the Particle Swarm Optimization (PSO) algorithm enhances both exploration and exploitation by dynamically refining the weights. This synergistic method boosts feature learning, minimizes false positives, and enhances detection accuracy. When evaluated using metrics such as precision, recall, F1-score, and confusion matrix, the proposed model outperforms traditional ML and DL methods, providing a robust solution to counter evolving cyber threats.