Hybrid Intrusion Detection System Based on Optimal Feature Selection and Evolutionary Algorithm for Wired Networks

The field of cybersecurity encounters ongoing difficulties in identifying and preventing attacks in networks, and the pervasive threat of cyberattacks demands continual advancements in intrusion detection systems (IDS) to safeguard network integrity. Traditional intrusion detection systems face the challenge of class imbalance. Addressing the formidable challenges posed by class imbalance and high-dimensional data, this research proposes a novel hybrid IDS approach. Leveraging (ACO), the algorithm navigates complex datasets to identify salient features, effectively mitigating the complexities associated with high-dimensional data. Subsequently, a Weighted Stacking Classifier amalgamates the strengths of Random Forest, AdaBoost, and Gradient Boosting classifiers, fortifying the system’s ability to handle class imbalance robustly. By strategically enhancing the importance of base classifiers with favourable training outcomes and diminishing the influence of those yielding inferior results, the hybrid IDS endeavors to optimize classification efficacy. The experimentation, conducted exclusively on the dataset named NSL-KDD, demonstrates the efficacy of the proposed model, yielding remarkable results. With a 90.13% Accuracy, 88.87% precision, 91.23% Recall, and 87.33% F1-score, the hybrid IDS exhibits superior performance in detecting malicious activity. The findings underscore the viability of the proposed hybrid IDS as a potent tool in the ongoing battle against cyber threats, positioning it for real-world deployment across diverse networks.