NIDS-ML-PSO: Network Intrusion Detection System based on Machine Learning Classifiers and Particle Swarm Optimization

As cyber threats continue to escalate with the rapid growth of internet usage, robust intrusion detection systems (IDSs) are essential for safeguarding network infrastructures. This study proposes an enhanced intrusion detection approach using the NSL-KDD dataset, where particle swarm optimization (PSO) is employed for feature selection to optimize machine learning classifier performance. PSO effectively reduces data dimensionality by identifying the most relevant features, improving computational efficiency and detection accuracy. Four machine learning classifiers, such as support vector machine (SVM), decision tree (DT), extra trees (ET), and random forest (RF), are evaluated with and without PSO to assess its impact. Experimental results demonstrate that PSO-based feature selection significantly improves performance, with RF achieving the highest accuracy of 98.33%. Comparative analysis with recent studies highlights the competitive advantage of the proposed method. The study concludes by identifying limitations and proposing future work, including exploring alternative feature selection techniques such as Genetic Algorithm (GA), Bat Algorithm (BA), and Cuckoo Search (CS) to further enhance IDS effectiveness.