Data-driven network intrusion detection using optimized machine learning algorithms

Network intrusion detection systems (NIDS) play a crucial role in maintaining cybersecurity by identifying malicious network activities. This study presents a comprehensive evaluation of machine learning approaches for network intrusion detection, comparing the performance of Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (K-NN), Gradient Boosting (GB), and Logistic Regression (LR) algorithms. The research investigates the impact of data preprocessing techniques, including data balancing and duplicate removal, on detection performance. Experimental results demonstrate exceptional performance of tree-based methods, with DT and RF achieving accuracy rates of 0.9997 and 0.9996 respectively, alongside precision rates exceeding 0.99. Comparative analysis with existing approaches, including deep learning methods, shows that our optimized tree-based models achieve comparable or superior performance while maintaining computational efficiency. The proposed approach demonstrates perfect Area Under the Curve (AUC) scores of 1.00 for tree-based methods, indicating robust detection capabilities across varying decision thresholds. This research contributes to the field by establishing that simpler machine learning models can achieve state-of-the-art performance in network intrusion detection, offering practical implications for real-world deployment in network security operations.