Technical Analysis and Comparison of Intrusion Detection Systems Using Hybrid Machine Learning Approach

Intrusion Detection Systems (IDS) are critical components in securing network environments against a myriad of cyber threats. The evolution of machine learning (ML) techniques has significantly enhanced the capabilities of IDS, offering improved detection accuracy, adaptability, and real-time analysis. This research covers a variety of machine learning approaches, such as supervised and hybrid strategies. We assess these models' performance using important measures including computational efficiency, precision, recall etc. The results show that although supervised machine learning models provides high accuracy, but when used in hybrid model including Random Forests and SVM improves performance. The result is a hybrid model that leverages the strengths of each approach. For instance, Random Forest can provide a robust feature representation, while SVM can refine the decision boundary, leading to a more accurate and reliable classification model. This combination often yields better performance than using any single algorithm alone. This paper underscores the importance of selecting appropriate ML techniques tailored to specific network environments and threat landscapes.