Intelligent Intrusion Detection System Combining Behavioral Analysis and Machine Learning Methods

The rapid escalation of cyber-attacks across complex network infrastructures exposes critical limitations in traditional intrusion detection systems (IDS), especially those relying solely on static signatures or isolated classifiers. This research introduces an Intelligent Intrusion Detection System (IIDS) that combines behavioral analysis with a hybrid ensemble machine learning model, integrating Support Vector Machine (SVM) and a soft Voting Classifier comprising Random Forest (RF) and Logistic Regression (LR). Unlike existing ensemble models, the novelty lies in the integration of temporal behavioral features like access patterns and command histories-with probabilistic fusion to enhance threat detection. The model is evaluated on the CICIDS2017 dataset and achieves 98.8% accuracy and 98.7% F1-score, outperforming state-of-the-art models including HIDM and recent deep hybrid learning approaches. Comparative analysis demonstrates improved generalization and robustness, especially in identifying low-frequency and behavioral anomalies. The results validate the efficacy of coupling behavioral insights with ensemble machine learning techniques to develop adaptive, accurate, and resilient IDS suited for evolving cybersecurity threats.