A Hybrid Intrusion Detection System Based on Feature Selection and Voting Classifier

In recent years, cyber threats have significantly increased in sophistication and targeted nature. Traditional security measures often prove inadequate in detecting malicious activities. Intrusion Detection Systems (IDS) can mitigate these threats by monitoring and alerting administrators to suspicious activities. However, the large volume and high dimensionality of network traffic data can pose a challenge for IDS, as irrelevant and redundant features can reduce the effectiveness of detection. Additionally, many machine learning-based IDS rely on individual base classifiers, which can lack robustness and may not perform well in varying situations. To address these issues, this paper proposes a hybrid IDS that combines feature selection and voting classifier techniques. The proposed model utilizes an improved binary Pigeon-Inspired Optimization algorithm and the Minimal-Redundancy-Maximal-Relevance algorithm for feature selection, and a voting classifier incorporating Random Forest, K-Nearest Neighbors, and XGBoost to classify network traffic. The model has been evaluated on three popular datasets: KDDCUP99, NLS-KDD and CIC-IDS2017. The proposed method demonstrates superior performance in terms of Accuracy, Precision, Recall, F1-score, and False Positive Rate when compared to several machine learning and deep learning models.