Combating Network Intrusions using Machine Learning Techniques with Multilevel Feature Selection Method

The heavy dependency on the internet, as well as other emerging technologies for access, storage, and sharing of information, has triggered a proportional increase in cyberattacks, thereby making network intrusion detection system (NIDS) a crucial component in security systems. NIDS is employed to monitor abnormal activities on a network. However, issues of low accuracy and high false positive remain prevalent among NIDSs. In an attempt to improve the performance in the prediction of network intrusions, this paper applied in parallel, four (4) machine learning models: k-Nearest Neighbor (k-NN), Naïve Bayes (NB), Logistic Regression (LR), and Artificial Neural Network (ANN) with multilevel feature selection method to determine which of the models has the best detection capability in terms of Accuracy, Positive Predicted Values (PPV), Recall, F1-score, and Receiver Operating Characteristics (ROC) Curve. The models were validated on NSL-KDD intrusion data and the result shows k-NN had the best performance with an accuracy of 79.1%, recall of 66.5%, positive predicted values of 96.7%, and F1-measure of 78.1%.