Intrusion detection system using classification algorithms with feature selection mechanism over real-time data traffic
Gulab Sah, Sweety Singh, Subhasish Banerjee · China Communications · 2024
The key objective of intrusion detection systems (IDS) is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal. These IDS uses many methods of machine learning (ML) to learn from past-experience attack i.e. signatures based and identify the new ones. Even though these methods are effective, but they have to suffer from large computational costs due to considering all the traffic features, together. Moreover, emerging technologies like the Internet of Things (IoT), big data, etc. are getting advanced day by day; as a result, network traffics are also increasing rapidly. Therefore, the issue of computational cost needs to be addressed properly. Thus, in this research, firstly, the ML methods have been used with the feature selection technique (FST) to reduce the number of features by picking out only the important ones from NSL-KDD, CICIDS2017, and CIC-DDoS2019 datasets later that helped to build IDSs with lower cost but with the higher performance which would be appropriate for vast scale network. The experimental result demonstrated that the proposed model i.e. Decision tree (DT) with Recursive feature elimination (RFE) performs better than other classifiers with RFE in terms of accuracy, specificity, precision, sensitivity, F1-score, and G-means on the investigated datasets.