Intrusion Detection System Based on RF-SVM Model Optimized with Feature Selection

With the emergence of increasingly growing network threats, network security becomes a major issue which causes huge existing and potential losses, such as WannaCry. Various methods had been adopted to maintain network security, among which Intrusion Detection System (IDS) is one of the most essential parts of cybersecurity to defense against sophisticated and ever-growing network attacks. A number of researchers have studied comprehensive datasets and effective approaches to build IDS. Machine learning models are also applied in IDS and obtained considerable results in building better network security system. In this paper, we proposed a two-stage IDS based on machine learning models RF and SVM optimized with Feature Selection algorithm CFS. We also conducted experiments on NSL-KDD benchmark datasets to evaluate the performance of the two-stage IDS, comparing against RF and SVM models respectively. The results demonstrated that our proposed two-stage IDS outperformed RF and SVM with an increase from 4.31% to 14.62% in Precision and a reduction of 93.84% in time than SVM.