A Hybrid IDS Using GA-Based Feature Selection Method and Random Forest
W Lee, S Stolfo, K Mok, N Moustafa, S Revathi, A Malathi, D Denning, W Gongxing, H Yimin, A Saxena, S Sinha, P Shukla, S Northcutt, J Novak, L Haripriya, M Jabbar, M Subba, S Biswas, S Karmakar, P Tang, X Tang, Z Tao, B Kavitha, S Karthikeyan, B Chitra, K Desale, R Ade, Y Aung, M Min, M Tavallaee, E Bagheri, W Lu, A Ghorbani, Y Chang, W Li, Z Yang, J Zhang, M Zulkernine, A Haque, M Zhao, C Fu, L Ji, K Tang, M Zhou, S Kwok, C Carter, T Ho
Abstract
In recent years, the rapid development of internet technology brings many severe network security problems linked to malicious intrusions. Intrusion Detection System is considered to be one of the significant techniques to safeguard the network from both external and internal attacks. However, with the fast expansion of the IoT network, cyberattacks are also changing quickly, and many unknown types are showing up in the contemporary network environment. Consequently, the efficiency of traditional signature-based and anomaly-based Intrusion Detection System is insufficient. We propose a novel Intrusion Detection System, which uses an evolutionary technique based feature selection approach and a Random Forest-based classifier. The evolution-based feature selector uses an innovative Fitness Function to select the important features and reduces dimensions of the data, which raise the Ture Positive Rate and reduce the False Positive Rate at the same time. With exceptional high accuracy in multi-classification tasks and outstanding capabilities of handling noise in massive data scenarios, the Random Forest technique is widely used in anomaly detection. This research proposes a framework that can select more steady features and improve the classification results as compared with other technologies. The proposed framework is tested and experimented on UNSW-NB15 datasets and NSL-KDD datasets. Various statistical results and detailed comparison to other methods are presented within this article.