Implementing a network intrusion detection system using semi-supervised support vector machine and random forest
Sandeep Shah, Pramita Sree Muhuri, Xiaohong Yuan, Kaushik Roy, Prosenjit Chatterjee
Abstract
Network security is an important aspect for any organization to keep their information systems secure. A Network Intrusion Detection System (NIDS) is an aid to secure the network by detecting abnormal or malicious traffic. In this paper, we applied a Semi-supervised machine learning approach to design a NIDS. We implemented semi-supervised Support Vector Machine (SVM) and semi-supervised Random Forest (RF) classifiers to classify the NSL-KDD dataset. We have classified the dataset in both binary and multiclass. We have also implemented a Genetic Algorithm (GA) approach to select the optimal features from the original features set. Results show that the random forest algorithm produces a better result than SVM using semi-supervised learning method. Also, the results show that applying the GA in SVM produces a better result than without using GA, and so does using GA in Semi-supervised Random Forest.