Efficient Feature Selection Technique for Network Intrusion Detection System Using Discrete Differential Evolution and Decision

Network intrusion is a critical challenge in information and communication systems amongst other forms of fraud perpetrated over the Internet. Despite the various traditional techniques proposed to prevent this intrusion, the threat persists. These days, intrusion detection systems (IDS) are faced with detecting attacks in large streams of connections due to the sporadic increase in network traffics. Although machine learning (ML) has been introduced in IDS to deal with finding patterns in big data, the irrelevant features in the data tend to degrade both the speed and accuracy of detection of attacks. Also, it increases the computational resource needed during training and testing of IDS models. Therefore, in this paper, we seek to find the optimal feature set using discretized differential evolution (DDE) and C4.5 ML algorithm from NSL-KDD standard intrusion dataset. The result obtained shows a significant improvement in detection accuracy, a reduction in training and testing time using the reduced feature set. The method also buttresses the fact that differential evolution (DE) is not limited to optimization of continuous problems but work well for discrete optimization.