Abstract

Wireless network has an exponential increase in various aspects of the human community. Accordingly, transmitting a vast volume of sensitive and non-sensitive data over the network puts them at risk of being attacked. To avoid this, Intrusion Detection System (IDS) security is intended to detect threats and protect devices from attacks. IDS usually uses one of the following alternative approaches: signature-based, anomaly-based, or hybrid of the two. In spite of the IDS has been the focus of much research in recent years, there is still space for improvement. Based on the anomalybased approach, this paper proposes a modified algorithm called a Multi-layer Feature Selection and Reduction IDS (MFSR-IDS) for providing high-level protection against Denial-of-Service (DoS) and Probe attacks. The MFSR-IDS framework makes three major contributions. First, it reduces the feature dimensionality of the network dataset across three layers. Second, it has a fast and accurate detection system. Third, it provides a mathematical model of the framework under consideration. The MFSR-IDS algorithm selects optimal number of features from KDDCUP’99 dataset which used to train the predictive model based on different learning classifiers and ensemble methodology. The performance of MFSR-IDS is evaluated in terms of Detection Rate (DR), False Positive Rate (FPR), FScore, ROC area, Accuracy (Acc) and Processing time. The experiments indicate that, the proposed MFSR-IDS outperforms some existing IDS frameworks in terms of DR, FPR, Acc and Processing time in detecting DoS and Probe attacks.