Dynamic Defense Mechanism for DoS Attacks in Wireless Environments Using Hybrid Intrusion Detection System and Statistical Approaches

Abstract

Security in wireless frameworks is a significant and difficult task because of the open environment. The Denial of Service (DoS) is as yet significant endeavour to make an online assistance inaccessible. The objective of this attack is to keep the authentic nodes from getting to the administrations. Intrusion detection systems assume an essential job in identifying DoS attacks that improve the performance of the system. However massive information from the system presents huge difficulties to the discovery of DoS attack, as the identification framework needs adaptable techniques for gathering, storing and processing a lot of information. In order to defeat these difficulties, this paper proposes Hybrid Intrusion Detection System (HIDS) framework dependent on different MLP strategies. In this article HIDS utilizes Naive Bayes (NB), irregular random forest (RF), decision tree (DT), multilayer perceptron (MLP), K-nearest neighbours (K-NN) and support vector machine (SVM) for better outcomes. The NSL-KDD dataset and UNSW-NB15 dataset are taken to examine the detection accuracy. The experiment results show that the proposed defence system is accomplished with high accuracy, high detection rate and low false alarm rate in both the datasets.