Proposed neural intrusion detection system to detect denial of service attacks in MANETs

Abstract

MANTs are groups of mobiles hosts that arrange themselves into a grid lacking some preexist organization where the active network environment makes it simple in danger by an attacker. A node leaves out, and another node enters in the network, making it easy to penetration. This paper aims to design a new method of intrusion detection in the MANET and avoiding Denial of Service (DoS) basis on the neural networks and Zone Sampling-Based Traceback algorithm (ZSBT). There are several restrictions in outdating intrusion detection, such as time-intense, regular informing, non-adaptive, accuracy, and suppleness. Therefore, a novel intrusion detection system is stimulated by Artificial Neural Network and ZSBT algorithm using a simulated MANET. Using KDD cup 99 as a dataset, the experiments demonstrate that the model could can detect DoS effectively.