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.
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PDFDOI: http://dx.doi.org/10.21533/pen.v10i3.2997
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Copyright (c) 2022 Raghad Mohammed Hadi, Salma Hameedi Abdullah, Wafaa M. Salih Abedi
This work is licensed under a Creative Commons Attribution 4.0 International License.
ISSN: 2303-4521
Digital Object Identifier DOI: 10.21533/pen
This work is licensed under a Creative Commons Attribution 4.0 International License