HPSGNN: A Hybrid of Particle Swarm and Genetic Neural Network System to Defense against Blackhole Attack Targeting MANETs

Tuka Jebur

Abstract

In this paper, propose a Hybrid of Particle Swarm and Genetic Neural Network system to the Defense Against Blackhole attack Targeting MANETs. Detection and Prevention System black hole attack in MANETs for this purpose two-stage applying the first stage using PSO to find an optimal cluster head this reduces power consumption, conjunction, the second stage using the genetic algorithm to find optimal path then used a neural network to detect and prevent malicious node in MANETs network with some criteria nodes. Therefore, by isolated all data forms from the network, the Blackhole node is eliminated.

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Paper Citation


in Harvard Style

Jebur T. (2020). HPSGNN: A Hybrid of Particle Swarm and Genetic Neural Network System to Defense against Blackhole Attack Targeting MANETs.In Proceedings of the 1st International Conference on Computing and Emerging Sciences - Volume 1: ICCES, ISBN 978-989-758-497-8, pages 53-61. DOI: 10.5220/0010410700530061


in Bibtex Style

@conference{icces20,
author={Tuka Jebur},
title={HPSGNN: A Hybrid of Particle Swarm and Genetic Neural Network System to Defense against Blackhole Attack Targeting MANETs},
booktitle={Proceedings of the 1st International Conference on Computing and Emerging Sciences - Volume 1: ICCES,},
year={2020},
pages={53-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010410700530061},
isbn={978-989-758-497-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Computing and Emerging Sciences - Volume 1: ICCES,
TI - HPSGNN: A Hybrid of Particle Swarm and Genetic Neural Network System to Defense against Blackhole Attack Targeting MANETs
SN - 978-989-758-497-8
AU - Jebur T.
PY - 2020
SP - 53
EP - 61
DO - 10.5220/0010410700530061