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Evaluation of Traffic Models under Multiple Black hole Attack in Wireless Mesh Network

Pushpender Sarao
Sharad Institute of Technology College of Engineering, Ichalkaranji, India

Manuscript received August 15, 2022; revised September 28, 2022; accepted February 1, 2023.

Abstract—Wireless networks are the networks in which data communication is done through the wireless channels and wireless nodes. In such type of the networks, cooperation of the all nodes is required. Due to which, chances of security attacks is possible and network performance and security is badly affected. Black hole attack is a security attack comes under the active, internal as well as external attacks. In black hole attack, malicious node presents a suitable, shortest and low cost path. If the above path is selected for data transmission, malicious node drops the received packets and as a result performance of the network is degraded. In this paper, impact of Random Direction (RD) and Reference Point Group Mobility (RPGM) models have been analyzed for wireless mesh network in the presence of multiple black hole attacks. By varying the number of network connections and node density, (Ad hoc on demand distance vector) AODV routing protocol is evaluated. Normalized routing load, average end to end delay, average throughput, and total number of packets received are considered as performance metrics. Bonn-motion tool has been used to create the scenario of mobility models. In the presence of black hole attack, and under RPGM mobility model, AODV routing protocol presents better results in respect of average throughput, normalized routing load. Simulation work has been carried out on network simulator version NS-2.35.
 
Keywords—Random direction, reference pint group mobility, black hole attack, throughput, normalized routing load

Cite: Pushpender Sarao, "Evaluation of Traffic Models under Multiple Black hole Attack in Wireless Mesh Network," Journal of Communications vol. 18, no. 3, pp. 172-179, March 2023. Doi: 10.12720/jcm.18.3.172-179

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.