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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44478

A NOVEL FIREFLY-BASED NODAL GRADIENT ARTIFICIAL NEURAL NETWORK TO MITIGATE BLACK HOLE AND GREY HOLE ATTACKS

S. MAHESWARI, R. VIJAYABHASKER, KANDASAMY K

Abstract

Wireless connectivity and the newest advancements in mobile devices are employed to develop “a Mobile Ad Hoc Network (MANET)”, which would be a grouping of mobile nodes connected without needing a permanent structure. Those networks are vulnerable to various attacks, comprising the black-hole attack (BHA), gray-hole attack (GHA), and so many others. Numerous scientists have focused on the identification and prevention of specific assaults, either GHA or BHA. Yet MANET's security over a dual-threat is weak. Hence, this paper introduces a novel firefly-based nodal gradient artificial neural network approach that is used to protect the attacks in MANETs. Finding the second cluster head inside the primary clusters eliminates the need for re-clustering, a Cuckoo search-based optimization approach is utilized. Dynamic threshold-based AODV routing protocol must be adaptive in the network structure and preserve routing data for packets to be routed to respective endpoints. The results portion of this paper explains how the system's performance can be enhanced by carefully selecting the optimum nodes for transmitting data packets throughout the network. The findings indicate that the provided method outperforms the prior work in terms of attack performance and that findings are depicted in graphical form by using the Origin tool.

Keywords

Mobile Ad Hoc Network (MANET), black-hole attack (BHA), gray-hole attack (GHA), Cuckoo search-based optimization

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