Intrusive Detection of Wormhole Attack Using Cluster - Based Classification Model In MANET
Author
Abstract

MANET Attack Detection - Nodes in a “distributed” Adhoc network do not share a single centralized infrastructure. Hosts and routers can be found on any mobile node. In addition, it sends packets to additional mobile nodes in the network that aren't directly connected to the main network. Network layer assaults such as black hole, wormhole, and denial-of-service (DoS) are all easily carried out on mobile Ad hoc networks (MANETs). Wrong-way attacks, which divert packets from one part of the network and route them through an alternate one, are extremely difficult to detect. Even though the wormhole attack has been countered, the current solutions still suffer from excessive delivery delays, packet delivery ratio issues, and energy consumption. In this paper, a cluster-based algorithm (CBA) detects hybrid wormhole assaults by computing based on sequence number, round-trip time (RTT), which is more optimistic than existing solutions for detecting both in-band and out-of-band connections are possible. RTT thresholds are predicted in this paper using CBA to distinguish between attack and non-attack routes. NS-2 network simulator is used to test the suggested technique. The proposed algorithm's performance was evaluated by looking at its throughput. Results demonstrate that CBA reduced 20% of total energy consumption compared to AODV, the traditional On-Demand Ad-hoc Distance Vector routing protocol.

Year of Publication
2022
Conference Name
2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)
Date Published
June
DOI
10.1109/ICACCS54159.2022.9785233
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