Visible to the public Analysis of Different Black Hole Attack Detection Mechanisms for AODV Routing Protocol in Robotics Mobile AdHoc Networks

TitleAnalysis of Different Black Hole Attack Detection Mechanisms for AODV Routing Protocol in Robotics Mobile AdHoc Networks
Publication TypeConference Paper
Year of Publication2020
AuthorsShakeel, M., Saeed, K., Ahmed, S., Nawaz, A., Jan, S., Najam, Z.
Conference Name2020 Advances in Science and Engineering Technology International Conferences (ASET)
KeywordsAd-hoc On-demand Distance Vector, AODV routing protocol, BH attack, BH finding, black hole attack detection mechanisms, Black hole attacks, malicious station attack, mobile ad hoc networks, pubcrawl, removal mechanisms, Resiliency, robotics MANET environment, Robotics MANETs, robotics mobile ad-hoc networks, route disclosure procedure, router, Routing attack, Routing protocols, Scalability, security attacks, telecommunication security
AbstractRobotics Mobile Ad-hoc Networks (MANETs) are comprised of stations having mobility with no central authority and control. The stations having mobility in Robotics MANETs work as a host as well as a router. Due to the unique characteristics of Robotics MANETs such type of networks are vulnerable to different security attacks. Ad-hoc On-demand Distance Vector (AODV) is a routing protocol that belongs to the reactive category of routing protocols in Robotics MANETs. However, it is more vulnerable to the Black hole (BH) attack that is one of the most common attacks in the Robotics MANETs environment. In this attack during the route disclosure procedure a malicious station promotes itself as a most brief path to the destination as well as after that drop every one of the data gotten by the malicious station. Meanwhile the packets don't reach to its ideal goal, the BH attack turns out to be progressively escalated when a heap of malicious stations attack the system as a gathering. This research analyzed different BH finding as well as removal mechanisms for AODV routing protocol.
DOI10.1109/ASET48392.2020.9118338
Citation Keyshakeel_analysis_2020