Title | To Detect and Prevent Black Hole Attack in Mobile Ad Hoc Network |
Publication Type | Conference Paper |
Year of Publication | 2021 |
Authors | Shah, Imran Ali, Kapoor, Nitika |
Conference Name | 2021 2nd Global Conference for Advancement in Technology (GCAT) |
Keywords | AODV, AODV protocol, Black hole attack, compositionality, data transfer, Detection and Prevention., MANET, MANET Attack Detection, manet attack prevention, Metrics, mobile ad hoc networks, mobile computing, Monitoring, pubcrawl, Resiliency, Routing, Routing protocols |
Abstract | Mobile Ad hoc Networks ‘MANETs’ are still defenseless against peripheral threats due to the fact that this network has vulnerable access and also the absence of significant fact of administration. The black hole attack is a kind of some routing attack, in this type of attack the attacker node answers to the Route Requests (RREQs) thru faking and playing itself as an adjacent node of the destination node in order to get through the data packets transported from the source node. To counter this situation, we propose to deploy some nodes (exhibiting some distinctive functionality) in the network called DPS (Detection and Prevention System) nodes that uninterruptedly monitor the RREQs advertised by all other nodes in the networks. DPS nodes target to satisfy the set objectives in which it has to sense the mischievous nodes by detecting the activities of their immediate neighbor. In the case, when a node demonstrates some peculiar manners, which estimates according to the experimental data, DPS node states that particular distrustful node as black hole node by propagation of a threat message to all the remaining nodes in the network. A protocol with a clustering approach in AODV routing protocol is used to sense and avert the black hole attack in the mentioned network. Consequently, empirical evaluation shows that the black hole node is secluded and prohibited from the whole system and is not allowed any data transfer from any node thereafter. |
DOI | 10.1109/GCAT52182.2021.9587471 |
Citation Key | shah_detect_2021 |