Visible to the public A Time Interval Based Blockchain Model for Detection of Malicious Nodes in MANET Using Network Block Monitoring Node

TitleA Time Interval Based Blockchain Model for Detection of Malicious Nodes in MANET Using Network Block Monitoring Node
Publication TypeConference Paper
Year of Publication2020
AuthorsNarayana, V.Lakshman, Midhunchakkaravarthy, Divya
Conference Name2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA)
Date Publishedjul
KeywordsBlock Analysis, compositionality, Conferences, data communication, malicious node detection, MANET Attack Detection, Metrics, mobile ad hoc networks, Monitoring, Network Block Monitoring Node, pubcrawl, resilience, Resiliency, Routing, routing table, Time Interval Based Blockchain Model
AbstractMobile Ad Hoc Networks (MANETs) are infrastructure-less networks that are mainly used for establishing communication during the situation where wired network fails. Security related information collection is a fundamental part of the identification of attacks in Mobile Ad Hoc Networks (MANETs). A node should find accessible routes to remaining nodes for information assortment and gather security related information during route discovery for choosing secured routes. During data communication, malicious nodes enter the network and cause disturbances during data transmission and reduce the performance of the system. In this manuscript, a Time Interval Based Blockchain Model (TIBBM) for security related information assortment that identifies malicious nodes in the MANET is proposed. The proposed model builds the Blockchain information structure which is utilized to distinguish malicious nodes at specified time intervals. To perform a malicious node identification process, a Network Block Monitoring Node (NBMN) is selected after route selection and this node will monitor the blocks created by the nodes in the routing table. At long last, NBMN node understands the location of malicious nodes by utilizing the Blocks created. The proposed model is compared with the traditional malicious node identification model and the results show that the proposed model exhibits better performance in malicious node detection.
DOI10.1109/ICIRCA48905.2020.9183256
Citation Keynarayana_time_2020