Visible to the public Blackhole Attack Detection Using Machine Learning Approach on MANET

TitleBlackhole Attack Detection Using Machine Learning Approach on MANET
Publication TypeConference Paper
Year of Publication2020
AuthorsPandey, S., Singh, V.
Conference Name2020 International Conference on Electronics and Sustainable Communication Systems (ICESC)
Keywordsad hoc network, Ad hoc networks, Ad-hoc-on-Demand Distance Vector, AODV, artificial neural network, attack vectors, black hole AODV, Black hole attack, Black hole attacks, Blackhole Attack (BA), blackhole attack detection, composability, delays, energy consumption, high QoS parameter, learning (artificial intelligence), machine learning approach, MANET, mobile ad hoc networks, Mobile Ad-hoc Network consists, Mobile adhoc Network, mobile radio, neural nets, on-demand routing mechanism, Predictive Metrics, pubcrawl, quality of service, Resiliency, Routing protocols, Scalability, Secure AODV, security, security mechanism, security of data, self-configurable type, support vector machine, Support vector machines, Support Vector Model, telecommunication security, Throughput, time 37.27 ms
Abstract

Mobile Ad-hoc Network (MANET) consists of different configurations, where it deals with the dynamic nature of its creation and also it is a self-configurable type of a network. The primary task in this type of networks is to develop a mechanism for routing that gives a high QoS parameter because of the nature of ad-hoc network. The Ad-hoc-on-Demand Distance Vector (AODV) used here is the on-demand routing mechanism for the computation of the trust. The proposed approach uses the Artificial neural network (ANN) and the Support Vector Machine (SVM) for the discovery of the black hole attacks in the network. The results are carried out between the black hole AODV and the security mechanism provided by us as the Secure AODV (SAODV). The results were tested on different number of nodes, at last, it has been experimented for 100 nodes which provide an improvement in energy consumption of 54.72%, the throughput is 88.68kbps, packet delivery ratio is 92.91% and the E to E delay is of about 37.27ms.

DOI10.1109/ICESC48915.2020.9155770
Citation Keypandey_blackhole_2020