Visible to the public Detection and Prevention of Black Hole Attack in SUPERMAN

TitleDetection and Prevention of Black Hole Attack in SUPERMAN
Publication TypeConference Paper
Year of Publication2020
AuthorsSharma, K., Bhadauria, S.
Conference Name2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)
KeywordsAd hoc networks, Black hole attacks, MANET Attack Detection, manet attack prevention, mobile computing, pubcrawl, Resiliency, Routing, Routing protocols, Scalability, security, Telecommunications, wireless networks
AbstractMANETs are wireless networks, providing properties such as self-configuration, mobility, and flexibility to the network, which make them a popular and widely used technique. As the usage and popularity of the networks increases, security becomes the most important factor to be concerned. For the sake of security, several protocols and methodologies have been developed for the networks. Along with the increase in security mechanisms, the number of attacks and attackers also increases and hence the threat to the network and secure communication within it increases as well. Some of the attacks have been resolved by the proposed methodologies but some are still a severe threat to the framework, one such attack is Black Hole Attack. The proposed work integrates the SUPERMAN (Security Using Pre-Existing Routing for Mobile Ad-hoc Networks) framework with appropriate methodology to detect and prevent the network from the Black Hole Attack. The mechanism is based on the AODV (Ad-hoc On-demand Distance Vector) routing protocol. In the methodology, the source node uses two network routes, from the source to the destination, one for sending the data packet and another for observing the intermediate nodes of the initial route. If any node is found to be a Black Hole node, then the route is dropped and the node is added to the Black Hole list and a new route to send the data packet to the destination is discovered.
DOI10.1109/ANTS50601.2020.9342822
Citation Keysharma_detection_2020