Title | Detection and Prevention of Blackhole Attack in AODV of MANET |
Publication Type | Conference Paper |
Year of Publication | 2021 |
Authors | Khan, Asif Uddin, Puree, Rajesh, Mohanta, Bhabendu Kumar, Chedup, Sangay |
Conference Name | 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) |
Keywords | AODV, Black hole attack, clustering, compositionality, Conferences, MANET, MANET Attack Detection, manet attack prevention, Mechatronics, Metrics, mobile nodes, Network topology, Partitioning algorithms, pubcrawl, Resiliency, Routing, Routing protocols, Topology |
Abstract | One of the most dynamic network is the Mobile Adhoc (MANET) network. It is a list of numerous mobile nodes. Dynamic topology and lack of centralization are the basic characteristics of MANET. MANETs are prone to many attacks due to these characteristics. One of the attacks carried out on the network layer is the blackhole attack. In a black-hole attack, by sending false routing information, malicious nodes interrupt data transmission. There are two kinds of attacks involving a black-hole, single and co-operative. There is one malicious node in a single black-hole attack that can act as the node with the highest sequence number. The node source would follow the direction of the malicious node by taking the right direction. There is more than one malicious node in the collaborative black-hole attack. One node receives a packet and sends it to another malicious node in this attack. It is very difficult to detect and avoid black-hole attacks. Many researchers have invented black-hole attack detection and prevention systems. In this paper, We find a problem in the existing solution, in which validity bit is used. This paper also provides a comparative study of many scholars. The source node is used to detect and prevent black hole attacks by using a binary partition clustering based algorithm. We compared the performance of the proposed solution with existing solution and shown that our solution outperforms the existing one. |
DOI | 10.1109/IEMTRONICS52119.2021.9422643 |
Citation Key | khan_detection_2021 |