Identification and eradication of attacker node in a mobile ad-hoc network environment using prediction model on delay f
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ORIGINAL PAPER
Identification and eradication of attacker node in a mobile ad‑hoc network environment using prediction model on delay factor V. M. Gayathri1 · P. Supraja1 Received: 19 January 2020 / Accepted: 4 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Mobile ad-hoc network (MANET) is a theoretical and experimental approach for achieving the applications to the best using VANETs. Given the mobility of nodes in the mobile ad-hoc networks, it is hard to depict the nature of the network or the structure of the network. With static nodes, it is easy to monitor a network. In a mobile environment, any node can come and join the network based on the distance covered by the entire network. A node that enters the region joins the network, while one that moves away leaves it and ceases participating in network communication. The routing table is updated, based on the movement of the nodes. Owing to the factors above, security fails to live up to expectations. Identifying a vulnerable node is a difficult proposition. This paper offers a prediction model based on the delay factor, which impacts the performance of the node and its network. The experimental results determine the malicious node. A malicious node is disconnected from the network. Keywords MANET · Prediction model · Delay · Malicious factor
1 Introduction Nodes are naturally mobile, and any node can join a network. The lack of security makes it possible for intruder nodes to enter the network and destroy communication. An intruder node is placed in the network and the results obtained from the simulations are analyzed. The delay factor is considered in the building of a prediction model. Once the node is identified as vulnerable, the information about the malicious node will be passed on to other nodes in the network. The details of the vulnerable node are deleted from the routing table of other nodes in the network. The vulnerable node continues to wait for data and control packets from other nodes, given its unawareness of the deletion of its details from their routing tables. The distance source routing (DSR) protocol used to implement the setup above (Govindan and Mohapatra 2011) Surveys computing methods for trust prediction and dynamics, and discusses aggregation algorithms on the effect of trust and its impact on the performance * V. M. Gayathri [email protected] P. Supraja [email protected] 1
Department of Information Technology, SRM Institute of Science and Technology, Kattankulathur, India
factor, and (Batabyal and Bhaumik 2015) Explains the synthetic mobility model, analyses mobile traces, and explores challenges in enhancing performance despite issues with mobility, and discusses open challenges in this research area. In this work, an intruder node is introduced into the network and a communication report made. The delay factor is also taken into consideration for a prediction model to be built. Once the node is identified as an intruder, information pertaining to it is passed on to other nodes in the
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