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Tversky indexive cramer-shoup cryptography based deep structured belief neural learning for secured routing in MANET
Author(s):
1. Mrs.R.NAVAMANI: Department of Computer Science, Periyar University, Salem, India
2. N.ELAMATHI: Department of Computer Science, Trinity College For Women, Namakkal, India
Abstract:
A secure routing is a significant concern due to its self-organizing and cooperative nature, capable of independent process, rapid changing topologies, limited physical security and so on. With the routing being a critical aspect for MANETs, existing routing protocols are not sufficient for security constraints. In this paper, a novel routing algorithm called a Tversky Indexive Cramer-Shoup Cryptography based Deep Structured Belief Neural Learning (TICSC-DSBNL) technique is introduced with security and higher data confidentiality in MANET. The TICSC-DSBNL technique comprises one input layer, three hidden layers and one output layer. The number of mobile nodes is taken as input in the input layer and sends the mobile node to the hidden layer 1. For every mobile node in the hidden layer 1, the trust value is calculated to identify the node as normal node or malicious node using Tversky Similarity index. The index is used to find the similarity between mobile nodes for classifying the node as normal node or malicious node. After that, the normal nodes are given to the hidden layer 2. In that layer, a route path between the nodes gets established and selects the shortest route path. In third hidden layer, the Cramer-Shoup cryptosystem is applied for encryption and decryption to perform secure routing with higher confidentiality in MANET. Simulation is conducted in with different performance metrics such as packet delivery ratio, packet drop rate, and delay, throughput, and data confidentiality rate with respect to the number of data packets. The discussed results indicates that the proposed TICSC-DSBNL technique improves the performance of secure routing with higher delivery ratio, data confidentiality with lesser delay as well as packet drop than the state-of-the-art methods.
Page(s): 4611-4621
DOI: DOI not available
Published: Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 12, Year: 2022
Keywords:
MANET , Deep Structured Belief Neural Learning , Tversky Similarity Index , Secure Routing , CramerShoup Cryptosystem
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