An Efficient Wormhole Detection and Optimal Path Selection for Secure Data Transmission in WSN Environment

Abstract

Recently, the Wormhole Attack (WA) affects the Wireless Sensor Networks (WSN). So far, there are '2' solutions to trounce WA, that is, utilizing specialized hardware or capturing a specific pattern extra overhead over the network. Some prevailing solutions for detecting-WAneed special hardware or firmly synchronized clocks or longer processing time in addition to that some solutions can't even locate the WormHole (WH) and also have low security.This paper proposed an efficient WH detection as well as Optimal Path (OP) selection for safe Data Transmission (DT) in WSN. The proposed workencompasses a '2' phase: i) training, and ii) testing. Here, the WSN values are considered and chosen secure node utilizing the Fit Factor (FF). After selecting the secure nodes, the routes are discovered aimed at the optimal assortment of the optimal routes. Next, the gathered data is tested with a trained system in which feature reduction and classification are performed. Lastly, the MCSC securely transfers the non-attack data. The proposed method's outcomes are examined and weighed against the other prevailing techniques and the outcomesexhibited the proposed method's efficiency for attack detection along withdata security. KeywordsWireless sensor network (WSN) • Fit factor • Weight and diversity based deer hunting optimization (WD 2 HO) • Missing value imputation based t-distributed stochastic neighbor embedding (MVI-t-DSNE) • Linear quadratic estimation based deep artificial neural network (LQE-DANN) • Modified Cramer-Shoup Cryptography (MCSC)

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

“…The proposed SabGAN-AqOA-EgAwR-WSN model provide 20.92%, 21.01%, 19.32%, 18.32%, 20.04%, 19.43%, 20.43%, 18.56%, 20.84%, 17.06%, 16.34%, and 15.40% lower delay compared with existing methods like Kavitha and Ganapathy, 13 Gulganwa and Jain, 8 William et al, 35 Srividya and Devi, 33 Mukherjee et al, 24 Mukherjee et al, 23 Mukherjee et al, 22 Jain et al, 11 Verma and Jha, 34 Sindhuja et al, 32 Saranraj et al, 30 and Mehta and Saxena, 20 respectively. The proposed SabGAN-AqOA-EgAwR-WSN model provide 22.02%, 21.81%, 20.62%, 19.72%, 20.04%, 17.43%, 19.03%, 18.26%, 19.04%, 17.96%, 16.14%, and 14.70% lower energy consumption compared with existing methods like Kavitha and Ganapathy, 13 Gulganwa and Jain, 8 William et al, 35 Srividya and Devi, 33 Mukherjee et al, 24 Mukherjee et al, 23 Mukherjee et al, 22 Jain et al, 11 Verma and Jha, 34 Sindhuja et al, 32 delivery ratio compared with existing methods like Kavitha and Ganapathy, 13 Gulganwa and Jain, 8 William et al, 35 Srividya and Devi, 33 Mukherjee et al, 24 Mukherjee et al, 23 Mukherjee et al, 22 Jain et al, 11 Verma and Jha, 34 Sindhuja et al, 32 Saranraj et al, 30 and Mehta and Saxena, 20 respectively. The proposed SabGAN-AqOA-EgAwR-WSN model provides 13.07%, 14.36%, 16.67%, 12.45%, 14.87%, 17.43%, 19.34%, 20.75%, 22.74%, 21.75%, 20.65%, and 24.32% higher network lifetime compared with existing methods like Kavitha and Ganapathy, 13 Gulganwa and Jain, 8 William et al,…”

“…Step 5: Confined exploration Commencing the confined exploration, the target is initiated and Aquila circles the target prey, and articulates the land and occurrences; consequently, it is enabled as demarcation flight with miniscules lither attack. 34 It is deliberate with the help of subsequent Equation ( 27),…”

“…where qua fn represents the eminence utility, and it is appraised with the help of subsequent Equation (34),…”

Wormhole attack mitigation strategies and their impact on wireless sensor network performance: A literature survey

“…In phase 1, a fit‐factor‐based training system of secure nodes is created for optimal route assortment. In phase 2, the system tests gathered data to perform classification and feature reduction 44 . CDABC is an “Artificial Bee Colony”‐based algorithm that uses the “cuddle death design” to find and remove malicious and unhealthy nodes without disturbing the environment while improving performance 48 …”

Trust Aware Secured Energy Efficient Rule based Fuzzy Clustering Protocol with modified Sun Flower optimization Algorithm in Wireless Sensor Networks

“…A number of different of optimization techniques can be used in WSNs to determine the routing path from a source to the intended recipient node. Numerous investigation has been performed in recent years to explore the best path from source to intended recipient nodes for the purpose of maximize the network's sensor node lifetime [30]. There are numerous swarm intelligence based cluster routing protocols namely EELTM [40], P-WWO [41] for the purpose of maximize the network's sensor node lifetime.…”