Towards Secure Data Transfer: Novel Approach For Route Selection And Attack Identification In Wireless Sensor Network Cyber Security

Abstract

Route selection in wireless sensor network (WSN) cybersecurity involves selecting safe routes for data transfer, whereas attack detection searches and eliminates any potential risks or criminal activity in the network to protect communication integrity and dependability. In this paper, we select the secure node and then select the optimal path using Quasi-Bee Reflection Optimization (QBRO). We gathered the NSL-KDD dataset from Kaggle and we proposed Fine-tuned Elman neural network (FTENN) as a classification method for detecting the attack in WSN, then transferring the non-attack data with secure using fine-tuned ElGamal Cryptography algorithm (FTEC). As a result, we evaluate the performance of our proposed method and compare it with the existing method. Our proposed approach demonstrates superior performance in cybersecurity selecting the optimal path, classification and data transfer compared to existing methods.