A Secure and Intelligent Approach for Next-Hop Selection Algorithm for Successful Data Transmission in Wireless Network

Abstract

AbstractQuality of service in the secure routing protocol is hard to determine an optimal route and malicious nodes. This paper proposed a secure and intelligent approach for the next-hop selection algorithm for successful data transmission in the wireless network. To enhance security, multi-class anomaly detection is used for better quality. To achieve the maximum outcomes with minimum input used, infinite feature selection techniques and multi-class support vector machines are used to identify the suspicious activity with its corresponding attack name. These techniques detect the known and unknown attacks in the multi-class environment using a standard dataset “UNSW_NB15 by the IXIA perform storm tool Australian center for cyber-security (ACCS)”. With the help of a hybrid grey wolf genetic algorithm, it improved the next-hop selection by consisting of three functions. Trust aware function is evaluated by graph theory using D-S evidence in which grid-wise deployed the nodes. Energy-aware function is evaluated by radio energy dissipation model. The last load function is directly impacting the delay. A secure and intelligent approach for the next-hop selection algorithm for successful data transmission in the wireless network gives better simulation results than SRPMA and IASR routing algorithms. Simulation results show that the proposed algorithm achieved the desirable performance against malicious nodes with their correspondence attack name using MATLAB 2015b.KeywordsWireless sensor networkMulti-objectiveHybridAnomaly detectionIntrusion detectionMulti-class SVMGrey wolf optimizationGenetic algorithmRoute discoverTrust awareInfinite feature selection