期刊名称：Indian Journal of Computer Science and Engineering
印刷版ISSN：2231-3850
电子版ISSN：0976-5166
出版年度：2021
卷号：12
期号：6
页码：1760-1770
DOI：10.21817/indjcse/2021/v12i6/211206024
语种：English
出版社：Engg Journals Publications
摘要：Wireless Sensor Network (WSN) is a generally hopeful technology for several real-time applications due to its cost-effective, size, and distribution nature. WSN is a collection of sensor nodes spread in a great region such that the required information can be collected. However, sensor nodes are susceptible to attacks, for example, intrusion, hackers, defective hardware starting the physical incident, etc. Therefore, it is compulsory to defend a sensor node from an intrusion. If it brings attacked next, the information transmitted through the sensor may be wrong and lead to incorrect data analysis, leading to unnecessary outcomes. To solve these issues, Reinforcement Learning for Intrusion Detection (RLID) and Improving Optimal Route by Cuckoo Search is proposed. The Reinforcement Learning uses the repeating node classification for detecting the intrusion during the route discovery. Reinforcement learning evaluates the sensor node behaviour by the quality of the link, and it is computed by sensor node packet forward rate and node residual energy. Here, the repeating node classification method classified the intrusion sensor based on node-link quality. As a result, it can improve intrusion detection performance efficiently. Besides, the Cuckoo Search Technique (CST) is used to find the optimal forwarder for transmitting the data from sender to destination. The main objective of this work is to offer optimal routing and communicate the data via normal sensor nodes in WSN. The simulation platform and the obtained results are compared with the baseline protocol to prove the efficiency of our proposed approach.
