Enhance the Intrusion Detection performance of WSN using RLA

Wireless Sensor Network (WSN) is a promising technology for numerous real-time applications because to the fact that it is cost-effective, is of a manageable size, and can be distributed. Nevertheless, sensor nodes are vulnerable to assaults, including illegal entrance, hacking, faulty hardware that generates a physical event, and other situations that are comparable to these. As a result, it is essential to mount a defense against an intrusion when a sensor node is involved. Reinforcement Learning (RL) is a branch of machine learning (ML) that uses many algorithms, each algorithm explains the data it uses in a different way. The RL algorithm is applied to increase the whole capability of connections when simultaneously assuring the reliability and minimizing the severe communications delay. RL algorithm is a lightweight learning method and it increasing the detection of intrusions by tracking the activity of sensor nodes in the WSN.