A Performance Evaluation of Intrusion Detection system to get better detection rate using ANN Technique

Intrusion Detection System (IDS) is a Detection System that works for detecting malicious attacks. This can be defined as software for security management. Many researchers have proposed the Intrusion Detection System with different techniques to achieve the best accuracy. This paper outlines an investigation on the unsupervised neural network models and choice of one of them for evaluation and implementation. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the two algorithms used are Back-propagation algorithm and Growing Self organization Map algorithm. After implementing these algorithms, we have proposed a comparative analysis between them and choose the best accuracy rate among them. Here, it has been proved that, the ANN procedure is validated against a simulated IoT network. The experimental results demonstrate far better accuracy and when use in implementation of application software, it can successfully detect various attacks.