Enhanced Learning Vector Quantization for Detecting Intrusions In IDS

Abstract

Nowadays, computer infrastructure attacks have become more challenging with computer network extension. The detection of intrusion is the most important process against threats. Several traditional methods are there, but still, there is an issue in detecting the errors related to security. In this article, the intrusion detection system is performed through various detection methods. The efficient proposed method is the Enhanced Learning Vector Quantization (ELVQ) algorithm for detecting the intrusions presented in network traffic. Also, the Pearson Correlation Coefficient Function (PCCF) for similarity determination is introduced. The proposed ELVQ classification achieves higher classification accuracy. In the end, risk factors are analyzed using Hidden Bernoulli Model (HBM). The proposed system is evaluated with KDD CUP99 dataset for efficient results. The evaluation results proved the proposed method performance with various measures and compared with various methods such as Random forest, Random tree, MLP, and Naïve Bayes.