Intrusion detection system based on machine learning techniques

Recently, the data flow over internet has exponentially increased due to massive growth of computer networks connected it. Some these can be classified as a malicious activity which cannot captured by firewalls and anti-malwares. Due this, intrusion detection systems are urgent need in order recognize keep integrity availability. In this study, an system based on cluster feature concepts KNN classifier been suggested handle various challenges issues such complete data, mixed-type noise data. To streng then proposed special kind patterns similarity measures supported deal with types challenges. The experimental results show that classification accuracy is better than K-nearest neighbor (KNN) support vector machine classifiers when processing incomplete set, inspite droping down overall accuracy.