K Nearest Neighbor Based Model for Intrusion Detection System

Network security has become more important in this digital era due to the usage of information and communications technology (ICT). Data security is also one of the major issues in today’s world. Due to the usage of this ICT technologies threat to network is also increasing. So in order to solve these problems the researchers has developed IDS that deals with network traffic to identify the harmful users and hackers in the computer. In this paper, we designed a model for IDS for classification of attacks using K-Nearest Neighbor classifier algorithm. KNN is a supervised and lazy machine learning classifier, it shows its best performance in terms of accuracy and classifications. Experimental analysis was conducted on ISCX dataset to judge the implementation of model. The Experimental outcome shows that our suggested model recorded an improved accuracy of 99.96%.