Network intrusion detection system using various data mining techniques

There are many risk of network attacks in the Internet environment. Nowdays, Security on the internet is a vital issue and therefore, the intrusion detection is one of the major research problem for business and personal networks which resist external attacks. A Network Intrusion Detection System (NIDS) is a software application that monitors the network or system activities for malicious activities and unauthorized access to devices. The goal of designing NIDS is to protect the data's confidentiality and integrity. Our project focuses on these issues with the help of Data Mining. This research paper includes the implementation of different data mining algorithms including Linear regression and K-Means Clustering to automatically generate the rules for classify network activities. A comparative analysis of these techniques to detect intrusions has also been made. To learn the patterns of the attacks, NSL-KDD dataset has been used.