Data Mining Approaches on Network Data: Intrusion Detection System

Abstract

In the modern era having tremendous growth and the usage of networking over computer systems and its applications mainly focuses to make our system as secure as possible. So with the help of intrusion detection system we can implement security on the system that protects our system data to access from unauthorized persons. Intrusion Detection System (IDS) takes responsibility to monitor the networks or host packets to find the malicious activities which occurs in the system. This paper consists data mining techniques to implement on IDS to identify both of the attacks that is known and unknown attacking patterns, so IDS helps the user to secure the information system. A Network Intrusion Detection System (NIDS) must be installed into the system to work as software application for detecting and monitoring the network activities and also protects from malicious and illegal access of devices. Data mining provides a way to analyze, classify, clean and eliminate the large amount of network data. Therefore, to come out from huge volume of dataset we use different data mining techniques like as classification, clustering along with association rules to analyze the network traffic and make the information as confidentiality and integrity.
Keywords: Intrusion Detection System, Classification, Clustering, Data Mining, Misuse Detection, Anomaly Detection, False Alarm Rate, Network Security