Classification of Intrusion Detection Using Data Mining Techniques

Nowadays, Internet became a common way for communication as well as a key path for business. Due to the rapid use of Internet, its security aspect is turn more important day by day for which various network intrusion detection systems (NIDSs) are used to protect network data as well as protect the overall network from various attacks. Various intrusion detection systems (IDSs) are placed in different positions of network to protect it. There are various ways by which intrusion detection system can be implemented from which decision tree approach is most commonly used. It provides the easiest way to identify the most corrected field to select, manage, and make proper decision about their identification from a large dataset. This paper focuses to identify normal and attack data present in the network with the help of C4.5 algorithm which is one of the decisions tree techniques, and also it helps to improve the IDS system to identify the type of attacks present in a network. Experimentation is performed on KDD-99 dataset having number of features and different class of normal and attack type data.