Intrusion Detection Using Data Mining

Presently, the internet is an almost universal method of communication for both individuals and businesses. Due to increased use of the internet, its security perspective is becoming more important every day for numerous network intrusion detection systems (IDS) from several attacks. Several IDS are located at heterogeneous locations of networks to preserve it. Various methods are used for detection of attacks or fraud and can be enforced in decision tree perspective. It gives the simplest way to recognise the maximum right area to select, manage and form optimize decision regarding their identification from greatest set of data. This chapter explores the modern intrusion detection with a distinctive determination perspective of data mining. This discussion focuses on major facets of intrusion detection strategy, that is, misuse detection. It focuses on identifying attacks, information or data which is present on the network using C4.5 algorithm, which is a type of decision tree technique and also it helps to enhance the IDS system to recognize types of attacks in network. For this attack detection, KDD-99 dataset is used; it contains several features and a different class of general and attack-type data.