The goal of building Intrusion Detection System is conceptualized with need of making secure and protected publically and privately accessible data so that it can be easily avoided from its unauthorized uses. Since increase of network density and heavy use of development of internet has generated a major challenge of making these network data and traffic protected from intruded attacks. Security of network traffic is becoming a major issue of computer network system. Attacks on the network are increasing day-by day. The most publicized attack on network traffic is considered as Intrusion. Data mining techniques are used to monitor and analyze large amount of network data & classify these network data into anomalous and normal data. Since data comes from various sources, network traffic is large. Data mining techniques such as classification and clustering are applied to build Intrusion Detection system. An effective Intrusion detection system requires high detection rate, low false alarm rate as well as high accuracy. This research paper includes effective Data mining techniques applied on IDS for the effective detection of pattern for both malicious and normal activities in network by strong classification mechanism, it will simplify the task of securing information system through this proposed Intrusion Detection system which is developed by the optimized use of newly Ant Colony optimization followed by Hyper pipes classifier classification. Intrusion detection system has been used for ascertaining intrusion and to preserve the security goals of information from attacks.

Accuracy, Attack, Ant Colony, Classifier, Clustering, Data mining, Detection, Information, Intrusion, Signature, optimization,etc.

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