Description:An intrusion detection system is employed to identify various forms of hostile activities that have the potential to undermine the security and integrity of a computer system. The intrusion detection system identifies network assaults targeting susceptible services, data-driven attacks on applications, host-based attacks such privilege escalation, unauthorized logins, access to sensitive files, and malware. The proposed invention comprises an intelligent hybrid architecture that integrates detection methodologies and tiers of intrusion detection system. The data mining techniques such as classification and clustering algorithms is utilized for feature selection, abuse detection, and anomaly detection. The usage of hybrid intrusion detection systems combines the techniques of misuse-anomaly and network-host. The two primary components of the hybrid IDS are the Signature-based Misuse Detection and the Clustering-based Anomaly Detection modules. A matching engine is used to detect intrusions; the signatures kept in the attack signature database are compared to incoming traffic. The temporal properties of network traffic are uncovered via anomaly detection through a data mining process. The proposed invention detects and counteracts unknown attacks, and it handles assaults with several connections.
4 Claims & 1 Figure , Claims:The scope of the invention is defined by the following claims:

Claim:
1. A System/Method for Collaborative Intrusion Detection System using Data Mining comprising the steps of:
a) A method is designed to extract features from the given dataset. A Hybrid Intrusion Detection System (IDS) technique integrates the benefits of both signature-based misuse detection systems and anomaly detection techniques in order to enhance the effectiveness of the system.
b) The Collaborative Hybrid Intrusion Detection System to identify and counteract Denial of Service (DoS) assaults at the network level, employing a collaborative method. The collaboration among the hybrid Intrusion Detection Systems (IDSs) aims to improve the identification of unauthorized individuals. This collaboration relies on the trustworthiness of the peer IDS, which is determined by analyzing the feedback gathered from test messages.
c) The integrated system for detecting unauthorized access that incorporates the techniques of anomaly detection and misuse detection. This entails integrating a signature-based usage detection system with an anomaly detection technique, hence mitigating the limitations associated with employing both systems in isolation.
2. A System/Method for Collaborative Intrusion Detection System using Data Mining as claimed in claim1, led to extract the features by using the data transformation.
3. A System/Method for Collaborative Intrusion Detection System using Data Mining as claimed in claim1, by using matching Engine the signature based misuses detected.
4. A System/Method for Collaborative Intrusion Detection System using Data Mining as claimed in claim1, K Means Clustering Algorithm is used to detect the anomalies.