Research and Design for Intrusion Detection System with Hybrid Detector and Apriori Algorithm

Network and host Intrusion Detection Systems (IDS) have become a standard component in security infrastructures. As the action of intrusion represents variable, complicated, and uncertainty characteristic, they face so many problems to resolve for intrusion detection. Each approach has its strengths and weaknesses. We propose a hybrid IDS, which combines network and host IDS, with anomaly and misuse detection mode, utilizes auditing programs to extract an extensive set of features that describe each network connection or host session, and applies data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities. We use an association rule to track all relevant data dependency rule sets for different access roles using a hierarchical structure. We identify malicious transactions from the transaction logs in the database using the data dependency rule sets. These rule sets are continuously updated and stored in a repository. The optimized algorithm actually improves the performance of IDS. Our approach is shown to reduce data access bottlenecks, and ensures minimal manual intervention for maintaining a secure database.