A new data mining based network Intrusion Detection model

Nowadays, as information systems are more open to the Internet, the importance of secure networks is tremendously increased. New intelligent Intrusion Detection Systems (IDSs) which are based on sophisticated algorithms rather than current signature-base detections are in demand. There is often the need to update an installed Intrusion Detection System (IDS) due to new attack methods or upgraded computing environments. Since many current Intrusion Detection Systems are constructed by manual encoding of expert knowledge, changes to them are expensive and slow. In data mining-based intrusion detection system, we should make use of particular domain knowledge in relation to intrusion detection in order to efficiently extract relative rules from large amounts of records. This paper proposes new ensemble boosted decision tree approach for intrusion detection system. Experimental results shows better results for detecting intrusions as compared to others existing methods.