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Nowadays, Intrusion Detection Systems (IDSs) are used as main security perspective in order to detect any breaches and to measures security level for most of the organizations. However, limitations exist within IDS frequently rendering them inaccurate in detecting attacks; the primary reason for this has been false negative or false positive alarms. The accuracy of the IDS enhanced through increasing of the true positive alerts which will effect to decreasing the false negative and false positive alarms of the intrusions’. The purpose of this paper is to introduce a new blacklist model in order to enhance the accuracy of the IDS. Based upon experimental testing of sample attacks, this paper proposes a new process model for updating a blacklist based on the extraction of patterns through comparison to known malicious activities and code, using similarity measurement against a predefined database and network traffic, after analysis and classification of anomalies. The proposed model shows continued promise in making analysis of network traffic more efficient, and IDS critically more accurate through increasing the true positive alarms.

A Blacklist Process Model to Enhance the IDS Using Similarity Measurements