Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security
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An Intrusion Detection System (IDS) is a crucial element of a network security posture. One class of IDS, called signature-based network IDSs, monitors network traffic, looking for evidence of malicious behavior as specified in attack descriptions (referred to as signatures). Many studies have reported that IDSs can generate thousands of alarms a day, many of which are false alarms. The problem often lies in the low accuracy of IDS signatures. It is therefore important to have more accurate signatures in order to reduce the number of false alarms. One part of the false alarm problem is the inability of IDSs to verify attacks (i.e. distinguish between successful and failed attacks). If IDSs were able to accurately verify attacks, this would reduce the number of false alarms a network administrator has to investigate. In this paper, we demonstrate the feasibility of using a data mining algorithm to automatically generate IDS verification rules. We show that this automated approach is effective in reducing the number of false alarms when compared to other widely used and maintained IDSs.