|Student Name:||David J. Weller-Fahy, SMSgt|
|Thesis:||Network Intrusion Dataset Assessment|
|Location:||Bldg 642, rm 219B (CCR Conference Room)|
|Date & Time:||02/25/2013 at 1200|
|Abstract:|| Research into classification using Anomaly Detection (AD) within the field of Network Intrusion Detection (NID), or Network Intrusion Anomaly Detection (NIAD), is common, but operational use of the classifiers used in the research is not. One reason for the lack of operational use is most published testing of AD methods uses artificial datasets: it is difficult to determine how well published results apply to other dataset and the networks they represent. This research develops a method to predict the accuracy of an AD-based classifier when applied to a new dataset, based on the measured difference between an already classified dataset and the new dataset. Use of this method will allow rapid operational application of new techniques within the NIAD field, and quick selection of the classifier(s) that will be most accurate.