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Without the legitimate user change, this would be an extremely interesting anomaly, possibly indicating the presence of an attacker, and one that our security team would investigate thoroughly. Since our goal was to imple- ment a practical monitoring system that detects just such anomalies, this finding is very encouraging. 3.6.2. Detection of real attack While detecting a change in user was a compelling result, the real purpose of this work is to detect true attackers. Therefore, we present a detection of one such event. This event occurred over a period of nine days. Paths were used as the scan shape and the OMM model was used. A 30-day period immediately preceding the attack was used as training data. The first 15 days were used to provide a baseline graph of existing edges, and the second 15 days were used to define new edges and estimate the new edge rates and OMM parameters for existing edges. In Figure 3.10, we give the heat map of the most anomalous window. Again, this is the union of those paths that exceeded the threshold, and the minimum p -value path in this detection was the smallest p -value in the entire time period. Copyright © 2014. Imperial College Press. All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law. EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 2/16/2016 3:37 AM via CGC-GROUP OF COLLEGES (GHARUAN) AN: 779681 ; Heard, Nicholas, Adams, Niall M..; Data Analysis for Network Cyber-security Account: ns224671
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