DocumentCode :
2315888
Title :
Applying graph-based anomaly detection approaches to the discovery of insider threats
Author :
Eberle, William ; Holder, Lawrence
Author_Institution :
Dept. of Comput. Sci., Tennessee Technol. Univ., Cookeville, TN
fYear :
2009
fDate :
8-11 June 2009
Firstpage :
206
Lastpage :
208
Abstract :
The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, but little work has been done in terms of detecting anomalies in graph-based data. In this paper we present graph-based approaches to uncovering anomalies in applications containing information representing possible insider threat activity: e-mail, cell-phone calls, and order processing.
Keywords :
data mining; graph theory; security of data; cell-phone call; data mining; e-mail; graph-based anomaly detection approach; Algorithm design and analysis; Application software; Computer science; Computer security; Data analysis; Data mining; Information analysis; Monitoring; Telecommunication traffic; Terrorism; anomaly detection; insider threat; minimum description length;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence and Security Informatics, 2009. ISI '09. IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4171-6
Electronic_ISBN :
978-1-4244-4173-0
Type :
conf
DOI :
10.1109/ISI.2009.5137304
Filename :
5137304
Link To Document :
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