DocumentCode :
252469
Title :
Anomaly detection system: Towards a framework for enterprise log management of security services
Author :
Ozulku, Omer ; Fadhel, Nawfal F. ; Argles, David ; Wills, Gary B.
Author_Institution :
ECS, Univ. of Southampton, Southampton, UK
fYear :
2014
fDate :
8-10 Dec. 2014
Firstpage :
97
Lastpage :
102
Abstract :
In recent years, enterprise log management systems have been widely used by organizations. Several companies such as (IBM, MacAfee and Splunk etc.) have brought their own log management solutions to the market. However, the problem is that these systems often require proprietary hardware and do not involve web usage mining to analyze the log data. The purpose of this paper is to investigate an approach towards a framework for managing security logs in enterprise organizations called of the anomaly detection system (ADS), built to detect anomalous behavior inside computer networks that is free from hardware constraints and benefits from web usage mining to extract useful information from the log files.
Keywords :
Internet; business data processing; computer network security; data mining; ADS; Web usage mining; anomalous behavior detection; anomaly detection system; computer networks; enterprise log management; enterprise organizations; proprietary hardware; security log management; security services; useful information extraction; Algorithm design and analysis; Data mining; Organizations; Security; Web servers; Anomaly Detection; RESTful style log data collection; enterprise log management; web usage mining algortithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Internet Security (WorldCIS), 2014 World Congress on
Conference_Location :
London
Type :
conf
DOI :
10.1109/WorldCIS.2014.7028175
Filename :
7028175
Link To Document :
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