The University of Southampton
University of Southampton Institutional Repository

Anomaly detection system: towards a framework for enterprise log management of security services

Anomaly detection system: towards a framework for enterprise log management of security services
Anomaly detection system: towards a framework for enterprise log management of security services
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.
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, organizations, security, web servers, anomaly detection, RESTful style log data collection, web usage mining algortithm
97-102
Ozulku, Omer
ff732bf2-3802-4bfb-b540-4675249ec4ff
Fadhel, Nawfal
e73b96f2-bf15-40cb-9af5-23c10ea8e319
Argles, David
7dd3d276-b2b2-4fb2-a0e8-4058bb01fc37
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0
Ozulku, Omer
ff732bf2-3802-4bfb-b540-4675249ec4ff
Fadhel, Nawfal
e73b96f2-bf15-40cb-9af5-23c10ea8e319
Argles, David
7dd3d276-b2b2-4fb2-a0e8-4058bb01fc37
Wills, Gary
3a594558-6921-4e82-8098-38cd8d4e8aa0

Ozulku, Omer, Fadhel, Nawfal, Argles, David and Wills, Gary (2014) Anomaly detection system: towards a framework for enterprise log management of security services. 2014 World Congress on Internet Security (WorldCIS), London, United Kingdom. 08 - 10 Dec 2014. pp. 97-102 . (doi:10.1109/WorldCIS.2014.7028175).

Record type: Conference or Workshop Item (Paper)

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.

Text
stamp.jsp_tp=&arnumber=7028175&tag=1 - Accepted Manuscript
Download (1kB)

More information

Published date: December 2014
Venue - Dates: 2014 World Congress on Internet Security (WorldCIS), London, United Kingdom, 2014-12-08 - 2014-12-10
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, organizations, security, web servers, anomaly detection, RESTful style log data collection, web usage mining algortithm
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 378768
URI: http://eprints.soton.ac.uk/id/eprint/378768
PURE UUID: b398451f-27d4-4308-be93-7388a832d393
ORCID for Nawfal Fadhel: ORCID iD orcid.org/0000-0002-1129-5217
ORCID for Gary Wills: ORCID iD orcid.org/0000-0001-5771-4088

Catalogue record

Date deposited: 22 Jul 2015 12:21
Last modified: 15 Mar 2024 02:51

Export record

Altmetrics

Contributors

Author: Omer Ozulku
Author: Nawfal Fadhel ORCID iD
Author: David Argles
Author: Gary Wills ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×