eprintid: 3265
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/32/65
datestamp: 2023-11-09 15:51:31
lastmod: 2023-11-09 15:51:31
status_changed: 2023-11-09 15:46:26
type: conference_item
metadata_visibility: show
creators_name: Said, A.M.
creators_name: Dominic, D.D.
creators_name: Faye, I.
title: Real-time network anomaly detection architecture based on frequent pattern mining technique
ispublished: pub
keywords: Anomalous behavior; Anomaly detection; Anomaly-based intrusion detection; Data stream; Frequent pattern mining; Network anomaly detection; Outlier Detection; Real-time networks, Data mining; Information systems; Network security; Statistics, Network architecture
note: cited By 2; Conference of 2013 International Conference on Research and Innovation in Information Systems, ICRIIS 2013 ; Conference Date: 27 November 2013 Through 28 November 2013; Conference Code:103283
abstract: Online network anomaly-based intrusion detection systems responsible about monitoring the novel anomalies. Network anomaly detection system architecture with a new outlier detection approach is presented in this paper. A new outlierness measurement is proposed which is based on frequent patterns technique and an approach for detecting outliers is introduced. The proposed approach features main advantages which are: effective and direct in detect the anomalous of the online traffic data; adaptive to underlying changes of the traffic streams. The empirical results exhibit a good detection for the new anomalous behavior and the accuracy performance of our proposed approach is approximately close to the static approach. © 2013 IEEE.
date: 2013
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897877635&doi=10.1109%2fICRIIS.2013.6716742&partnerID=40&md5=a0aef52b5faa06eb7a370b00b534d13c
id_number: 10.1109/ICRIIS.2013.6716742
full_text_status: none
publication: International Conference on Research and Innovation in Information Systems, ICRIIS
place_of_pub: Kuala Lumpur
pagerange: 392-397
refereed: TRUE
isbn: 9781479924875
issn: 23248149
citation:   Said, A.M. and Dominic, D.D. and Faye, I.  (2013) Real-time network anomaly detection architecture based on frequent pattern mining technique.  In: UNSPECIFIED.