relation: https://khub.utp.edu.my/scholars/3265/
title: Real-time network anomaly detection architecture based on frequent pattern mining technique
creator: Said, A.M.
creator: Dominic, D.D.
creator: Faye, I.
description: 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
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897877635&doi=10.1109%2fICRIIS.2013.6716742&partnerID=40&md5=a0aef52b5faa06eb7a370b00b534d13c
relation: 10.1109/ICRIIS.2013.6716742
identifier: 10.1109/ICRIIS.2013.6716742