%0 Conference Paper
%A Said, A.M.
%A Dominic, D.D.
%A Faye, I.
%D 2013
%F scholars:3265
%K 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
%P 392-397
%R 10.1109/ICRIIS.2013.6716742
%T Real-time network anomaly detection architecture based on frequent pattern mining technique
%U https://khub.utp.edu.my/scholars/3265/
%X 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.
%Z 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