TY  - CONF
N2  - 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.
N1  - 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
SP  - 392
ID  - scholars3265
TI  - Real-time network anomaly detection architecture based on frequent pattern mining technique
KW  - Anomalous behavior; Anomaly detection; Anomaly-based intrusion detection; Data stream; Frequent pattern mining; Network anomaly detection; Outlier Detection; Real-time networks
KW  -  Data mining; Information systems; Network security; Statistics
KW  -  Network architecture
CY  - Kuala Lumpur
AV  - none
A1  - Said, A.M.
A1  - Dominic, D.D.
A1  - Faye, I.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897877635&doi=10.1109%2fICRIIS.2013.6716742&partnerID=40&md5=a0aef52b5faa06eb7a370b00b534d13c
EP  - 397
Y1  - 2013///
SN  - 23248149
ER  -