@inproceedings{scholars3265,
            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},
             doi = {10.1109/ICRIIS.2013.6716742},
            year = {2013},
         address = {Kuala Lumpur},
           title = {Real-time network anomaly detection architecture based on frequent pattern mining technique},
         journal = {International Conference on Research and Innovation in Information Systems, ICRIIS},
           pages = {392--397},
            isbn = {9781479924875},
            issn = {23248149},
          author = {Said, A. M. and Dominic, D. D. and Faye, I.},
        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. {\^A}{\copyright} 2013 IEEE.},
        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},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897877635&doi=10.1109\%2fICRIIS.2013.6716742&partnerID=40&md5=a0aef52b5faa06eb7a370b00b534d13c}
}