Real-time network anomaly detection architecture based on frequent pattern mining technique
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 tec...
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| 主要な著者: | , , |
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| フォーマット: | Conference or Workshop Item |
| 出版事項: |
2013
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| オンライン･アクセス: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897877635&doi=10.1109%2fICRIIS.2013.6716742&partnerID=40&md5=a0aef52b5faa06eb7a370b00b534d13c http://eprints.utp.edu.my/32494/ |
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| 要約: | 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. |
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