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...
Saved in:
| Main Authors: | , , |
|---|---|
| 格式: | Conference or Workshop Item |
| 出版: |
2013
|
| 在線閱讀: | 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/ |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | 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. |
|---|