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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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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my.utp.eprints.324942022-03-29T14:04:09Z Real-time network anomaly detection architecture based on frequent pattern mining technique Said, A.M. Dominic, D.D. Faye, I. 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. 2013 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897877635&doi=10.1109%2fICRIIS.2013.6716742&partnerID=40&md5=a0aef52b5faa06eb7a370b00b534d13c Said, A.M. and Dominic, D.D. and Faye, I. (2013) Real-time network anomaly detection architecture based on frequent pattern mining technique. In: UNSPECIFIED. http://eprints.utp.edu.my/32494/ |
| institution |
Universiti Teknologi Petronas |
| building |
UTP Resource Centre |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Petronas |
| content_source |
UTP Institutional Repository |
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http://eprints.utp.edu.my/ |
| description |
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. |
| format |
Conference or Workshop Item |
| author |
Said, A.M. Dominic, D.D. Faye, I. |
| spellingShingle |
Said, A.M. Dominic, D.D. Faye, I. Real-time network anomaly detection architecture based on frequent pattern mining technique |
| author_facet |
Said, A.M. Dominic, D.D. Faye, I. |
| author_sort |
Said, A.M. |
| title |
Real-time network anomaly detection architecture based on frequent pattern mining technique |
| title_short |
Real-time network anomaly detection architecture based on frequent pattern mining technique |
| title_full |
Real-time network anomaly detection architecture based on frequent pattern mining technique |
| title_fullStr |
Real-time network anomaly detection architecture based on frequent pattern mining technique |
| title_full_unstemmed |
Real-time network anomaly detection architecture based on frequent pattern mining technique |
| title_sort |
real-time network anomaly detection architecture based on frequent pattern mining technique |
| publishDate |
2013 |
| url |
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/ |
| _version_ |
1738657394717622272 |
| score |
13.250246 |