An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack
Recently, damage caused by DDoS attacks increases year by year. Along with the advancement of communication technology, this kind of attack also evolves and it has become more complicated and hard to detect using flash crowd agent, slow rate attack and also amplification attack that exploits a vulne...
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2016
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my.utm.734892017-11-20T08:43:00Z http://eprints.utm.my/id/eprint/73489/ An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack Yusof, A. R. Udzir, N. I. Selamat, A. QA Mathematics QA75 Electronic computers. Computer science Recently, damage caused by DDoS attacks increases year by year. Along with the advancement of communication technology, this kind of attack also evolves and it has become more complicated and hard to detect using flash crowd agent, slow rate attack and also amplification attack that exploits a vulnerability in DNS server. Fast detection of the DDoS attack, quick response mechanisms and proper mitigation are a must for an organization. An investigation has been performed on DDoS attack and it analyzes the details of its phase using machine learning technique to classify the network status. In this paper, we propose a hybrid KNN-SVM method on classifying, detecting and predicting the DDoS attack. The simulation result showed that each phase of the attack scenario is partitioned well and we can detect precursors of DDoS attack as well as the attack itself. Springer Verlag 2016 Conference or Workshop Item PeerReviewed Yusof, A. R. and Udzir, N. I. and Selamat, A. (2016) An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack. In: 29th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2016, 2 - 4 Aug 2016, Morioka, Japan. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978792974&doi=10.1007%2f978-3-319-42007-3_9&partnerID=40&md5=cbb5cd7ae07e6911e782a3ac237a5a30 |
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QA Mathematics QA75 Electronic computers. Computer science Yusof, A. R. Udzir, N. I. Selamat, A. An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack |
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Recently, damage caused by DDoS attacks increases year by year. Along with the advancement of communication technology, this kind of attack also evolves and it has become more complicated and hard to detect using flash crowd agent, slow rate attack and also amplification attack that exploits a vulnerability in DNS server. Fast detection of the DDoS attack, quick response mechanisms and proper mitigation are a must for an organization. An investigation has been performed on DDoS attack and it analyzes the details of its phase using machine learning technique to classify the network status. In this paper, we propose a hybrid KNN-SVM method on classifying, detecting and predicting the DDoS attack. The simulation result showed that each phase of the attack scenario is partitioned well and we can detect precursors of DDoS attack as well as the attack itself. |
format |
Conference or Workshop Item |
author |
Yusof, A. R. Udzir, N. I. Selamat, A. |
author_facet |
Yusof, A. R. Udzir, N. I. Selamat, A. |
author_sort |
Yusof, A. R. |
title |
An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack |
title_short |
An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack |
title_full |
An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack |
title_fullStr |
An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack |
title_full_unstemmed |
An evaluation on KNN-SVM algorithm for detection and prediction of DDoS attack |
title_sort |
evaluation on knn-svm algorithm for detection and prediction of ddos attack |
publisher |
Springer Verlag |
publishDate |
2016 |
url |
http://eprints.utm.my/id/eprint/73489/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978792974&doi=10.1007%2f978-3-319-42007-3_9&partnerID=40&md5=cbb5cd7ae07e6911e782a3ac237a5a30 |
_version_ |
1643656674574073856 |
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13.213208 |