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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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Main Authors: Yusof, A. R., Udzir, N. I., Selamat, A.
格式: Conference or Workshop Item
出版: Springer Verlag 2016
主题:
QA Mathematics
QA75 Electronic computers. Computer science
在线阅读: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
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id my.utm.73489
record_format eprints
spelling 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
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
QA75 Electronic computers. Computer science
spellingShingle 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
description 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
score 13.250246

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