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Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm

Recent trends have revealed that SIP based IP-PBX DoS attacks contribute to most overall IP-PBX attacks which is resulting in loss of revenues and quality of service in telecommunication providers. IP-PBX face challenges in detecting and mitigating malicious traffic. In this research, Support Vector...

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Main Authors: Jama, Abdirisaq M., Khalifa, Othman Omran, Subramaniam, Nantha Kumar
Format: Article
Language:English
English
Published: Institute of Computing, International Journal of Communication Networks and Information Security (IJCNIS) 2021
Subjects:
T Technology (General)
T10.5 Communication of technical information
Online Access:http://irep.iium.edu.my/91807/7/91807_Novel%20Approach%20for%20IP-PBX%20Denial%20of%20Service%20Intrusion%20Detection%20using%20Support%20Vector%20Machine.pdf
http://irep.iium.edu.my/91807/13/91807_Novel%20approach%20for%20IP-PBX%20denial%20of%20service%20intrusion%20detection_Scopus.pdf
http://irep.iium.edu.my/91807/
https://www.ijcnis.org/index.php/ijcnis/article/view/4967
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spelling my.iium.irep.918072021-09-28T03:09:26Z http://irep.iium.edu.my/91807/ Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm Jama, Abdirisaq M. Khalifa, Othman Omran Subramaniam, Nantha Kumar T Technology (General) T10.5 Communication of technical information Recent trends have revealed that SIP based IP-PBX DoS attacks contribute to most overall IP-PBX attacks which is resulting in loss of revenues and quality of service in telecommunication providers. IP-PBX face challenges in detecting and mitigating malicious traffic. In this research, Support Vector Machine (SVM) machine learning detection & prevention algorithm were developed to detect this type of attacks Two other techniques were benchmarked decision tree and Naïve Bayes. The training phase of the machine learning algorithm used proposed real-time training datasets benchmarked with two training datasets from CICIDS and NSL-KDD. Proposed real-time training dataset for SVM algorithm achieved highest detection rate of 99.13% while decision tree and Naïve Bayes has 93.28% & 86.41% of attack detection rate, respectively. For CICIDS dataset, SVM algorithm achieved highest detection rate of 76.47% while decision tree and Naïve Bayes has 63.71% & 41.58% of detection rate, respectively. Using NSL-KDD training dataset, SVM achieved 65.17%, while decision tree and Naïve Bayes has 51.96% & 38.26% of detection rate, respectively. The time taken by the algorithms to classify the attack is very important. SVM gives less time (2.9 minutes) for detecting attacks while decision tree and naïve Bayes gives 13.6 minutes 26.2 minutes, respectively. Proposed SVM algorithm achieved the lowest false negative value of (87 messages) while decision table and Naïve Bayes achieved false negative messages of 672 and 1359, respectively Institute of Computing, International Journal of Communication Networks and Information Security (IJCNIS) 2021-08 Article PeerReviewed application/pdf en http://irep.iium.edu.my/91807/7/91807_Novel%20Approach%20for%20IP-PBX%20Denial%20of%20Service%20Intrusion%20Detection%20using%20Support%20Vector%20Machine.pdf application/pdf en http://irep.iium.edu.my/91807/13/91807_Novel%20approach%20for%20IP-PBX%20denial%20of%20service%20intrusion%20detection_Scopus.pdf Jama, Abdirisaq M. and Khalifa, Othman Omran and Subramaniam, Nantha Kumar (2021) Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm. International Journal of Communication Networks and Information Security, 13 (2). pp. 249-257. E-ISSN 2073-607X https://www.ijcnis.org/index.php/ijcnis/article/view/4967
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T Technology (General)
T10.5 Communication of technical information
spellingShingle T Technology (General)
T10.5 Communication of technical information
Jama, Abdirisaq M.
Khalifa, Othman Omran
Subramaniam, Nantha Kumar
Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm
description Recent trends have revealed that SIP based IP-PBX DoS attacks contribute to most overall IP-PBX attacks which is resulting in loss of revenues and quality of service in telecommunication providers. IP-PBX face challenges in detecting and mitigating malicious traffic. In this research, Support Vector Machine (SVM) machine learning detection & prevention algorithm were developed to detect this type of attacks Two other techniques were benchmarked decision tree and Naïve Bayes. The training phase of the machine learning algorithm used proposed real-time training datasets benchmarked with two training datasets from CICIDS and NSL-KDD. Proposed real-time training dataset for SVM algorithm achieved highest detection rate of 99.13% while decision tree and Naïve Bayes has 93.28% & 86.41% of attack detection rate, respectively. For CICIDS dataset, SVM algorithm achieved highest detection rate of 76.47% while decision tree and Naïve Bayes has 63.71% & 41.58% of detection rate, respectively. Using NSL-KDD training dataset, SVM achieved 65.17%, while decision tree and Naïve Bayes has 51.96% & 38.26% of detection rate, respectively. The time taken by the algorithms to classify the attack is very important. SVM gives less time (2.9 minutes) for detecting attacks while decision tree and naïve Bayes gives 13.6 minutes 26.2 minutes, respectively. Proposed SVM algorithm achieved the lowest false negative value of (87 messages) while decision table and Naïve Bayes achieved false negative messages of 672 and 1359, respectively
format Article
author Jama, Abdirisaq M.
Khalifa, Othman Omran
Subramaniam, Nantha Kumar
author_facet Jama, Abdirisaq M.
Khalifa, Othman Omran
Subramaniam, Nantha Kumar
author_sort Jama, Abdirisaq M.
title Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm
title_short Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm
title_full Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm
title_fullStr Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm
title_full_unstemmed Novel approach for IP-PBX denial of service intrusion detection using support vector machine algorithm
title_sort novel approach for ip-pbx denial of service intrusion detection using support vector machine algorithm
publisher Institute of Computing, International Journal of Communication Networks and Information Security (IJCNIS)
publishDate 2021
url http://irep.iium.edu.my/91807/7/91807_Novel%20Approach%20for%20IP-PBX%20Denial%20of%20Service%20Intrusion%20Detection%20using%20Support%20Vector%20Machine.pdf
http://irep.iium.edu.my/91807/13/91807_Novel%20approach%20for%20IP-PBX%20denial%20of%20service%20intrusion%20detection_Scopus.pdf
http://irep.iium.edu.my/91807/
https://www.ijcnis.org/index.php/ijcnis/article/view/4967
_version_ 1712284848267198464
score 13.211869

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