A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number
Internet traffic classification is a beneficial technique in the direction of intrusion detection and network monitoring. After several years of searching, there are still many open problems in Internet traffic classification. The hybrid classifier combines more than one classification method to ide...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/24/12113 |
| _version_ | 1797506702014152704 |
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| author | Hamza Awad Hamza Ibrahim Omer Radhi A. L. Zuobi Awad M. Abaker Musab B. Alzghoul |
| author_facet | Hamza Awad Hamza Ibrahim Omer Radhi A. L. Zuobi Awad M. Abaker Musab B. Alzghoul |
| author_sort | Hamza Awad Hamza Ibrahim |
| collection | DOAJ |
| description | Internet traffic classification is a beneficial technique in the direction of intrusion detection and network monitoring. After several years of searching, there are still many open problems in Internet traffic classification. The hybrid classifier combines more than one classification method to identify Internet traffic. Using only one method to classify Internet traffic poses many risks. In addition, an online classifier is very important in order to manage threats on traffic such as denial of service, flooding attack and other similar threats. Therefore, this paper provides some information to differentiate between real and live internet traffic. In addition, this paper proposes a hybrid online classifier (HOC) system. HOC is based on two common classification methods, port-base and ML-base. HOC is able to perform an online classification since it can identify live Internet traffic at the same time as it is generated. HOC was used to classify three common Internet application classes, namely web, WhatsApp and Twitter. HOC produces more than 90% accuracy, which is higher than any individual classifiers. |
| first_indexed | 2024-03-10T04:36:17Z |
| format | Article |
| id | doaj.art-0380a6b7158e494e812cabe226bae408 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:36:17Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0380a6b7158e494e812cabe226bae4082023-11-23T03:43:25ZengMDPI AGApplied Sciences2076-34172021-12-0111241211310.3390/app112412113A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port NumberHamza Awad Hamza Ibrahim0Omer Radhi A. L. Zuobi1Awad M. Abaker2Musab B. Alzghoul3College of Computer at Al-Gunfudah, Umm Al-Qura University, Mecca 24382, Saudi ArabiaCollege of Computer at Al-Gunfudah, Umm Al-Qura University, Mecca 24382, Saudi ArabiaCollege of Computer at Al-Gunfudah, Umm Al-Qura University, Mecca 24382, Saudi ArabiaCollege of Computer at Al-Gunfudah, Umm Al-Qura University, Mecca 24382, Saudi ArabiaInternet traffic classification is a beneficial technique in the direction of intrusion detection and network monitoring. After several years of searching, there are still many open problems in Internet traffic classification. The hybrid classifier combines more than one classification method to identify Internet traffic. Using only one method to classify Internet traffic poses many risks. In addition, an online classifier is very important in order to manage threats on traffic such as denial of service, flooding attack and other similar threats. Therefore, this paper provides some information to differentiate between real and live internet traffic. In addition, this paper proposes a hybrid online classifier (HOC) system. HOC is based on two common classification methods, port-base and ML-base. HOC is able to perform an online classification since it can identify live Internet traffic at the same time as it is generated. HOC was used to classify three common Internet application classes, namely web, WhatsApp and Twitter. HOC produces more than 90% accuracy, which is higher than any individual classifiers.https://www.mdpi.com/2076-3417/11/24/12113Internet traffic classificationmachine learningclassification methodsport-based methodhybrid classifieronline Internet traffic classification |
| spellingShingle | Hamza Awad Hamza Ibrahim Omer Radhi A. L. Zuobi Awad M. Abaker Musab B. Alzghoul A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number Applied Sciences Internet traffic classification machine learning classification methods port-based method hybrid classifier online Internet traffic classification |
| title | A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number |
| title_full | A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number |
| title_fullStr | A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number |
| title_full_unstemmed | A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number |
| title_short | A Hybrid Online Classifier System for Internet Traffic Based on Statistical Machine Learning Approach and Flow Port Number |
| title_sort | hybrid online classifier system for internet traffic based on statistical machine learning approach and flow port number |
| topic | Internet traffic classification machine learning classification methods port-based method hybrid classifier online Internet traffic classification |
| url | https://www.mdpi.com/2076-3417/11/24/12113 |
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