Malicious URL and Intrusion Detection using Machine Learning
Cyberattacks are becoming increasingly sophisticated and evolving danger to the Web users. Therefore, addressing the growing threat of cyberattacks and providing automated solutions became a necessity. The purpose of this paper is to use machine learning (ML) techniques for malicious websites detect...
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2024
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| Online Access: | https://hdl.handle.net/11073/26366 |
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| _version_ | 1864513437585899520 |
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| author | Hamza, Amr |
| author2 | Hammam, Farah Abouzeid, Medhat Ahmed, Mohammad Arsalan Dhou, Salam Aloul, Fadi |
| author2_role | author author author author author |
| author_facet | Hamza, Amr Hammam, Farah Abouzeid, Medhat Ahmed, Mohammad Arsalan Dhou, Salam Aloul, Fadi |
| author_role | author |
| dc.creator.none.fl_str_mv | Hamza, Amr Hammam, Farah Abouzeid, Medhat Ahmed, Mohammad Arsalan Dhou, Salam Aloul, Fadi |
| dc.date.none.fl_str_mv | 2024 2025-09-30T07:13:54Z 2025-09-30T07:13:54Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | A. Hamza, F. Hammam, M. Abouzeid, M. A. Ahmed, S. Dhou and F. Aloul, "Malicious URL and Intrusion Detection using Machine Learning," 2024 International Conference on Information Networking (ICOIN), Ho Chi Minh City, Vietnam, 2024, pp. 795-800, doi: 10.1109/ICOIN59985.2024.10572207. 979-8-3503-3094-6 1976-7684 https://hdl.handle.net/11073/26366 10.1109/ICOIN59985.2024.10572207 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | IEEE xplore |
| dc.relation.none.fl_str_mv | https://ieeexplore.ieee.org/abstract/document/10572207 |
| dc.subject.none.fl_str_mv | Malicious URLs Machine learning System penetration Intrusion detection |
| dc.title.none.fl_str_mv | Malicious URL and Intrusion Detection using Machine Learning |
| dc.type.none.fl_str_mv | Peer-Reviewed Preprint info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | Cyberattacks are becoming increasingly sophisticated and evolving danger to the Web users. Therefore, addressing the growing threat of cyberattacks and providing automated solutions became a necessity. The purpose of this paper is to use machine learning (ML) techniques for malicious websites detection and classification, and intrusion detection. Different ML algorithms were applied, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). Two datasets were utilized to train the MLmodels. The first dataset contains two classes of websites: “malicious” and “benign”. The second dataset has six classes of different network intrusion cyber-attacks: “normal”, “blackhole”, “TCP-SYN”, “PortScan”, “Diversion”, and “Overflow”. Experimental results demonstrated that the ML algorithms were able to achieve high accuracy in predicting website maliciousness and intrusion detection. Using the first dataset, DT KNN, and SVM classifiers exhibited the best performance for detecting malicious URLs with accuracies over 99%. Using the second dataset, the DT classifier proved most suitable for intrusion detection, achieving an accuracy of 95%. This paper suggests the integration of ML techniques into online security systems to enhance their efficacy in detecting and preventing cyber threats. |
| format | article |
| id | aus_9ee6a2b495ba0b92de1c88671ae302bf |
| identifier_str_mv | A. Hamza, F. Hammam, M. Abouzeid, M. A. Ahmed, S. Dhou and F. Aloul, "Malicious URL and Intrusion Detection using Machine Learning," 2024 International Conference on Information Networking (ICOIN), Ho Chi Minh City, Vietnam, 2024, pp. 795-800, doi: 10.1109/ICOIN59985.2024.10572207. 979-8-3503-3094-6 1976-7684 10.1109/ICOIN59985.2024.10572207 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/26366 |
| publishDate | 2024 |
| publisher.none.fl_str_mv | IEEE xplore |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Malicious URL and Intrusion Detection using Machine LearningHamza, AmrHammam, FarahAbouzeid, MedhatAhmed, Mohammad ArsalanDhou, SalamAloul, FadiMalicious URLsMachine learningSystem penetrationIntrusion detectionCyberattacks are becoming increasingly sophisticated and evolving danger to the Web users. Therefore, addressing the growing threat of cyberattacks and providing automated solutions became a necessity. The purpose of this paper is to use machine learning (ML) techniques for malicious websites detection and classification, and intrusion detection. Different ML algorithms were applied, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). Two datasets were utilized to train the MLmodels. The first dataset contains two classes of websites: “malicious” and “benign”. The second dataset has six classes of different network intrusion cyber-attacks: “normal”, “blackhole”, “TCP-SYN”, “PortScan”, “Diversion”, and “Overflow”. Experimental results demonstrated that the ML algorithms were able to achieve high accuracy in predicting website maliciousness and intrusion detection. Using the first dataset, DT KNN, and SVM classifiers exhibited the best performance for detecting malicious URLs with accuracies over 99%. Using the second dataset, the DT classifier proved most suitable for intrusion detection, achieving an accuracy of 95%. This paper suggests the integration of ML techniques into online security systems to enhance their efficacy in detecting and preventing cyber threats.IEEE xplore2025-09-30T07:13:54Z2025-09-30T07:13:54Z2024Peer-ReviewedPreprintinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfA. Hamza, F. Hammam, M. Abouzeid, M. A. Ahmed, S. Dhou and F. Aloul, "Malicious URL and Intrusion Detection using Machine Learning," 2024 International Conference on Information Networking (ICOIN), Ho Chi Minh City, Vietnam, 2024, pp. 795-800, doi: 10.1109/ICOIN59985.2024.10572207.979-8-3503-3094-61976-7684https://hdl.handle.net/11073/2636610.1109/ICOIN59985.2024.10572207en_UShttps://ieeexplore.ieee.org/abstract/document/10572207oai:repository.aus.edu:11073/263662025-09-30T11:38:40Z |
| spellingShingle | Malicious URL and Intrusion Detection using Machine Learning Hamza, Amr Malicious URLs Machine learning System penetration Intrusion detection |
| status_str | publishedVersion |
| title | Malicious URL and Intrusion Detection using Machine Learning |
| title_full | Malicious URL and Intrusion Detection using Machine Learning |
| title_fullStr | Malicious URL and Intrusion Detection using Machine Learning |
| title_full_unstemmed | Malicious URL and Intrusion Detection using Machine Learning |
| title_short | Malicious URL and Intrusion Detection using Machine Learning |
| title_sort | Malicious URL and Intrusion Detection using Machine Learning |
| topic | Malicious URLs Machine learning System penetration Intrusion detection |
| url | https://hdl.handle.net/11073/26366 |