Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures
Defacement attacks have long been considered one of prime threats to websites and web applications of companies, enterprises, and government organizations. Defacement attacks can bring serious consequences to owners of websites, including immediate interruption of website operations and damage of th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/8/2/35 |
| _version_ | 1828364758866198528 |
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| author | Xuan Dau Hoang Ngoc Tuong Nguyen |
| author_facet | Xuan Dau Hoang Ngoc Tuong Nguyen |
| author_sort | Xuan Dau Hoang |
| collection | DOAJ |
| description | Defacement attacks have long been considered one of prime threats to websites and web applications of companies, enterprises, and government organizations. Defacement attacks can bring serious consequences to owners of websites, including immediate interruption of website operations and damage of the owner reputation, which may result in huge financial losses. Many solutions have been researched and deployed for monitoring and detection of website defacement attacks, such as those based on checksum comparison, diff comparison, DOM tree analysis, and complicated algorithms. However, some solutions only work on static websites and others demand extensive computing resources. This paper proposes a hybrid defacement detection model based on the combination of the machine learning-based detection and the signature-based detection. The machine learning-based detection first constructs a detection profile using training data of both normal and defaced web pages. Then, it uses the profile to classify monitored web pages into either normal or attacked. The machine learning-based component can effectively detect defacements for both static pages and dynamic pages. On the other hand, the signature-based detection is used to boost the model’s processing performance for common types of defacements. Extensive experiments show that our model produces an overall accuracy of more than 99.26% and a false positive rate of about 0.27%. Moreover, our model is suitable for implementation of a real-time website defacement monitoring system because it does not demand extensive computing resources. |
| first_indexed | 2024-04-14T05:21:31Z |
| format | Article |
| id | doaj.art-6bc1db8b62474f0da4c11cb55ab6dd08 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-04-14T05:21:31Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-6bc1db8b62474f0da4c11cb55ab6dd082022-12-22T02:10:10ZengMDPI AGComputers2073-431X2019-05-01823510.3390/computers8020035computers8020035Detecting Website Defacements Based on Machine Learning Techniques and Attack SignaturesXuan Dau Hoang0Ngoc Tuong Nguyen1Posts and Telecommunications Institute of Technology, Hanoi 100000, VietnamPosts and Telecommunications Institute of Technology, Hanoi 100000, VietnamDefacement attacks have long been considered one of prime threats to websites and web applications of companies, enterprises, and government organizations. Defacement attacks can bring serious consequences to owners of websites, including immediate interruption of website operations and damage of the owner reputation, which may result in huge financial losses. Many solutions have been researched and deployed for monitoring and detection of website defacement attacks, such as those based on checksum comparison, diff comparison, DOM tree analysis, and complicated algorithms. However, some solutions only work on static websites and others demand extensive computing resources. This paper proposes a hybrid defacement detection model based on the combination of the machine learning-based detection and the signature-based detection. The machine learning-based detection first constructs a detection profile using training data of both normal and defaced web pages. Then, it uses the profile to classify monitored web pages into either normal or attacked. The machine learning-based component can effectively detect defacements for both static pages and dynamic pages. On the other hand, the signature-based detection is used to boost the model’s processing performance for common types of defacements. Extensive experiments show that our model produces an overall accuracy of more than 99.26% and a false positive rate of about 0.27%. Moreover, our model is suitable for implementation of a real-time website defacement monitoring system because it does not demand extensive computing resources.https://www.mdpi.com/2073-431X/8/2/35defacement attacks of websitesdefacement monitoring and detectionanomaly-based defacement detectionsignature-based defacement detectionmachine learning-based defacement detection |
| spellingShingle | Xuan Dau Hoang Ngoc Tuong Nguyen Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures Computers defacement attacks of websites defacement monitoring and detection anomaly-based defacement detection signature-based defacement detection machine learning-based defacement detection |
| title | Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures |
| title_full | Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures |
| title_fullStr | Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures |
| title_full_unstemmed | Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures |
| title_short | Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures |
| title_sort | detecting website defacements based on machine learning techniques and attack signatures |
| topic | defacement attacks of websites defacement monitoring and detection anomaly-based defacement detection signature-based defacement detection machine learning-based defacement detection |
| url | https://www.mdpi.com/2073-431X/8/2/35 |
| work_keys_str_mv | AT xuandauhoang detectingwebsitedefacementsbasedonmachinelearningtechniquesandattacksignatures AT ngoctuongnguyen detectingwebsitedefacementsbasedonmachinelearningtechniquesandattacksignatures |