Malicious website detection
Malicious websites are among the major security threats on the Internet. This threat has been existing for years yet the best solution to overcome it has not been implemented by many people. Most of the existing methods for detecting malicious websites are focusing towards specific attacks. However,...
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| Format: | Undergraduates Project Papers |
| Language: | English |
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2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/27110/ http://umpir.ump.edu.my/id/eprint/27110/ http://umpir.ump.edu.my/id/eprint/27110/1/Malicious%20website.pdf |
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ump-27110 |
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eprints |
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ump-271102019-12-24T04:59:26Z http://umpir.ump.edu.my/id/eprint/27110/ Malicious website detection Ong, Vienna Lee QA75 Electronic computers. Computer science Malicious websites are among the major security threats on the Internet. This threat has been existing for years yet the best solution to overcome it has not been implemented by many people. Most of the existing methods for detecting malicious websites are focusing towards specific attacks. However, attacks are getting more complex and hackers have become more sophisticated with their blended techniques to evade existing countermeasures. In this thesis, a method will be introduced. With previous existing methods in consideration, the method to use for this project is by using heuristic-based detection with machine learning technique and the feature that will be used together with the technique is URL based feature. The purpose of this method is to classify benign and malicious website using machine learning and then will automatically detect malicious websites. By using this method is also to ensure the detection accuracy is high and all malicious websites can be detected even the latest one prompted by the hackers. In conclusion, the proposed method is the most effective way to detect malicious websites and easy to be implemented. 2019-01 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/27110/1/Malicious%20website.pdf Ong, Vienna Lee (2019) Malicious website detection. Faculty of Computer System & Software Engineering, Universiti Malaysia Pahang. http://fypro.ump.edu.my/ethesis/index.php |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
QA75 Electronic computers. Computer science |
| spellingShingle |
QA75 Electronic computers. Computer science Ong, Vienna Lee Malicious website detection |
| description |
Malicious websites are among the major security threats on the Internet. This threat has been existing for years yet the best solution to overcome it has not been implemented by many people. Most of the existing methods for detecting malicious websites are focusing towards specific attacks. However, attacks are getting more complex and hackers have become more sophisticated with their blended techniques to evade existing countermeasures. In this thesis, a method will be introduced. With previous existing methods in consideration, the method to use for this project is by using heuristic-based detection with machine learning technique and the feature that will be used together with the technique is URL based feature. The purpose of this method is to classify benign and malicious website using machine learning and then will automatically detect malicious websites. By using this method is also to ensure the detection accuracy is high and all malicious websites can be detected even the latest one prompted by the hackers. In conclusion, the proposed method is the most effective way to detect malicious websites and easy to be implemented. |
| format |
Undergraduates Project Papers |
| author |
Ong, Vienna Lee |
| author_facet |
Ong, Vienna Lee |
| author_sort |
Ong, Vienna Lee |
| title |
Malicious website detection |
| title_short |
Malicious website detection |
| title_full |
Malicious website detection |
| title_fullStr |
Malicious website detection |
| title_full_unstemmed |
Malicious website detection |
| title_sort |
malicious website detection |
| publishDate |
2019 |
| url |
http://umpir.ump.edu.my/id/eprint/27110/ http://umpir.ump.edu.my/id/eprint/27110/ http://umpir.ump.edu.my/id/eprint/27110/1/Malicious%20website.pdf |
| first_indexed |
2023-09-18T22:42:31Z |
| last_indexed |
2023-09-18T22:42:31Z |
| _version_ |
1777417009906057216 |