Lightweight approach for malicious domain detection using machine learning
The web-based attacks use the vulnerabilities of the end users and their system and perform malicious activities such as stealing sensitive information, injecting malwares, redirecting to malicious sites without their knowledge. Malicious website links are spread through social media posts, emails a...
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| Veröffentlicht in: | Nauchno-tekhnicheskiĭ vestnik informat͡s︡ionnykh tekhnologiĭ, mekhaniki i optiki Jg. 22; H. 2; S. 262 - 268 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
ITMO University
01.12.2024
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| Schlagworte: | |
| ISSN: | 2226-1494, 2500-0373 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | The web-based attacks use the vulnerabilities of the end users and their system and perform malicious activities such as stealing sensitive information, injecting malwares, redirecting to malicious sites without their knowledge. Malicious website links are spread through social media posts, emails and messages. The victim can be an individual or an organization and it creates huge money loss every year. Recent Internet Security report states that 83 % of systems in the internet are infected by the malware during the last 12 months due to the users who do not aware of the malicious URL (Uniform Resource Locators) and its impacts. There are some methods to detect and prevent the access malicious domain name in the internet. Blacklist-based approaches, heuristic-based methods, and machine/deep learning-based methods are the three categories. This study provides a machine learning-based lightweight solution to classify malicious domain names. Most of the existing research work is focused on increasing the number of features for better classification accuracy. But the proposed approach uses fewer number of features which include lexical, content based, bag of words, popularity features for malicious domain classification. Result of the experiment shows that the proposed approach performs better than the existing one. |
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| ISSN: | 2226-1494 2500-0373 |
| DOI: | 10.17586/2226-1494-2022-22-2-262-268 |