International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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URL Filtering by Using Machine Learning

  • Saqib, Malik Najmus (Department of Cybersecurity, College of Computer Science and Engineering University of Jeddah)
  • Received : 2022.08.05
  • Published : 2022.08.30
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Abstract

The growth of technology nowadays has made many things easy for humans. These things are from everyday small task to more complex tasks. Such growth also comes with the illegal activities that are perform by using technology. These illegal activities can simple as displaying annoying message to big frauds. The easiest way for the attacker to perform such activities is to convenience user to click on the malicious link. It has been a great concern since a decay to classify URLs as malicious or benign. The blacklist has been used initially for that purpose and is it being used nowadays. It is efficient but has a drawback to update blacklist automatically. So, this method is replace by classification of URLs based on machine learning algorithms. In this paper we have use four machine learning classification algorithms to classify URLs as malicious or benign. These algorithms are support vector machine, random forest, n-nearest neighbor, and decision tree. The dataset that is used in this research has 36694 instances. A comparison of precision accuracy and recall values are shown for dataset with and without preprocessing.

Keywords

Acknowledgement

This work was funded by the University of Jeddah, Jeddah, Saudi Arabia, under grant No. (UJ-02-042-DR). The authors, therefore, acknowledge with thanks the University of Jeddah technical and financial support.

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