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A Machine Learning Based Three-Step Framework for Malicious URL Detection
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AbstractIn order to solve the shortcomings of using blacklist method to detect malicious URLs, such as slow update speed, the research of using machine learning to detect malicious URLs increasing. These research have proposed their own methods and obtained great accuracy, but the summary research on malicious URLs detection is insufficient. In this paper, we propose a three-step framework for malicious URLs detection, and we overview 14 related works by our three-step framework and find that almost all research on malicious URLs detection using machine learning can be classified by the three-step framework. We evaluate some machine learning models and context-considering methods and their suitability by our three-step framework. According to the results, we verify the importance of considering context and find that context-considering embedding methods are more important and the malicious URLs detection accuracy improved with context-considering methods.
Title: A Machine Learning Based Three-Step Framework for Malicious URL Detection
Description:
AbstractIn order to solve the shortcomings of using blacklist method to detect malicious URLs, such as slow update speed, the research of using machine learning to detect malicious URLs increasing.
These research have proposed their own methods and obtained great accuracy, but the summary research on malicious URLs detection is insufficient.
In this paper, we propose a three-step framework for malicious URLs detection, and we overview 14 related works by our three-step framework and find that almost all research on malicious URLs detection using machine learning can be classified by the three-step framework.
We evaluate some machine learning models and context-considering methods and their suitability by our three-step framework.
According to the results, we verify the importance of considering context and find that context-considering embedding methods are more important and the malicious URLs detection accuracy improved with context-considering methods.
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