Phishing attacks are among emerging security issues that recently draws
significant attention in the cyber security community. There are numerous
existing approaches for phishing URL detection. However, malicious URL
detection is still a research hotspot because attackers can bypass newly
introduced detection mechanisms by changing their tactics. This paper will
introduce a transformer-based malicious URL detection model, which has
significant accuracy and outperforms current detection methods. We conduct
experiments and compare them with six existing classical detection models.
Experiments demonstrate that our transformer-based model is the best performing
model from all perspectives among the seven models and achieves 97.3 % of
detection accuracy.

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