Efficient Classification of Malicious URLs: M-BERT—A Modified BERT Variant for Enhanced Semantic Understanding

Abstract

Malicious websites present a substantial threat to the security and privacy of individuals using the internet. Traditional approaches for identifying these malicious sites have struggled to keep pace with evolving attack strategies. In recent years, language models have emerged as a potential solution for effectively detecting and categorizing malicious websites. This study introduces a novel Bidirectional Encoder Representations from Transformers (BERT) model, based on the Transformer encoder architecture, designed to capture pertinent characteristics of malicious web addresses. Additionally, large-scale language models are employed for training, dataset assessment, and interpretability analysis. The evaluation results demonstrate the effectiveness of the large language model in accurately classifying malicious websites, achieving an impressive precision rate of 94.42%. This performance surpasses that of existing language models. Furthermore, the interpretability analysis sheds light on the decision-making process of the model, enhancing our understanding of its classification outcomes. In conclusion, the proposed BERT model, built on the Transformer encoder architecture, exhibits robust performance and interpretability in the identification of malicious websites. It holds promise as a solution to bolster the security of network users and mitigate the risks associated with malicious online activities.

Keywords

• Natural language processing
• deep learning
• fraudulent URL classification