Earticle

초록

영어

Phishing attacks have surged with the expansion of the internet, becoming a major cause of personal information leaks. A significant challenge in phishing detection lies in the shared characteristics between phishing and benign webpages, as attackers intentionally design phishing pages to closely resemble benign ones. These shared features often result in ambiguous representations in the embedding space, which complicates accurate detection. To address this issue, we propose a method that introduces feature disentanglement into deep learning models for phishing webpage detection. By leveraging both URL and HTML data, our method employs a triplet loss function to better separate phishing and benign classes in the embedding space, thus reducing the overlap of shared features. This disentanglement effectively decreases the rate of false positives and false negatives. Experimental results show that our approach improves phishing detection accuracy by up to 9.85 percentage points and increases the F1 score by up to 11.92 percentage points compared to existing methods.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
III. PROPOSED METHOD
A. Character-based URL Analysis using CNN
B. Word-based URL Analysis using Transformer
C. HTML DOM Structure Analysis using GCN
D. Triplet Network for Webpage Disentanglement
IV. EXPERIMENTAL RESULTS
A. Dataset and Preprocessing
B. Confusion Matrix Analysis
C. Performance Comparison
D. t-SNE Visualization of Embeddings
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Jun-Ho Yoon [ Dept. of Computer Engineering Gyeongsang National University Republic of Korea ]
• Seok-Jun Buu [ Dept. of Computer Engineering Gyeongsang National University Republic of Korea ] Corresponding Author

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