Earticle

초록

영어

Phishing attacks increasingly exploit mobile platforms and target users communicating in lowresource or code-mixed languages, posing challenges to traditional centralized detection systems. This study proposes hybrid knowledge distillation and federated learning framework for real-time, on-device phishing detection. The approach integrates a fine-tuned XLM-RoBERTa "teacher" model with a compact MobileBERT "student" model, distilled to achieve near teacher-level performance while enabling efficient offline inference. The distilled MobileBERT model, converted to ONNX for platform portability, achieved a fivefold reduction in size while preserving 98% of the original model’s accuracy. We conducted zero-shot evaluations on Korean, Spanish, and Turkish datasets to guarantee crosslinguistic robustness, and consistently obtained good accuracy and recall rates. Moreover, privacy-preserving updates were made possible using a federated learning simulation, which permits decentralized model enhancements without data exchange. The suggested architecture offers a cost-effective, expandable, and privacy-conscious approach to phishing detection in scenarios with limited connection and linguistic diversity.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. METHODOLOGY
A. Data Acquisition and Teacher Model
B. Student Model: Knowledge Distillation and Conversion
C. Federated Learning for On-Device Updates
IV. RESULTS AND DISCUSSION
A. Comparison with Baseline Models
B. Teacher vs. Student Performance
C. Large-Scale Cross-Lingual Validation
D. On-Device Performance Validation
E. Federated Learning Simulation
F. Explainability and Model Transparency.
V. CONCLUSION
VI. DATA AVAILABILITY
ACKNOWLEDGMENT
REFERENCES

저자

• Mwania Vincent Ngundi [ Computer Engineering, Chosun University P.O. Box 61452 Gwangju, South Korea ]
• Hyoung-Ju Kim [ SW Human Resource Development Foundation, Chosun University P.O. Box 61452 Gwangju, South Korea ]
• Pankoo Kim [ Department of AI Software Computer Engineering Chosun University P.o Box 61452 Gwangju, South Korea ]

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