Earticle

초록

영어

The rise of cryptocurrencies has also led to an increase in fraudulent activities, posing challenges in fraud detection on decentralized platforms like Ethereum. This issue is particularly pronounced in decentralized environments like Ethereum, where new transaction patterns continue to emerge. In this dynamic and changing environment, it is important to address the problem of concept drift, which refers to the continuous evolution of data patterns. To address the dynamic nature of these fraud patterns, we propose an automated hyperparameter optimization (HPO) approach using Proximal Policy Optimization (PPO). Unlike traditional HPO methods, PPO efficiently navigates the complex hyperparameter space, adapting to evolving fraud schemes with minimal human intervention. Our method enhances the adaptability and robustness of fraud detection models, effectively improving detection accuracy. Experimental results demonstrate that PPO outperforms existing HPO techniques, offering a more flexible and powerful tool for maintaining the performance of fraud detection systems in the rapidly changing cryptocurrency landscape.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. PROPOSED METHOD
A. Proximal Policy Optimization Algorithm In HPO
B. Scam Node Detection Model for the Ethereum Transaction Graph
IV. EXPERIMENTS
A. Ethereum Transaction Dataset and Implementation
B. Comparison DGTSD to Other Models
C. Performance of PPO's reward function vs. number of steps in HPO
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

저자

• Su-Hwan Choi [ Dept. of Computer Science and Engineering Gyeongsang National University Jinju. Republic of Korea ]
• Seok-Jun Buu [ Dept. of Computer Science and Engineering Gyeongsang National University Jinju. Republic of Korea ] Corresponding Author

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