기계학습을 활용한 이더리움 미확인 스마트 컨트랙트 자동 분류 방안
Automated Classification of Unknown Smart Contracts of Ethereum Using Machine Learning
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참고문헌
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초록
암호화폐를 위해 개발된 블록체인 시스템은 탈중앙화, 분산원장 및 부분적 실명은닉성의 특징을 가지고 있어 최근다양한 분야에서 적용이 시도되고 있다. 그 중 부분적 실명은닉성은 사용자 프라이버시를 강력히 보장하지만 범죄악용 등 부작용 또한 나타나고 있어 이를 공격하기 위한 방안들이 지속 연구되어 왔다. 본 연구에서는 2세대 암호화폐의 대표인 이더리움 블록체인 시스템에서의 사용자 행위 식별을 위해 기계학습을 활용한 미확인 스마트 컨트랙트 기능 및 디자인 패턴의 자동 분류 방안에 대하여 제안한다.
Abstract
A blockchain system developed for crypto-currency has attractive characteristics, such as de-centralization, distributedledger, and partial anonymity, making itself adopted in various fields. Among those characteristics, partial anonymity stronglyassures privacy of users, but side effects such as abuse of crime are also appearing, and so countermeasures forcircumventing such abuse have been studied continuously. In this paper, we propose a machine-learning based method forclassifying smart contracts in Ethereum regarding their functions and design patterns and for identifying user behaviorsaccording to them.
참고문헌 (16)
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