【Author】
Lee, Chaehyeon; Maharjan, Sajan; Ko, Kyungchan; Hong, James Won-Ki
【Source】BLOCKCHAIN AND TRUSTWORTHY SYSTEMS, BLOCKSYS 2019
【Abstract】As an emergent electronic payment system, Bitcoin has attracted attention for its desirable features such as disintermediation, decentralization, and tamper-proof recording of data. The Bitcoin network also employs public key cryptography to prevent the disclosure of information related to participating users. Although the public key cryptography ensures the privacy and hides the true identity of users in the Bitcoin network, it has recently been abused for illegal activities that have tarnished the charm of this novel technology. Detecting the illegal transactions associated with illicit activities in Bitcoin is therefore imperative. This paper proposes a machine-learning based approach that classifies Bitcoin transactions as illegal or legal. The detected illegal transactions can be excluded from the subsequent block, promoting user acceptance and adoption of the Bitcoin technology.
【Keywords】Bitcoin; Illegal transaction detection; Classification; Bitcoin transaction analysis; Transaction feature extraction
【摘要】比特币作为一种新兴的电子支付系统，以其去中介化、去中心化、数据记录防篡改等优点而备受关注。比特币网络还采用公钥加密技术，以防止参与用户的相关信息被泄露。虽然公钥加密在比特币网络中确保了隐私并隐藏了用户的真实身份，但最近它被滥用于非法活动，玷污了这项新技术的魅力。因此，检测与比特币非法活动相关的非法交易是势在必行的。本文提出了一种基于机器学习的方法，将比特币交易划分为非法或合法。检测到的非法交易可以被排除在后续的区块之外，促进用户接受和采用比特币技术。
【关键词】比特币;非法交易检测;分类;比特币交易分析;事务特征提取
评论