Massive multiplayer online role-playing games (MMORPG) have been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the balance of the game economy ecosystem and damaging the game experience. Therefore, some studies have emerged to address the problem of RMT detection. However, they cannot well handle the label uncertainty problem in practice, where there are only labeled RMT samples (positive samples) and unlabeled samples, which could either be RMT samples or normal transactions (negative samples). Meanwhile, the trading relationship between RMTers is modeled in a simple way, leading to some normal transactions being falsely classified as RMT. In this article, we propose PU-Detector, a novel framework based on PU learning (learning from positive and unlabeled data) for RMT detection, considering the fact that there are only labeled RMT samples and other unlabeled transactions. We first automatically estimate the likelihood of one transaction being RMT by developing an improved PU learning method and proposing an assessment rule. Sequentially, we use the estimated likelihood as edge weight to construct a trading graph to learn trader representation. Then, with the trader representations and basic trading features, we detect RMT samples by the improved PU learning method. PU-Detector is evaluated on a large-scale real world dataset consisting of 33,809,956 transaction logs generated by 43,217 unique players. Compared with other approaches, it achieves the state-of-the-art performance and demonstrates its advantages in detecting underlying RMT samples.

大型多人在线角色扮演游戏（MMORPG）已成为最受欢迎和令人兴奋的网络游戏之一。近年来，一种被称为真实货币交易（RMT）的作弊现象出现，并以多种方式损害了幻想世界。 RMT是通过出售游戏内物品、货币甚至角色来赚取真钱，打破了游戏经济生态系统的平衡，损害了游戏体验。因此，出现了一些研究来解决RMT检测问题。然而，它们在实践中不能很好地处理标签不确定性问题，其中只有标记的RMT样本（正样本）和未标记的样本，这些样本可能是RMT样本，也可能是正常交易（负样本）。同时，RMTers之间的交易关系建模简单，导致一些正常交易被错误地归类为RMT。在本文中，考虑到只有标记的 RMT 样本和其他未标记的交易，我们提出了 PU-Detector，这是一种基于 PU 学习（从正数据和未标记数据中学习）进行 RMT 检测的新颖框架。我们首先通过开发改进的 PU 学习方法并提出评估规则来自动估计一笔交易被 RMT 的可能性。接下来，我们使用估计的可能性作为边权重来构建交易图来学习交易者表示。然后，根据交易者表征和基本交易特征，我们通过改进的PU学习方法检测RMT样本。 PU-Detector 在大规模现实世界数据集上进行评估，该数据集由 43,217 个独特玩家生成的 33,809,956 条交易日志组成。与其他方法相比，它实现了最先进的性能，并展示了其在检测底层 RMT 样本方面的优势。