无监督机器学习在游戏反欺诈领域的应用研究

doi:10.3969/j.issn.1671-1122.2020.09.007

信息网络安全 ›› 2020, Vol. 20 ›› Issue (9): 32-36.doi: 10.3969/j.issn.1671-1122.2020.09.007

无监督机器学习在游戏反欺诈领域的应用研究

徐瑜, 周游, 林璐(), 张聪

杭州浮云网络科技有限公司,杭州 310000

收稿日期:2020-07-16 出版日期:2020-09-10 发布日期:2020-10-15
通讯作者: 林璐 E-mail:lu@fuyuncn.com
作者简介:徐瑜（1987—）,男,浙江,硕士,主要研究方向为网络安全、自然语言处理|周游（1975—）,男,浙江,硕士,主要研究方向为大数据、人工智能、区块链技术|林璐（1995—）,女,福建,硕士,主要研究方向为数据挖掘、人工智能|张聪（1988—）,男,吉林,硕士,主要研究方向为数据挖掘、人工智能

Applied Research of Unsupervised Machine Learning in Game Anti-fraud

XU Yu, ZHOU You, LIN Lu(), ZHANG Cong

Hangzhou Fuyun Network Technology Co., Ltd., Hangzhou 310000, China

Received:2020-07-16 Online:2020-09-10 Published:2020-10-15
Contact: Lu LIN E-mail:lu@fuyuncn.com

摘要/Abstract

摘要：

随着在线游戏市场不断壮大,互联网游戏“薅羊毛”事件日渐增多,这对网络游戏资产平衡,特别是游戏发行商的利益,造成严重影响。文章提出一种基于无监督机器学习的游戏机器人检测方法,该方法专注于发现游戏机器人与人类玩家在行为上的区别,引入word2vec思想对事件类型向量进行处理,通过聚类分析发现游戏机器人及新的欺诈模式。将无监督机器学习应用于在线游戏反欺诈引擎后,在线游戏机器人检测准确率提升约8%,极大地提高了检测的准确率。

关键词: 无监督机器学习, 时间序列, 游戏机器人, 游戏反欺诈

Abstract:

As the online game market continues to grow, there are more and more events of "get a deal" happen in the online game, which has had a serious impact on the balance of game assets, especially the interests of game publishers. This paper proposed a game bot detection method based on unsupervised machine learning, this method focused on discovering the differences in behavior between game bots and human players, introduced the word2vec idea to process the event type vector, discovered game bots and new fraud patterns through cluster analysis. After applied unsupervised machine learning to the online game anti-fraud engine, the accuracy of online game bot detection increased by about 8%, greatly improve the detection accuracy rate.

Key words: unsupervised machine learning, time series, game bot, game anti-fraud

中图分类号:

TP309

徐瑜, 周游, 林璐, 张聪. 无监督机器学习在游戏反欺诈领域的应用研究[J]. 信息网络安全, 2020, 20(9): 32-36.

XU Yu, ZHOU You, LIN Lu, ZHANG Cong. Applied Research of Unsupervised Machine Learning in Game Anti-fraud[J]. Netinfo Security, 2020, 20(9): 32-36.

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	非向量化			向量化
特征	准确率	召回率	误封率	准确率	召回率	误封率
游戏切换事件时间序列	60.70%	39.00%	1.01%	62.70%	44.75%	1.07%
金币变化事件时间序列	59.49%	37.63%	1.03%	62.50%	41.88%	1.01%
点击事件变化时间序列	77.78%	66.50%	0.76%	80.99%	69.75%	0.66%
金币值变化时间序列	59.39%	31.63%	0.87%
游戏时长变化时间序列	85.73%	75.88%	0.51%

特征	准确率	召回率	误封率
统计特征+短文本特征	84.93%	88.75%	0.63%
向量化点击事件变化时间序列特征	80.99%	69.75%	0.66%
游戏时长变化时间序列特征	85.73%	75.88%	0.51%
统计特征+短文本特征+向量化点击事件变化时间序列特征	81.48%	95.13%	0.87%
统计特征+短文本特征+游戏时长变化时间序列特征	85.16%	90.38%	0.63%

无监督机器学习在游戏反欺诈领域的应用研究

Applied Research of Unsupervised Machine Learning in Game Anti-fraud

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