在线多人游戏中的属性推理攻击——以《DOTA2》为例

Pier Paolo Tricomi, Lisa Facciolo, Giovanni Apruzzese, M. Conti
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引用次数: 2

摘要

你知道超过7000万Dota2玩家的游戏数据是免费的吗?如果这些数据被恶意使用怎么办?本文首次对这一问题进行了研究。由于电子游戏的广泛流行,我们提出了Dota2背景下属性推理攻击(AIA)的第一个威胁模型。我们解释了攻击者如何(以及为什么)利用Dota2生态系统中丰富的公共数据来推断玩家的私人信息。由于缺乏具体的证据证明我们的AIA的有效性,我们实证证明和评估其在现实中的影响。通过对500名Dota2玩家进行的调查,我们验证了玩家的Dota2活动与他们的现实生活之间是否存在相关性。然后,在找到这样的联系(p < 0.01和ρ > 0.3)后,我们在道德上执行多样化的AIA。我们利用机器学习的能力,通过使用公开的游戏内数据来推断调查对象的真实属性。我们的研究结果表明,通过应用领域专业知识,一些AIA可以达到98%的精度和90%以上的准确度。因此,本文提出了一个微妙但具体的威胁,它可能会影响整个竞争游戏领域。我们提醒了Dota2的开发者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attribute Inference Attacks in Online Multiplayer Video Games: A Case Study on DOTA2
Did you know that over 70 million of Dota2 players have their in-game data freely accessible? What if such data is used in malicious ways? This paper is the first to investigate such a problem. Motivated by the widespread popularity of video games, we propose the first threat model for Attribute Inference Attacks (AIA) in the Dota2 context. We explain how (and why) attackers can exploit the abundant public data in the Dota2 ecosystem to infer private information about its players. Due to lack of concrete evidence on the efficacy of our AIA, we empirically prove and assess their impact in reality. By conducting an extensive survey on 500 Dota2 players spanning over 26k matches, we verify whether a correlation exists between a player's Dota2 activity and their real-life. Then, after finding such a link (p < 0.01 and ρ > 0.3), we ethically perform diverse AIA. We leverage the capabilities of machine learning to infer real-life attributes of the respondents of our survey by using their publicly available in-game data. Our results show that, by applyingdomain expertise, some AIA can reach up to 98% precision and over 90% accuracy. This paper hence raises the alarm on a subtle, but concrete threat that can potentially affect the entire competitive gaming landscape. We alerted the developers of Dota2.
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