通过性能-技巧不一致性检测网络游戏中的被动作弊

Daiping Liu, Xing Gao, Mingwei Zhang, Haining Wang, A. Stavrou
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引用次数: 8

摘要

作为第一人称射击(FPS)在线游戏中最常用的bot, aimbots很难被发现,因为它们是完全被动的,并且在许多方面与优秀的诚实玩家相似。在本文中,我们进行了第一次实地测量研究,以了解aimbots的现状以及它们在野外的表现。为了收集数据,我们设计了一种名为“诱饵目标”的新颖通用技术,以准确捕获两款最受欢迎的FPS游戏中现有的瞄准机器人。我们的测量结果显示,使用瞄准机器人的作弊者在所有方面都不如优秀的诚实玩家,尽管瞄准机器人可以帮助他们获得很高的射击表现。为了描述作弊者不熟练和公然的本质,我们确定了7个特征,其中6个是新颖的,这些特征不容易被目标机器人模仿。利用这组功能,我们提出了一个准确而健壮的服务器端目标机器人检测器,称为AimDetect。AimDetect的核心是一个级联分类器,用于检测aibots的性能和技巧之间的不一致性。我们使用真实的游戏轨迹来评估AimDetect的有效性和通用性。我们的结果表明,AimDetect可以捕获几乎所有的目标机器人,并且很少有误报和很小的开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting Passive Cheats in Online Games via Performance-Skillfulness Inconsistency
As the most commonly used bots in first-person shooter (FPS) online games, aimbots are notoriously difficult to detect because they are completely passive and resemble excellent honest players in many aspects. In this paper, we conduct the first field measurement study to understand the status quo of aimbots and how they play in the wild. For data collection purpose, we devise a novel and generic technique called baittarget to accurately capture existing aimbots from the two most popular FPS games. Our measurement reveals that cheaters who use aimbots cannot play as skillful as excellent honest players in all aspects even though aimbots can help them to achieve very high shooting performance. To characterize the unskillful and blatant nature of cheaters, we identify seven features, of which six are novel, and these features cannot be easily mimicked by aimbots. Leveraging this set of features, we propose an accurate and robust server-side aimbot detector called AimDetect. The core of AimDetect is a cascaded classifier that detects the inconsistency between performance and skillfulness of aimbots. We evaluate the efficacy and generality of AimDetect using the real game traces. Our results show that AimDetect can capture almost all of the aimbots with very few false positives and minor overhead.
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