You are a Game Bot!: Uncovering Game Bots in MMORPGs via Self-similarity in the Wild
You are a Game Bot!: Uncovering Game Bots in MMORPGs via Self-similarity in the Wild
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你是一个游戏机器人！：通过野外自相似性发现 MMORPG 中的游戏机器人
DOI：
10.14722/ndss.2016.23436
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
H. Kim
中科院分区：
文献类型：
--
作者：
Eunjo Lee;Jiyoung Woo;Hyoungshick Kim;Aziz Mohaisen;H. Kim
游戏bot是大型多人在线角色扮演游戏（mmorpg）的一大威胁，因为它们会严重破坏mmorpg的声誉和游戏内经济平衡。现有的游戏机器人检测技术不仅对游戏内容的变化很敏感，而且在检测新出现的未知机器人模式方面也很有限。为了克服随着时间的推移学习机器人模式的限制，我们提出了一个通过机器学习技术检测游戏机器人的框架。所提出的框架利用自相似性来有效衡量每个玩家随时间重复活动的频率，这是识别机器人的重要线索。因此，我们使用现实世界的MMORPG（游戏邦注：如《Lineage》、《Aion》和《Blade & Soul》）数据集来评估所提议框架的可行性。实验结果表明：1)自相似性可以作为各种mmorpg的一般特征；2)机器人行为更新后的检测模型维护过程可以实现；3)我们的机器人检测框架是可行的。
Game bots are a critical threat to Massively Multiplayer Online Role-Playing Games (MMORPGs) because they can seriously damage the reputation and in-game economy equilibrium of MMORPGs. Existing game bot detection techniques are not only generally sensitive to changes in game contents but also limited in detecting emerging bot patterns that were hitherto unknown. To overcome the limitation of learning bot patterns over time, we propose a framework that detects game bots through machine learning technique. The proposed framework utilizes self-similarity to effectively measure the frequency of repeated activities per player over time, which is an important clue to identifying bots. Consequently, we use realworld MMORPG (“Lineage”, “Aion” and “Blade & Soul”) datasets to evaluate the feasibility of the proposed framework. Our experimental results demonstrate that 1) self-similarity can be used as a general feature in various MMORPGs, 2) a detection model maintenance process with newly updated bot behaviors can be implemented, and 3) our bot detection framework is practicable.