Detecting Bad Mouthing Attacks in Bot Voting Systems for Online Games
Date Issued
2007
Date
2007
Author(s)
Lou, Cheng-Chun
DOI
en-US
Abstract
In recent years, MMORPGs have become extremely popular; however, the cheating seen in traditional online games has also been increasing in MMORPG. One of the most common forms of cheating in MMORPG is the increased use of game bots, i.e., auto-playing game clients. Because of the difficulty of automatically detecting a game bot, some game companies have begun to use voting schemes, in which each player can vote that another suspicious player is a game bot. However, in a voting-based system, collusion, e.g., unfairly low ratings ("bad-mouthing"), is a critical concern to be resolved.
In this work, we propose two mechanisms based on the vote history of the voter and the votee to detect a collusion cluster whether it be a single cluster or multiple clusters: 1) voter-based collusion cluster detection, and 2) votee-voter-based collusion cluster detection. Detection accuracy can exceed 83% and 97% when the collusion cluster bad-mouths three times and five times, respectively. In addition, we evaluate our schemes under different environments, and high quality performance is maintained.
Subjects
大型多人線上角色扮演遊戲
遊戲自動程式
名譽系統
共謀偵測
MMORPG
game bot
reputation system
collusion detection
social network
Type
thesis
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