statf09841Proposal

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Revision as of 14:09, 29 October 2009 by J237wang (talk | contribs) (Project 1 : Recognizing Cheaters in Multi-Player Online Game Environment)
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Use the following format for your proposal (maximum one page)

Project 1 : How to Make a Birdhouse

By: Maseeh Ghodsi, Soroush Ghodsi and Ali Ghodsi

Write your proposal here


Project 1 : Recognizing Cheaters in Multi-Player Online Game Environment

By: Mark Stuart, Mathieu Zerter, Iulia Pargaru

Multiplayer online games constitute a very large market in the entertainment industry that generates billions in revenue.<ref> S. F. Yeung, John C. S. Lui, Jianchuan Liu, Jeff Yan, Detecting Cheaters for Multiplayer Games: Theory, Design, and Implementation </ref> Multiplayer on-line games are games in which players use characters to perform specific actions and interact with other characters. The number of online game users is rapidly increasing. Computer play-programs are often used to automatically perform actions on behalf of a human player. This type of cheating gains the player unfair advantage, abusing resources, disrupting players’ gaming experience and even harming servers.<ref>Hyungil Kim, Sungwoo Hong, Juntae Kim, Detection of Auto Programs for MMORPGs</ref> Computer play-programs usually have a specific goal or a task that is repeated often. We suspect that sequences of events and actions created by play-programs are statistically different from the sequence of events generated by a human player. We will be using an on-line game called Tibia created by CIPSoft as a study case.

We have recruited volunteers who agreed to provide us with their gaming information. We are gathering and parsing packets sent by the user to the game server that contain detailed information about the actions performed by the user. The original data consist of: User ID, length of event, time of event, action type, action details, cheating (0 or 1). The sequences of events produced by human and the play-programs will be transformed into a set of features to reveal additional information such as periodicity of events, common sequential actions, rare events or actions not performed often, creating a measure for complexity of an action. Various algorithms will be applied to classify the data represented by the set available attributes. Some similar studies suggest that the following methods perform an effective classification of human vs. machine in on-line game environment:

  • Dynamic Bayesian Network
  • Isomap
  • Desicion Tree
  • Artificial Neural Network
  • Support Vector Machines
  • K nearest neighbours
  • Naive Bayesian

We intend to find a classification algorithm that detects in-game cheating in on-line game Tibia with reasonable accuracy.


Project 2 :

By: Jiheng Wang