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Detection Analysis And Counter Measure Of Online Game Cheats

As the popularity of e-commerce continues to rise, so does the level of fraud risk for financial institutions and their customers, elevating concerns over fraud risk to the C-suite. As a result, executives and boards must think beyond fraud protection to fraud prevention, which requires a working knowledge of the possible threats and solutions.

By real-time data you mean a so-called online algorithm, where data points are received time after time. The significance of a peak might be determined by values in the future. It would be nice to extend the algorithm to become online by modifying the past results without sacrificing the time complexity too much. Huber Oct 9 '19 at 12:54. Cheating in online games comes with many consequences for both players and companies. Therefore, cheating detection and prevention is an important part of developing a commercial online game.

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Client-side cheat detection in games using machine learning

Detection Analysis And Counter Measure Of Online Game Cheats Age Of Empires 2

Detection Analysis And Counter Measure Of Online Game Cheats

Detection Analysis And Counter Measure Of Online Game Cheats Free

Supervisor(s):Bojan KolosnjajiFatih Kilic
Status:finished
Topic:Machine Learning Methods
Author:Mai Ton Nu Cam
Submission:2015-11-15
Type of Thesis: Bachelorthesis
Proof of ConceptNo

Astract:

Statistics show that over the last years the popularity and the market share of online games has been increasing [Sta15c]. Playing with thousands of other players worldwide there is always the risk of people cheating to gain an advantage over others. This leads to an unbalance in a game and therefore unsatisfied customers from the game developers’ point of view. The goal of the gaming industry is to prevent that and to reveal as many cheats as possible to be able to consequently punish cheating players. There are many solutions for cheat detection, but the biggest problem with them is that they often rely on signatures to recognize cheats and therefore are unable to automatically adapt to new ones. Our goal is to reach a solution, in which the cheat detection program can learn to adapt to new situations without manual input. To fulfill these requirements machine learning presents itself as a suitable concept. Using Hidden Markov Models we will train the models to represent normal behavior and comparing test data to those models will yield whether a cheat has been used or not. Our assumption is that using cheats will result in deviations from the described normal behavior. With regular training the models can adapt themselves to code changes of a game and are able to even detect newly created cheats since they do not depend on prior knowledge of a cheat.