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A new AI innovation aims to stop cheating in multiplayer video games

Computer scientists from the University of Texas at Dallas have created a new cheat-detection system that can be used for any massively multiplayer online game

Gamers play the League of Legends computer game at an esports hotel in Osaka, Japan. (Photo credit: Buddhika Weerasinghe/Bloomberg)
Gamers play the League of Legends computer game at an esports hotel in Osaka, Japan. (Photo credit: Buddhika Weerasinghe/Bloomberg) (Bloomberg)

Counter-Strike is one of the most popular online, first-person shooter games in the world today. The game’s Counter-Strike: Global Offensive title is immensely successful in India, with multiple e-sports teams participating in CS:GO tournaments down the years.

But there’s also a seedy underbelly of cheating that is rampant in the world of video games. In Counter-Strike, players can play as terrorists or counter-terrorist operatives at different locations. The game comes with multiple hacks, or cheat codes as they are called, which help players gain an advantage over others. Some of these codes or console commands unlock extra weapons, while others generate special in-game conditions that make it easier for a player to win. These cheats not only make the gaming experience unfair, especially in competitions and tournaments, but can also affect gamers in the long-run.

Now, computer scientists from the University of Texas at Dallas claim to have found an interesting antidote to the problem. Researchers at the university have created a new cheat-detection system that can be used for any massively multiplayer online, or MMO, game that sends data traffic to a central server. According to the UT Dallas website, the researchers devised their approach to detect cheaters using Counter-Strike, but the same system can be applied to MMOs.

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Detecting cheating in MMO games is tricky, simply because the data that goes from a player’s computer to a central game server is encrypted. Previously, the only way to detect any cheating anomalies would be to go through decrypted game logs. But this new mechanism bypasses the decrypted data problem by analyzing the encrypted data traffic to and from the server in real time, according to an official news release.

For the study, 20 students from the university downloaded Counter-Strike and three software cheats: an aimbot, which automatically targets an opponent, a speed hack, which allows the player to move faster, and a wallhack, which makes walls transparent so that players can easily see their opponents

“Players who cheat send traffic in a different way,” says Dr Latifur Khan, an author of the study on the research, and professor of computer science and director of the Big Data Analytics and Management Lab at UT Dallas. “We’re trying to capture those characteristics,” says Khan. The findings of the study were published in the IEEE Transactions on Dependable and Secure Computing journal in August.

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For the study, 20 students from the university downloaded Counter-Strike and three software cheats: an aimbot, which automatically targets an opponent, a speed hack, which allows the player to move faster, and a wallhack, which makes walls transparent so that players can easily see their opponents. The researchers also set up a server dedicated for the project so the students’ activity would not disrupt other online players, according to a press release.

While studying the game data, which travels in small packets or bundles of information, researchers looked at the different sizes of data packets. These vary depending on the contents. They also analyzed features like the number of incoming and outgoing packets, their size, the time they were transmitted, their direction and the number of packets in a burst (i.e. a group of consecutive packets). This monitoring of data traffic was used to identify patterns that indicate cheating, the release explains. After that, this information on the patterns was used to train a machine-learning model to predict cheating based on patterns and features in the game data.

According to the UT Dallas website, the researchers also adjusted their statistical model, based on a small set of gamers, to work for larger numbers of players. The researchers believe video gaming companies could use this technique with their own data to train gaming softwares to detect cheating. “Our aim is to ensure that games like Counter-Strike remain fun and fair for all players,” Khan says in the release.

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  • FIRST PUBLISHED
    17.11.2020 | 05:35 PM IST

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