Deep learning and multivariate time series for cheat detection in video games

Online video games drive a multi-billion dollar industry dedicated to maintaining a competitive and enjoyable experience for players. Traditional cheat detection systems struggle when facing new exploits or sophisticated fraudsters. More advanced solutions based on machine learning are more adaptive but rely heavily on in-game data, which means that each game has to develop its own cheat detection system. In this work, we propose a novel approach to cheat detection that doesn't require in-game data. Firstly, we treat the multimodal interactions between the player and the platform as multivariate time series. We then use convolutional neural networks to classify these time series as corresponding to legitimate or fraudulent gameplay. Our models achieve an average accuracy of respectively 99.2% and 98.9% in triggerbot and aimbot (two widespread cheats), in an experiment to validate the system's ability to detect cheating in players never seen before. Because this approach is based solely on player behavior, it can be applied to any game or input method, and even various tasks related to modeling human activity.- (undefined

Prevention vs detection in online game cheating

Abstract. Cheating is a major problem in online games, but solving this would require either a complicated architecture design, costly third-party anti-cheat, or both. This paper aims to explore the differences between preventive and detective solutions against online game cheating. Specifically, it explores solutions against software-based cheatings, what kind of cheats there are, and what proposed and implemented solutions there are. This paper was conducted using literature reviews as methodology, using relevant papers from databases such as ResearchGate, ACM, and IEEE. In this paper, it was concluded that a good prevention strategy during the game development phase is adequate to mitigate and prevent cheating but will require appropriate anti-cheat software to maintain fairness during the lifetime of the game. The importance of an online game’s network architecture choice in preventing cheating became apparent within this paper after comparing the benefits of each type side-by-side. Results showed that peer-to-peer architecture not having a trusted centralized authority means that the game needs to rely more on an anti-cheat software to prevent and detect cheating. This paper could not conclude what an appropriate anti-cheat software is because the topic is outside of the scope of this paper and lacks public data. Still, it does raise the question of whether a more aggressive anti-cheat strategy is suitable for a game or not

Analysis of human-computer interaction time series using Deep Learning

Dissertação de mestrado integrado em Engenharia InformáticaThe collection and use of data resulting from human-computer interaction are becoming more and more common. These have been allowing for the birth of intelligent systems that extract powerful knowledge, potentially improving the user experience or even originating various digital services. With the rapid scientific advancements that have been taking place in the field of Deep Learning, it is convenient to review the underlying techniques currently used in these systems. In this work, we propose an approach to the general task of analyzing such interactions in the form of time series, using Deep Learning. We then rely on this approach to develop an anti-cheating system for video games using only keyboard and mouse input data. This system can work with any video game, and with minor adjustments, it can be easily adapted to new platforms (such as mobile and gaming consoles). Experiments suggest that analyzing HCI time series data with deep learning yields better results while providing solutions that do not rely highly on domain knowledge as traditional systems.A recolha e a utilização de dados resultantes da interação humano-computador estão a tornar-se cada vez mais comuns. Estas têm permitido o surgimento de sistemas inteligentes capazes de extrair conhecimento ex tremamente útil, potencialmente melhorando a experiência do utilizador ou mesmo originando diversos serviços digitais. Com os acelerados avanços científicos na área do Deep Learning, torna-se conveniente rever as técni cas subjacentes a estes sistemas. Neste trabalho, propomos uma abordagem ao problema geral de analisar tais interações na forma de séries temporais, utilizando Deep Learning. Apoiamo-nos então nesta abordagem para desenvolver um sistema de anti-cheating para videojogos, utilizando apenas dados de input de rato e teclado. Este sistema funciona com qualquer jogo e pode, com pequenos ajustes, ser adaptado para novas plataformas (como dispositivos móveis ou consolas). As experiências sugerem que analisar dados de séries temporais de interação humano-computador pro duz melhores resultados, disponibilizando soluções que não são altamente dependentes de conhecimento de domínio como sistemas tradicionais

Behaviour-Based Cheat Detection in Multiplayer Games with Event-B

Cheating is a key issue in multiplayer games as it causes unfairness which reduces legitimate users' satisfaction and is thus detrimental to game revenue. Many commercial solutions prevent cheats by reacting to speci c implementations of cheats. As a result, they respond more slowly to fast-changing cheat techniques. This work proposes a framework using Event-B to describe and detect cheats from server-visible game behaviours. We argue that this cheat detection is more resistant to changing cheat technique

Cheating and Virtual Crime in Massively Multiplayer Online Games

Massively Multiplayer Online Games (MMOG) have become extremely popular since the birth of the Internet, with many millions of players playing games such as Poker and World of Warcraft. However, they do not seem to be well understood, and academic research into them has been limited. This project explains the nature of MMOG, and the relationship between MMOG and information security. This project discusses the problem of cheating in MMOG and it explains what cheating is, how it occurs, and how information security can be used to prevent it. The nature of virtual economies in MMOG is discussed, and the virtual crimes that have affected MMOG along with preventative measures are examined

Selected Computing Research Papers Volume 1 June 2012

An Evaluation of Anti-phishing Solutions (Arinze Bona Umeaku) ..................................... 1 A Detailed Analysis of Current Biometric Research Aimed at Improving Online Authentication Systems (Daniel Brown) .............................................................................. 7 An Evaluation of Current Intrusion Detection Systems Research (Gavin Alexander Burns) .................................................................................................... 13 An Analysis of Current Research on Quantum Key Distribution (Mark Lorraine) ............ 19 A Critical Review of Current Distributed Denial of Service Prevention Methodologies (Paul Mains) ............................................................................................... 29 An Evaluation of Current Computing Methodologies Aimed at Improving the Prevention of SQL Injection Attacks in Web Based Applications (Niall Marsh) .............. 39 An Evaluation of Proposals to Detect Cheating in Multiplayer Online Games (Bradley Peacock) ............................................................................................................... 45 An Empirical Study of Security Techniques Used In Online Banking (Rajinder D G Singh) .......................................................................................................... 51 A Critical Study on Proposed Firewall Implementation Methods in Modern Networks (Loghin Tivig) .................................................................................................... 5

Cheat Detection using Machine Learning within Counter-Strike: Global Offensive

Deep learning is becoming a steadfast means of solving complex problems that do not have a single concrete or simple solution. One complex problem that fits this description and that has also begun to appear at the forefront of society is cheating, specifically within video games. Therefore, this paper presents a means of developing a deep learning framework that successfully identifies cheaters within the video game CounterStrike: Global Offensive. This approach yields predictive accuracy metrics that range between 80-90% depending on the exact neural network architecture that is employed. This approach is easily scalable and applicable to all types of games due to this project\u27s basic design philosophy and approach

Scaffolding Novices to Leverage Auditory Awareness Cues in First-Person Shooters

Today's digital games require the mastery of many different skills. This is accomplished through play itself -- sometimes experientially and other times by using explicit guidance provided by the game designer. Multiplayer games, due to their competitive nature, provide fewer opportunities for designers to guide players into mastering particular skills, and so players must learn and master skills experientially. However, when novices compete against better players -- as they would if they were new to the game -- they can feel overwhelmed by the skill differential. This may hinder the ability of novices to learn experientially, and more importantly, may lead to extended periods of unsatisfying play and missed social play opportunities as they struggle to improve in a competitive context. A game genre that suffers from this problem is the multiplayer first-person shooter (FPS), in which the skill difference between new players and experts who have reached a high level of expertise can be quite large. To succeed in a FPS, players must master a number of skills, the most obvious of which are navigating a complex 3D environment and targeting opponents. To target opponents in a 3D environment, you must also be able to locate them -- a skill known as "opponent location awareness". With the goal of helping novices learn the skill of opponent location awareness, we first conducted an experiment to determine how experts accomplish this important task in multiplayer FPS games. After determining that an understanding of audio cues -- and how to leverage them -- was critical, we designed and evaluated two systems for introducing this skill of locating opponents through audio cues -- an explicit stand-alone training system, and a modified game interface for embedded training. We found that both systems improved accuracy and confidence, but that the explicit training system led to more audio cues being recognized. Our work may help people of disparate skill be able to play together, by scaffolding novices to learn and use a strategy commonly employed by experts