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

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

Information-Theoretic Secure Outsourced Computation in Distributed Systems

Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design

Application of information theory and statistical learning to anomaly detection

In today\u27s highly networked world, computer intrusions and other attacks area constant threat. The detection of such attacks, especially attacks that are new or previously unknown, is important to secure networks and computers. A major focus of current research efforts in this area is on anomaly detection.;In this dissertation, we explore applications of information theory and statistical learning to anomaly detection. Specifically, we look at two difficult detection problems in network and system security, (1) detecting covert channels, and (2) determining if a user is a human or bot. We link both of these problems to entropy, a measure of randomness information content, or complexity, a concept that is central to information theory. The behavior of bots is low in entropy when tasks are rigidly repeated or high in entropy when behavior is pseudo-random. In contrast, human behavior is complex and medium in entropy. Similarly, covert channels either create regularity, resulting in low entropy, or encode extra information, resulting in high entropy. Meanwhile, legitimate traffic is characterized by complex interdependencies and moderate entropy. In addition, we utilize statistical learning algorithms, Bayesian learning, neural networks, and maximum likelihood estimation, in both modeling and detecting of covert channels and bots.;Our results using entropy and statistical learning techniques are excellent. By using entropy to detect covert channels, we detected three different covert timing channels that were not detected by previous detection methods. Then, using entropy and Bayesian learning to detect chat bots, we detected 100% of chat bots with a false positive rate of only 0.05% in over 1400 hours of chat traces. Lastly, using neural networks and the idea of human observational proofs to detect game bots, we detected 99.8% of game bots with no false positives in 95 hours of traces. Our work shows that a combination of entropy measures and statistical learning algorithms is a powerful and highly effective tool for anomaly detection

Born to Run: A Grounded Theory Study of Cheating in the Online Speedrunning Community

Video games represent a growing genre of media quickly becoming one of the leading forms of entertainment (Jordan, 2014). This popularity has allowed new playstyles to emerge across the video game genre, such as e-Sports and speedrunning. In particular, the speedrunning community has somewhat redefined what it means to “cheat” in a video game by accepting the use certain software and hardware violations that could be seen as “cheating” to the general gaming community. This paper examined the social construction of cheating in this digital community through the use of grounded theory methods

Accurate Player Modeling and Cheat-Proof Gameplay in Peer-to-Peer Based Multiplayer Online Games

We present the first detailed measurement study and models of the virtual populations in popular Massively Multiplayer Online Role-Playing Games (MMORPGs). Our results show that, amongst several MMORPGs with very different play styles, the patterns of behaviors are consistent and can be described using a common set of models. In addition, we break down actions common to Trading Card Games (TCGs) and explain how they can be executed between players without the need for a third party referee. In each action, the player is either prevented from cheating, or if they do cheat, the opponent will be able to prove they have done so. We show these methods are secure and may be used in many various styles of TCGs. We measure moves in a real TCG to compare to our implementation of Match+Guardian (M+G), our secure Peer-to-Peer (P2P) protocol for implementing online TCGs. Our results, based on an evaluation of M+G\u27s performance on the Android (TM) platform, show that M+G can be used in a P2P fashion on mobile devices. Finally, we introduce and outline a HYbrid P2P ARchitecture for Trading Card Games, HYPAR-TCG. The system utilizes Distributed Hash Tables (DHTs) and other P2P overlays to store cached game data and to perform game matchmaking. This helps reduce the network and computational load to the central servers. We describe how a centralized server authority can work in concert with a P2P gameplay protocol, while still allowing for reputation and authoritative account management

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

온라인 게임에서 유저의 행태에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 경영대학 경영학과, 2018. 2. 유병준.This dissertation consists of two essays on user behavior in online games. In the first essay, I identified multi-botting cheaters and measured their impacts using basic information in database such as user ID, playtime and item purchase record. I addressed the data availability issue and proposed a method for companies with limited data and resources. I also avoided large-scale transaction processing or complex development, which are fairly common in existing cheating detection methods. With respect to identifying cheaters, we used algorithms named DTW (Dynamic Time Warping) and JWD (Jaro–Winkler distance). I also measured the effects of using hacking tool by employing DID (Difference in Differences). My analysis results show some counter-intuitive results. Overall, cheaters constitute a minute part of users in terms of numbers – only about 0.25%. However, they hold approximately 12% of revenue. Furthermore, the usage of hacking tools causes a 102% and 79% increase in playtime and purchase respectively right after users start to use hacking tools. According to additional analysis, it could be shown that the positive effects of hacking tools are not just short-term. My granger causality test also reveals that cheating users activity does not affect other users' purchases or playtime trend. In the second essay, I propose a methodology to deal with churn prediction that meets two major purposes in the mobile casual game context. First, reducing the cost of data preparation, which is growing its importance in the big-data environment. Second, coming up with an algorithm that shows favorable performance comparable to that of the state-of-the-art. As a result, we succeed in greatly lowering the cost of the data preparation process by employing the sequence structure of the log data as it is. In addition, our sequence classification model based on CNN-LSTM shows superior results compared to the models of previous studies.Essay 1. Is Cheating Always Bad? A study of cheating identification and measurement of the effect 1 1. Introduction 2 2. Literature Review 8 3. Data 16 4. Hypotheses 17 5. Methodology 20 5.1 Cheating Identification 20 5.2 Measurement of Cheating Tool Usage Effect 28 6. Result 33 6.1 Cheating Identification 33 6.2 Measurement of Cheating Tool Usage Effect 33 7. Additional Analysis 35 7.1 Lifespan of Cheating Users 35 7.2 Granger Causality Test 36 8. Discussion and Conclusion 37 9. References 48 Essay 2. Churn Prediction in Mobile Casual Game: A Deep Sequence Classification Approach 61 1. Introduction 62 2. Definition of Churn 64 3. Related Works 65 4. Data 66 5. Methodology 66 5.1 Data Preparation 66 5.2 Prediction Model 71 6. Result and Discussion 74 7. References 77Docto