Anomaly detection in competitive multiplayer games

Abstract

As online video games rise in popularity, there has been a significant increase in fraudulent behavior and malicious activity. Numerous methods have been proposed to automate the identification and detection of such behaviors but most studies focused on situations with perfect prior knowledge of the gaming environment, particularly, in regards to the malicious behaviour being identified. This assumption is often too strong and generally false when it comes to real-world scenarios. For these reasons, it is useful to consider the case of incomplete information and combine techniques from machine learning and solution concepts from game theory that are better suited to tackle such settings, and automate the detection of anomalous behaviors. In this thesis, we focus on two major threats in competitive multiplayer games: intrusion and device compromises, and cheating and exploitation.

The former is a knowledge-based anomaly detection, focused on understanding the technology and strategy being used by the attacker in order to prevent it from occurring. One of the major security concerns in cyber-security are Advanced Persistent Threats (APT). APTs are stealthy and constant computer hacking processes which can compromise systems bypassing traditional security measures in order to gain access to confidential information held in those systems. In online video games, most APT attacks leverage phishing and target individuals with fake game updates or email scams to gain initial access and steal user data, including but not limited to account credentials and credit card numbers. In our work, we examine the two player game called FlipIt to model covert compromises and stealthy hacking processes in partial observable settings, and show the efficiency of game theory concept solutions and deep reinforcement learning techniques to improve learning and detection in the context of fraud prevention.

The latter defines a behavioral-based anomaly detection. Cheating in online games comes with many consequences for both players and companies; hence, cheating detection and prevention is an important part of developing a commercial online game. However, the task of manually identifying cheaters from the player population is unfeasible to game designers due to the sheer size of the player population and lack of test datasets. In our work, we present a novel approach to detecting cheating in competitive multiplayer games using tools from hybrid intelligence and unsupervised learning, and give proof-of-concept experimental results on real-world datasets.