Online gaming is a popular activity involving the adoption of complex systems and network infrastructures. The relevance of gaming, which generates large amounts of market revenue, drove research in modeling network devices' behavior to evaluate bandwidth consumption, predict and sustain high loads, and detect malicious activity. In this context, process mining appears promising due to its ability to combine data-driven analyses with model-based insights. In this paper, we propose a process mining-based method that analyzes gaming network traffic, allowing: unsupervised characterization of different states from gaming network data; encoding such states through process mining into interpretable Petri nets; and classification of gaming network traffic data to identify different video games being played. We apply the method to the UPSIDE case study, involving gaming network data of several devices interacting with two video games: Clash Royale and Rocket League. Results demonstrate that the gaming network behavior can be effectively and interpretably modeled through states represented as Petri nets with sufficient coherence and specificity while maintaining a good classification accuracy of the two different video games.

翻译：在线游戏作为一种涉及复杂系统与网络基础设施的流行活动，其产生的巨大市场收益推动了网络设备行为建模的相关研究，旨在评估带宽消耗、预测并维持高负载以及检测恶意活动。在此背景下，过程挖掘技术因其能够将数据驱动分析与基于模型的洞察相结合而展现出应用潜力。本文提出一种基于过程挖掘的方法，用于分析游戏网络流量，实现以下目标：从游戏网络数据中对不同状态进行无监督特征提取；通过过程挖掘将这些状态编码为可解释的Petri网；以及对游戏网络流量数据进行分类以识别正在运行的不同电子游戏。我们将该方法应用于UPSIDE案例研究，该案例涉及多台设备与两款电子游戏（Clash Royale和Rocket League）交互产生的游戏网络数据。结果表明：通过以Petri网表示的状态能够有效且可解释地建模游戏网络行为，这些模型在保持良好分类精度的同时，兼具足够的连贯性与特异性，从而准确区分两款不同的电子游戏。