Abstract
Game traffic depends on two main factors, the game protocol and the gamers’ behavior. Based on a few popular real-time multiplayer games this paper investigates the latter factor showing how a set of typical game phases—e.g., player movement, changes in the environment—impacts traffic on different observation levels. The nature of human behavior has such a high impact on traffic characteristics that it influences the traffic both at a macroscopic—e.g., traffic rate—and at a microscopic—payload content—level. First, by understanding the nature of this impact a user behavior detection algorithm is introduced to grab specific events and states from passive traffic measurements. The algorithms focus on the characteristics of the traffic rate, showing what information can be gathered by observing only packet header information. Second, as an application of our method some results, including a detailed analysis of measurements taken from an operational broadband network, are presented. Third, a novel model and an algorithm are introduced to extend the Deep Packet Inspection traffic classification method with the analysis of non-fix byte signatures, which are not considered in current methods. The model captures the variation of the dynamic byte segments and provides parameters for the algorithm. The introduced algorithm exploits the spatial and temporal correlation by examining and extracting the correlation structure of the traffic and constructing signatures based on the observed correlation. The algorithm is evaluated by examining proprietary gaming traffic and also other known non-gaming protocols.
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Notes
Practical meaning: the rate of decay of statistical dependence in a time series is slower than an exponential decay. See [5] for details.
The amount of time the player remains in the same state.
It is difficult to add reference to sites collecting these servers as they are always on the move to avoid legal consequences.
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Szabó, G., Veres, A. & Molnár, S. On the impacts of human interactions in MMORPG traffic. Multimed Tools Appl 45, 133–161 (2009). https://doi.org/10.1007/s11042-009-0298-4
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DOI: https://doi.org/10.1007/s11042-009-0298-4