@Article{cmc.2020.010309,
AUTHOR = {Jinfeng Li, Li Feng, Longqing Zhang, Hongning Dai, Lei Yang, Liwei Tian},
TITLE = {Identifying Game Processes Based on Private Working Sets},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {639--651},
URL = {http://www.techscience.com/cmc/v65n1/39587},
ISSN = {1546-2226},
ABSTRACT = {Fueled by the booming online games, there is an increasing demand for 
monitoring online games in various settings. One of the application scenarios is the monitor 
of computer games in school computer labs, for which an intelligent game recognition 
method is required. In this paper, a method to identify game processes in accordance with 
private working sets (i.e., the amount of memory occupied by a process but cannot be 
shared among other processes) is introduced. Results of the W test showed that the memory 
sizes occupied by the legitimate processes (e.g., the processes of common native windows 
applications) and game processes followed normal distribution. Using the <i>T</i>-test, a 
significant difference was identified between the legitimate processes and C/S-based 
computer games, in terms of the means and variances of their private working sets. 
Subsequently, we derived the density functions of the private working sets of the 
considered game processes and those of the legitimate processes. Given the private working 
set of a process and the derived probability density functions, the probability that the 
process is a legitimate process and the probability that the process is a game process can be 
determined. After comparing the two probabilities, we can easily determine whether the 
process is a game process or not. As revealed from the test results, the recognition accuracy 
of this method for C/S-based computer games was approximately 90%.},
DOI = {10.32604/cmc.2020.010309}
}