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Behavior Pattern Recognition of Game Dragon Nest using Bloom Filter Method.

Author(s)
Deris Stiawan Department of Computer Engineering, Faculty of Computer Science, Universitas Sriwijaya, Indonesia
Mohd. Yazid Idris Media and Game Innovation Center of Excellence, Institute of Human Centered Engineering, Universiti Teknologi, Malaysia
Diky Aryandi Department of Computer Engineering, Faculty of Computer Science, Universitas Sriwijaya, Indonesia
Ahmad Heryanto Department of Computer Engineering, Faculty of Computer Science, Universitas Sriwijaya, Indonesia
Tri Wanda Septian Department of Computer Engineering, Faculty of Computer Science, Universitas Sriwijaya, Indonesia
Farkhana Muchtar School of Computing, Faculty of Engineering, Universiti Teknologi, Malaysia
Rahmat Budiarto College of Comp. Sc. & IT, Albaha University, Albaha, Saudi Arabia
Abstract
Dragon Nest is one of Massively Multiplayer Online Role-playing Game (MMORPG) online games. It has become the most popular online game played by people around the world. This work observes two examples of the MMORPG online games: the Dragon Nest INA and the Legend DN II. The purpose is to analyze the traffic data of the Dragon Nest to find and discern the patterns of behavior of the Dragon Nest INA and the Legend DN II using Deep Packet Inspection (DPI). A dataset is constructed by capturing traffic data from the testbed environment. Then feature extraction, feature selection, and visualization are performed during the experiments. Experiment results show the traffic data of the Dragon Nest INA is higher than the Legend DN II. It is because of the difference in the number of entries in the game. Then, the Bloom filter method is used as a tool to check the existence of a pattern of the Dragon Nest in the dataset. The false positive rate of matching experiment is 0.399576%.
Publication Details
Page(s) 128-133
DOI DOI not available
Published Journal: International Journal of Communication Networks and Information Security, Volume: 11, Issue: 1, Year: 2019

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Keywords
RAPD, RGA, PCR
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