TY - GEN
T1 - The gravy value
T2 - 21st International Conference on Information Security Applications, WISA 2020
AU - Park, Semi
AU - Lee, Kyungho
N1 - Funding Information:
This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2020-2015-0-00403)supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) Following(or This research) was results of a study on the “HPC Support” Project, supported by the ‘Ministry of Science and ICT’ and NIPA.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - The critical success of online games has led the industry to global success, as the market size is expected to reach 18,194 USD by the year 2020. However, the success of the online game market has led to the growth of illegal activities, such as the use of game bots. Game bots are software applications capable of collecting game items, which are often banned from online game service providers. The illegal activities are not limited to tax evasion and money laundering. In order to help detect these bots, this study employs the dataset from an MMORPG called Aion. By detecting the bots using the server-side analysis, this paper analyzed user behavior and used features based on the experience, skill, and gravy value that represents the cost-efficiency. We experimented with machine learning algorithms such as MLP, SVM, and Random Forest. As a result, the F-score for detecting the total sum of the accounts that consists of the game bots and real users reached 0.9638. We believe our study may help online game service providers, future researchers, and governmental agencies to detect and classify the MMORPG game bots.
AB - The critical success of online games has led the industry to global success, as the market size is expected to reach 18,194 USD by the year 2020. However, the success of the online game market has led to the growth of illegal activities, such as the use of game bots. Game bots are software applications capable of collecting game items, which are often banned from online game service providers. The illegal activities are not limited to tax evasion and money laundering. In order to help detect these bots, this study employs the dataset from an MMORPG called Aion. By detecting the bots using the server-side analysis, this paper analyzed user behavior and used features based on the experience, skill, and gravy value that represents the cost-efficiency. We experimented with machine learning algorithms such as MLP, SVM, and Random Forest. As a result, the F-score for detecting the total sum of the accounts that consists of the game bots and real users reached 0.9638. We believe our study may help online game service providers, future researchers, and governmental agencies to detect and classify the MMORPG game bots.
KW - BOT detection
KW - MMORPG
KW - Machine learning
KW - Online game
KW - Security
UR - http://www.scopus.com/inward/record.url?scp=85098245200&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-65299-9_11
DO - 10.1007/978-3-030-65299-9_11
M3 - Conference contribution
AN - SCOPUS:85098245200
SN - 9783030652982
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 142
EP - 153
BT - Information Security Applications - 21st International Conference, WISA 2020, Revised Selected Papers
A2 - You, Ilsun
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 August 2020 through 28 August 2020
ER -