Abstract
A game BOT is a major threat in the online game industry. There have been many efforts to distinguish game BOT users from normal users. Several studies have proposed BOT detection models based on the analysis of users' in-game action sequence data. These studies indicated that the analysis of users' in-game actions is effective to detect BOTs. However, they do not use sufficiently large data sets to train and test their algorithms. In this paper, we have proposed a BOT detection model that uses users' in-game action sequence data obtained with the aid of big data analysis environments. We did empirical analysis of the dataset of "Blade and Soul", the third largest MMORPG in Korea. The result shows that a large amount of sequence data leads to high accuracy.
| Original language | English |
|---|---|
| Title of host publication | Annual Workshop on Network and Systems Support for Games |
| Publisher | IEEE Computer Society |
| ISBN (Print) | 9781479929610 |
| DOIs | |
| Publication status | Published - 2013 Jan 1 |
| Event | 2013 12th Annual Workshop on Network and Systems Support for Games, NetGames 2013 - Denver, CO, United States Duration: 2013 Dec 9 → 2013 Dec 10 |
Other
| Other | 2013 12th Annual Workshop on Network and Systems Support for Games, NetGames 2013 |
|---|---|
| Country | United States |
| City | Denver, CO |
| Period | 13/12/9 → 13/12/10 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Human-Computer Interaction
- Electrical and Electronic Engineering
Cite this
I know what the BOTs did yesterday : Full action sequence analysis using Naïve Bayesian algorithm. / Lee, Jina; Lim, Jiyoun; Cho, Wonjun; Kim, Huy Kang.
Annual Workshop on Network and Systems Support for Games. IEEE Computer Society, 2013. 6820611.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - I know what the BOTs did yesterday
T2 - Full action sequence analysis using Naïve Bayesian algorithm
AU - Lee, Jina
AU - Lim, Jiyoun
AU - Cho, Wonjun
AU - Kim, Huy Kang
PY - 2013/1/1
Y1 - 2013/1/1
N2 - A game BOT is a major threat in the online game industry. There have been many efforts to distinguish game BOT users from normal users. Several studies have proposed BOT detection models based on the analysis of users' in-game action sequence data. These studies indicated that the analysis of users' in-game actions is effective to detect BOTs. However, they do not use sufficiently large data sets to train and test their algorithms. In this paper, we have proposed a BOT detection model that uses users' in-game action sequence data obtained with the aid of big data analysis environments. We did empirical analysis of the dataset of "Blade and Soul", the third largest MMORPG in Korea. The result shows that a large amount of sequence data leads to high accuracy.
AB - A game BOT is a major threat in the online game industry. There have been many efforts to distinguish game BOT users from normal users. Several studies have proposed BOT detection models based on the analysis of users' in-game action sequence data. These studies indicated that the analysis of users' in-game actions is effective to detect BOTs. However, they do not use sufficiently large data sets to train and test their algorithms. In this paper, we have proposed a BOT detection model that uses users' in-game action sequence data obtained with the aid of big data analysis environments. We did empirical analysis of the dataset of "Blade and Soul", the third largest MMORPG in Korea. The result shows that a large amount of sequence data leads to high accuracy.
KW - BOT detection
KW - Naïve Bayesian classifier
KW - online game security
KW - sequence data
UR - http://www.scopus.com/inward/record.url?scp=84906748540&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906748540&partnerID=8YFLogxK
U2 - 10.1109/NetGames.2013.6820611
DO - 10.1109/NetGames.2013.6820611
M3 - Conference contribution
AN - SCOPUS:84906748540
SN - 9781479929610
BT - Annual Workshop on Network and Systems Support for Games
PB - IEEE Computer Society
ER -