I know what the BOTs did yesterday: Full action sequence analysis using Naïve Bayesian algorithm

Jina Lee; Jiyoun Lim; Wonjun Cho; Huy Kang Kim

doi:10.1109/NetGames.2013.6820611

I know what the BOTs did yesterday: Full action sequence analysis using Naïve Bayesian algorithm

Jina Lee, Jiyoun Lim, Wonjun Cho, Huy Kang Kim

Graduate School of Information Security

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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	https://doi.org/10.1109/NetGames.2013.6820611
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

ASJC Scopus subject areas

Computer Networks and Communications
Human-Computer Interaction
Electrical and Electronic Engineering

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Lee, J, Lim, J, Cho, W & Kim, HK 2013, I know what the BOTs did yesterday: Full action sequence analysis using Naïve Bayesian algorithm. in Annual Workshop on Network and Systems Support for Games., 6820611, IEEE Computer Society, 2013 12th Annual Workshop on Network and Systems Support for Games, NetGames 2013, Denver, CO, United States, 13/12/9. https://doi.org/10.1109/NetGames.2013.6820611

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title = "I know what the BOTs did yesterday: Full action sequence analysis using Na{\"i}ve Bayesian algorithm",

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.",

keywords = "BOT detection, Na{\"i}ve Bayesian classifier, online game security, sequence data",

author = "Jina Lee and Jiyoun Lim and Wonjun Cho and Kim, {Huy Kang}",

year = "2013",

month = jan,

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doi = "10.1109/NetGames.2013.6820611",

language = "English",

isbn = "9781479929610",

booktitle = "Annual Workshop on Network and Systems Support for Games",

publisher = "IEEE Computer Society",

note = "2013 12th Annual Workshop on Network and Systems Support for Games, NetGames 2013 ; Conference date: 09-12-2013 Through 10-12-2013",

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T1 - I know what the BOTs did yesterday

T2 - 2013 12th Annual Workshop on Network and Systems Support for Games, NetGames 2013

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

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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

Y2 - 9 December 2013 through 10 December 2013

ER -

I know what the BOTs did yesterday: Full action sequence analysis using Naïve Bayesian algorithm

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