Earticle

초록

영어

On-line game service is the hottest game genre today, and one of major on-line game is MOBA (multiplayer online battle arena) game. In MOBA games, the collaboration of team players and team strategy are vital elements together, besides the individual player's game control capability. Thus important issue for the MOBA service providers is to detect bad players showing abnormal plays or appearances in games with embedded malicious intentions. Previous approach had been presented to cope with such players; however they have not yet shown any promising results to judge each player. In this paper, we propose an efficient and automatic abnormal player decision support scheme using PageRank and normal distribution to find and judge bad players. Our scheme computes BPR (bad player ranking) for each user, and BPR can be used in service provider’s final decision to decide a specific user as an abnormal player. Our scheme has main advantage that it requires small computational efforts to identify bad players, because we utilize PageRank algorithm which shows efficient computation and information search capability.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. MOBA Game and Abnormal Behavior
  2.2. THE TRIBUNAL: On-line Bad Player Decision Support
  2.3. Big Data based Approach and PageRank for Decision Support
 3. PageRank based Bad Player Identification Scheme
  3.1. Evidence Accumulation for Bad Player Identification using PageRank Algorithm
  3.2. Confidence Decision for Bad Player Identification using Normal Distribution
 4. Conclusions
 References

키워드

저자

• Jae Youn Shim [ Department of Computer Science, University of Seoul ]
• Tae Hyun Kim [ Department of Computer Science, University of Seoul ]
• Seong Whan Kim [ Department of Computer Science, University of Seoul ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605