Artificial Neural Network for Bot Detection System in MMOGs

被引：0
|
作者：
Prasetya, Kusno [1 ]
Da, Wu Zheng [1 ]
机构：
[1] Bond Univ, Sch Informat Technol, Gold Coast, Qld, Australia
来源：
2010 9TH ANNUAL WORKSHOP ON NETWORK AND SYSTEMS SUPPORT FOR GAMES (NETGAMES 2010) | 2010年
关键词：
Cheating; Bot; multiplayer online games; Artificial Neural Network;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Cheating is one of the biggest and constant problems in MMOGs. Games with high frequency of cheating will surely lose its appeal to genuine players who want to play the game. This is the reason why game provider these days put cheating prevention as one of the top priorities. Bot is just one way of cheating, but very efficient one. There are various methods to prevent cheating using bot. In this paper, we examine the potential of Artificial Neural Network (ANN) to detect and recognize bot from human players. We start with the assumption that one bot always acts in the similar pattern in gameplay. Meanwhile, it is much more rarer to see 2 players with similar gameplay pattern. The result of our experiment supports our initial hypothesis with the potential for future research in order to get better results.
引用
收藏
页数：2
相关论文
共 50 条
  • [1] Fall Detection System using Artificial Neural Network
    Purushothaman, Anagha
    Vineetha, K. V.
    Kurup, Dhanesh G.
    [J]. PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT), 2018, : 1146 - 1149
  • [2] Artificial Neural Network Classifier for Intrusion Detection System in Computer Network
    Lokeswari, N.
    Rao, B. Chakradhar
    [J]. PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION TECHNOLOGIES, IC3T 2015, VOL 3, 2016, 381 : 581 - 591
  • [3] Improving Intrusion Detection System using Artificial Neural Network
    Albahar, Marwan Ali
    Binsawad, Muhammad
    Almalki, Jameel
    El-Etriby, Sherif
    Karali, Sami
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (06) : 578 - 588
  • [4] Hacker Intrusion Detection System based on Artificial Neural Network
    Huang, Jing
    Chen, Hai Bin
    Zhang, Jiang
    Zhang, Han Bo
    [J]. INFORMATION TECHNOLOGY APPLICATIONS IN INDUSTRY, PTS 1-4, 2013, 263-266 : 2924 - +
  • [5] Artificial neural network approach for fault detection in rotary system
    Rajakarunakaran, S.
    Venkumar, P.
    Devaraj, D.
    Rao, K. Surya Prakasa
    [J]. APPLIED SOFT COMPUTING, 2008, 8 (01) : 740 - 748
  • [6] ANNIDS: Intrusion detection system based on artificial neural network
    Liu, YH
    Tian, DX
    Wang, AM
    [J]. 2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 1337 - 1342
  • [7] Applying Artificial Neural Network and eXtended Classifier System for Network Intrusion Detection
    Alsharafat, Wafa'
    [J]. INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2013, 10 (03) : 230 - 238
  • [8] Artificial neural network approach for fault detection in LPG transfer system
    Rajakarunakaran, S.
    Devaraj, D.
    Venkat Ratnam, G.S.
    Surya Prakasa Rao, K.
    [J]. Advances in Modelling and Analysis B, 2007, 50 (1-2): : 36 - 51
  • [9] An Enhanced Resilient Backpropagation Artificial Neural Network for Intrusion Detection System
    Naoum, Reyadh Shaker
    Abid, Namh Abdula
    Al-Sultani, Zainab Namh
    [J]. INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2013, 13 (03): : 98 - 104
  • [10] Fly visual system inspired artificial neural network for collision detection
    Zhang, Zhuhong
    Yue, Shigang
    Zhang, Guopeng
    [J]. NEUROCOMPUTING, 2015, 153 : 221 - 234
12345