Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Application of Visualization in Clothing Thermal Computational Design Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Online Game Bot Detection Based on Extreme Learning Machine

Authors : Xu Huang, Jing Fan, Shaowen Gao, Wenjun Hu

Published in: Transactions on Edutainment XIV

Publisher: Springer Berlin Heidelberg

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Some players of Massively Multiuser Online Role-Playing Games (MMORPG) manipulate game bots to accumulate property quickly in the game world, for getting a high-level experience quickly without spending too much time and energy. It has a great impact on the game experience of human players, and lead to an unfair phenomenon in games. We analyze and screen players in online games to quickly capture game bots, and let game operators do subsequent processing. First, we analyze game log data and arrange user behavior sequences to form a matrix with user information. Second, Extreme Learning Machine (ELM) is used for classification and screening. Some traditional classification methods, i.e. SVM and KNN, are used on the same data to verify the algorithm effect. Empirical study demonstrates that the proposed method is competitive with some traditional methods in terms of accuracy and efficiency.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Study of Animation Simulation Technology for Earthquake Escape Education
next chapter Attention Decrease Detection Based on Video Analysis in E-Learning
Literature
1.
Oh, J., Borbora, Z.H., Sharma, D., Srivastava, J.: Bot detection based on social interactions in MMORPGs. In: International Conference on Social Computing, vol. 10(1), pp. 536–543 (2013)
2.
Novak, T., Hoffman, D., Duhachek, A.: The influence of global directed and experiential activities on online flow experiences. J. Consum. Psychol. 13(1-2), 3–16 (2003)CrossRef
3.
Kang, A.R., Woo, J., Park, J., Kim, H.K.: Online game bot detection based on party-play log analysis. Comput. Math Appl. 65, 1384–1395 (2013)CrossRef
4.
Mitterhofer, S., Krügel, C., Kirda, E., Platzer, C.: Server-side bot detection in massively multiplayer online games. IEEE Secur. Priv. 7(3), 29–36 (2009)CrossRef
5.
Chen, K.-T., Jiang, J.-W., Huang, P., Chu, H.-H., Lei, C.-L., Chen, W.-C.: Identifying mmorpg bots: a traffic analysis approach. EURASIP J. Adv. Signal Process. 2009, 22 (2009)
6.
Hilaire, S., Chul Kim, H., Kim, C.-K.: How to deal with bot scum in mmorpgs? In: 2010 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), pp. 1–6 (2010)
7.
Thawonmas, R., Kashifuji, Y., Chen, K.-T.: Detection of MMORPG bots based on behavior analysis. In: Advances in Computer Entertainment Technology, pp. 91–94 (2008)
8.
Ahmad, M.A., Keegan, B., Srivastava, J., Williams, D., Contractor, N.S.: Mining for gold farmers: automatic detection of deviant players in MMOGS. In: CSE, vol. 4, pp. 340–345 (2009)
9.
Yeung, S., Liu, J.-S., Lui, J., Yan, J.: Detecting cheaters for multiplayer games: theory, design and implementation. In: 3rd IEEE 2006 Consumer Communications and Networking Conference, CCNC 2006, vol. 2, pp. 1178–1182 (2006)
10.
Chen, K.-T., Hong, L.-W.: User indentification based on game-play activity patterns. In: NETGAMES, pp. 7–12 (2007)
11.
Varvello, M., Voelker., G.M.: Second life: a social network of humans and bots. In: Proceedings of the 20th International Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2010, pp. 9–14. ACM (2010)
12.
van Kesteren, M., Langevoort, J., Grootjen, F.: A step in the right direction: bot detection in MMORPGS using movement analysis. In: Proceedings of the 21st Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2009) (2009)
13.
Thawonmas, R., Kurashige, M., Iizuka, K., Kantardzic, M.: Clustering of online game users based on their trails using self-organizing map. In: Harper, R., Rauterberg, M., Combetto, M. (eds.) ICEC 2006. LNCS, vol. 4161, pp. 366–369. Springer, Heidelberg (2006). https://​doi.​org/​10.​1007/​11872320_​51 CrossRef
14.
Thawonmas, R., Kurashige, M., Chen, K.-T.: Detection of landmarks for clustering of online-game players. IJVR 6(3), 11–16 (2007)
15.
16.
Gianvecchio, S., Wu, Z., Xie, M., Wang, H.: Battle of botcraft: fighting bots in online games with human observational proofs. In: Proceedings of the 16th ACM Conference on Computer and Communications Security, CCS 2009, NY, USA, pp. 256–268. ACM, New York (2009)
17.
Choi, Y., Chang, S., Kim, Y., Lee, H., Son, W., Jin, S.: Detecting and monitoring game bots based on large-scale user-behavior log data analysis in multiplayer online games. J. Supercomput. 72, 3572–3587 (2016)CrossRef
18.
Yong, L., Wenliang, H., Yunliang, J., Zhiyong, Z.: Quick attribute reduct algorithm for neighborhood rough set model. Inf. Sci. V271, 65–81 (2014)MathSciNetCrossRefMATH
19.
Jiang Yunliang, X., Yunxi, L.Y.: Performance evaluation of feature and matching in stereo visual odometry. Neurocomputing 120, 380–390 (2013)CrossRef
20.
Huang, G., Huang, G.-B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015)CrossRefMATH
21.
Huang, G., Song, S., Gupta, J.N.D., Wu, C.: Semi-supervised and unsupervised extreme learning machines. IEEE Trans. Cybern. 44(12), 2405–2417 (2014)CrossRef
22.
He, Q., Jin, X., Du, C., Zhuang, F., Shi, Z.: Clustering in extreme learning machine feature space. Neurocomputing 128, 88–95 (2014)CrossRef
23.
Huang, G.-B.: An insight into extreme learning machines random Neurons, random features kernels. Cognit. Comput. 6(3), 376–390 (2014)CrossRef
Metadata
Title
Online Game Bot Detection Based on Extreme Learning Machine
Authors
Xu Huang
Jing Fan
Shaowen Gao
Wenjun Hu
Copyright Year
2018
Publisher
Springer Berlin Heidelberg
Book
Transactions on Edutainment XIV

Print ISBN: 978-3-662-56688-6

Electronic ISBN: 978-3-662-56689-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-662-56689-3_13

Premium Partner

    Image Credits