Table 2: Summary of human and bot entropy values.
The decision threshold between human and bot players is set
as the mid-point of the average entropy values of human and bot users;
i.e., we choose (9.33+8.56)/2=8.95
as the threshold.
Table 3: Summary of data and results.
There are 138 traces, each of 10,000 seconds.
We use 80% of them for training and
the remainder for testing.
Table 5: The results based on six different detection schemes,
given the step-size inputs,
i.e., no temporal information
is considered. The table shows the average results of the
false positive rates, false negative rates and error rates,
after 10 repeats of the 10-fold cross-validation procedure.
Overall, the performance is enhanced if Isomap is applied.
The only exception is the poor performance
when applying linear SSVM in a low-dimensional space.
The decision boundary tends to become nonlinear
in a low-dimensional space;
thus, linear SSVM may not be appropriate in this case.
Figure 9: The histograms of the step sizes for trajectories
of (a) bot users and (b) human users.
A large number of constant size steps are found
in bot trajectories, but not in human trajectories.
However, after adding some Gaussian noise in (a) to (a1),
we obtain a histogram that is hard to distinguish, at least visually,
from that for a human user.
[1]
K.-T. Chen, J.-W. Jiang, P. Huang, H.-H. Chu, C.-L. Lei, and W.-C. Chen,
"
Identifying MMORPG Bots: A Traffic Analysis Approach,"
EURASIP
Journal on Advances in Signal Processing, 2009.
[2]
P. Golle and N. Ducheneaut, "Preventing bots from playing online games,"
Computers in Entertainment, vol. 3, no. 3, pp. 3-3, 2005.
[3]
J. Dibbell and M. Video, "The life of the Chinese gold farmer,"
Life, 2007.
[4]
K.-T. Chen and L.-W. Hong, "
User Identification based on Game-Play Activity Patterns," in
NetGames 07: Proceedings of the 6th ACM SIGCOMM workshop
on Network and System Support for Games. ACM, 2007, pp. 7-12.
[5]
L. von Ahn, M. Blum, N. J. Hopper, and J. Langford, "CAPTCHA: Using hard
AI problems for security," in
Proceedings of Eurocrypt, 2003, pp.
294-311.
[6]
P. Hingston, "A turing test for computer game bots,"
IEEE Transactions
on Computational Intelligence and AI in Games, vol. 1, no. 3, pp. 169-186,
2009.
[7]
T. P. Novak, D. L. Hoffman, and A. Duhachek, "The influence of goal-directed
and experiential activities on online flow experiences,"
Journal of
Consumer Psychology, vol. 13, no. 1, pp. 3-16, 2003.
[8]
S. Ila, D. Mizerski, and D. Lam, "Comparing the effect of habit in the online
game play of Australian and Indonesian gamers," in
Proceedings of
the Australia and New Zealand Marketing Association Conference, 2003.
[9]
S. F. Yeung, J. C. S. Lui, J. Liu, and J. Yan, "Detecting cheaters for
multiplayer games: theory, design and implementation,"
Consumer
Communications and Networking Conference, 2006. 3rd IEEE, vol. 2, no. 8-10,
pp. 1178-1182, 2006.
[10]
C. M. Bishop,
Pattern Recognition and Machine Learning. Springer, 2006.
[11]
J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric
framework for nonlinear dimensionality reduction."
Science, vol. 290,
no. 5500, pp. 2319-2323, December 2000.
[12]
T. M. Cover and J. A. Thomas,
Elements of Information Theory (2nd
Ed.). Wiley-Interscience, July 2006.
[13]
H. Kim, S. Hong, and J. Kim, "Detection of auto programs for MMORPGs," in
Proceedings of AI 2005: Advances in Artificial Intelligence, 2005, pp.
1281-1284.
[14]
C. Thurau, C. Bauckhage, and G. Sagerer, "Learning human-like movement
behavior for computer games," in
In Proc. 8th Int. Conf. on the
Simulation of Adaptive Behavior (SAB'04). IEEE Press, 2004, pp. 315-323.
[15]
C. Thurau, C. Bauckhauge, and G. Sagerer, "Combining self organizing maps and
multilayer perceptrons to learn bot-behavior for a commercial game," in
Proceedings of the GAME-ON03 Conference, 2003, pp. 119-123.
[16]
C. Thurau and C. Bauckhage, "Towards manifold learning for gamebot behavior
modeling," in
In Proc. Int. Conf. on Advances in Computer
Entertainment Technolog (ACE'05), 2005, pp. 446-449.
[17]
C. Thurau, T. Paczian, and C. Bauckhage, "Is bayesian imitation learning the
route to believable gamebots?" in
In Proc. GAME-ON North America,
2005, pp. 3-9.
[18]
C. Thurau and C. Bauckhage, "Tactical waypoint maps: Towards imitating tactics
in fps games," in
Proc. 3rd International Game Design and Technology
Workshop and Conference (GDTW'05), M. Merabti, N. Lee, and M. Overmars,
Eds., 2005, pp. 140-144.
[19]
E. Keogh, S. Lonardi, and C. A. Ratanamahatana, "Towards parameter-free data
mining," in
KDD '04: Proc. of the 10th ACM SIGKDD inter. conf. on
Knowledge discovery and data mining, 2004, pp. 206-215.
[20]
H.-K. Pao and J. Case, "Computing entropy for ortholog detection," in
International Conference on Computational Intelligence, 2004, pp.
89-92.
[21]
M. Li, J. H. Badger, X. Chen, S. Kwong, P. Kearney, and H. Zhang, "An
information-based sequence distance and its application to whole
mitochondrial genome phylogeny,"
Bioinformatics, vol. 17, no. 2, pp.
149-154, 2001.
[22]
M. Li and P. Vitányi,
An Introduction to Kolmogorov Complexity and
Its Applications (2nd Ed.). New York:
Springer, 1997.
[23]
J. Lin, E. Keogh, S. Lonardi, and B. Chiu, "A symbolic representation of time
series, with implications for streaming algorithms," in
Proc. of the
8th ACM SIGMOD Workshop on Res. Issues in Data Mining and Knowledge
Discovery, 2003, pp. 2-11.
[24]
"id Software, Inc."
http://www.idsoftware.com/.
[25]
"Id Software: id History,"
http://www.idsoftware.com/business/history/.
[26]
M. Malakhov, "CR Bot 1.15," May 2000,
http://arton.cunst.net/quake/crbot/.
[27]
R. R. Feltrin, "Eraser Bot 1.01," May 2000,
http://downloads.gamezone.com/demos/d9862.htm.
[28]
jibe, "ICE Bot 1.0," 1998,
http://ice.planetquake.gamespy.com/.
[29]
K.-T. Chen, A. Liao, H.-K. K. Pao, and H.-H. Chu, "
Game Bot Detection Based on Avatar Trajectory," in
Proceedings of IFIP ICEC 2008, 2008.
[30]
V. N. Vapnik,
The Nature of Statistical Learning Theory, 2nd ed. Springer, November 1999.
[31]
C. J. C. Burges, "A tutorial on support vector machines for pattern
recognition,"
Data Mining and Knowledge Discovery, vol. 2, no. 2, pp.
121-167, 1998.
[32]
T. F. Cox and M. A. A. Cox,
Multidimensional Scaling, Second
Edition. Chapman & Hall/CRC, 2000.
[33]
H. Hotelling, "Analysis of a complex of statistical variables into principal
components,"
J. of Educational Psychology, vol. 24, pp. 417-441,
1933.
[34]
Y.-J. Lee and O. L. Mangasarian, "Ssvm: A smooth support vector machine for
classification,"
Comput. Optim. Appl., vol. 20, no. 1, pp. 5-22,
2001.
[35]
G. Shakhnarovich, T. Darrell, and P. Indyk,
Nearest-Neighbor Methods in
Learning and Vision: Theory and Practice. The MIT Press, 2006.
[36]
B. Schölkopf and A. Smola,
Learning with Kernels Support Vector
Machines, Regularization, Optimization and Beyond. Cambridge, MA, USA: MIT Press, 2002.
[37]
K.-T. Chen, H.-K. K. Pao, and H.-C. Chang, "
Game Bot Identification based on Manifold Learning," in
Proceedings of ACM NetGames 2008, 2008.