This document summarizes a research paper that proposes using a user's game-play activity patterns, specifically their idle time distributions, to identify users for network games. The researchers collected activity log data from 287 users of an MMORPG over 3 days. They found idle time was more diverse than active time between users and could be used as a distinguishing biometric. Their identification scheme uses consistency and discriminability tests based on KL divergence distances between users' idle time distributions, which they found could accurately identify users over 90% of the time with a 20-minute detection period given 200 minutes of stored activity history.

1. May 31, 2007
Network Game Design:
User Identification based
on Game-Play Activity
Patterns
Chun-Yang Chen, Academia Sinica
Li-Wen Hong, Academia Sinica
ACM NetGames 2007

2. Motivation
Password-based User Identity
Vulnerability
Account hijacking (Identity Theft)
Severity & prevalence
No general solution until the victim appears
Account sharing
Increase the difficulty of demographical studies of game
Authors / Paper Title 2

3. User Identity: Current Solutions
Digital signature
Smart card
Biometrical signature
fingerprint
voice
keystroke
Authors / Paper Title 3

4. Our Solution
A novel biometric:
Game-Play Activity
Patterns
Authors / Paper Title 4

5. Outline
Motivation
Data Collection
Player Activity Analysis
Proposed Scheme
Performance Evaluation
Contribution & Future Work
Authors / Paper Title 5

6. Observation
More Regular More Unpredictable
Motivation Data Collection Player Activity Analysis Proposed Schemes …
Authors / Paper Title 6

7. Data Collection
A MMORPG -- Angel’s Love
A commercial game in Taiwan
40 thousands of players online
The player activity logs we use
Trace period of 3 days
287 randomly chosen accounts
Remove logs shorter than 200 minutes
Motivation Data Collection Player Activity Analysis Proposed Schemes …
Authors / Paper Title 7

8. Definitions
Active period
An active period of a game character is defined as a time
interval (t1 , t 2 ) which the character continuously moves,
in
with a tolerance of discontinuity up to 1 second.
Idle period
An idle period of a game character is defined as a time
interval (t1 , t 2 ) which the character has no movements,
in
where t 2 − t1 ≥ second.
1
Data Collection Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 8

9. Data Summary
Player # : Data Activity Active Idle
287 Length Rate Period Period
0.35
5% 7 hr 3 sec 7 sec
cycle/min
2.28
50% 51 hr 6 sec 18 sec
cycle/min
5.12
95% 67 hr 9 sec 181 sec
cycle/min
Data Collection Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 9

10. Distribution of Active / Idle Period
4
Data Collection Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 10

11. Idle time is much more diverse
than active time
Data Collection Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 11

12. Average Active Time v.s. Idle Time
Players’ active/idle patterns can be very different
candidate features for user identification
Data Collection Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 12

13. Why choosing idle time rather
than active time
Idle time distribution captures more variability
Idle time process has smaller degree of auto-
correlations
Data Collection Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 13

14. Idle Time Distribution of
Random Players
Data Collection Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 14

15. KL Distances
ITD: Idle time distribution
RET: Relative Entropy Test
relative entropy between two ITDs
based on the KL distance
KL distance: Kullback-Leibler distance
P(i )
DKL ( P || Q) = ∑ P(i ) log
i Q(i )
DSKL ( P || Q) = DSKL (Q || P) = DKL ( P || Q) + DKL (Q || P)
Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 15

16. KL distances of Players
Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 16

17. Identification Scheme:
Consistency Test
Perform consistency test
KLD: distribution of KL distance
2 KLDs for each player
KLDs are tested by two-sided Wilcoxon test
0.95
Authors / Paper Title 17

18. Identification Scheme:
Discriminability Test
Perform discriminability test
KLDi,j : distribution of KL distances between ni ITDs of player i
& nj ITDs of player j
KLDs are tested by one-sided Wilcoxon test
Authors / Paper Title 18

19. Factor Consideration
Consideration: effect of the detection time &
the history size
Trec: how long the player history kept in database
(in minutes)
Tobs : the detection time once the player log in (in
minutes)
Proposed Schemes Performance Evaluation Contribution & Future Work
Authors / Paper Title 19

20. Evaluation Result
Proposed Schemes Performance Evaluation Contribution & Future Work
Authors / Paper Title 20

21. Performance Evaluation
Effect of Trec
mean of activity cycles is one minute Trec = idle times
assuming one million user accounts, Trec = 200 minutes, each
idle time uses 4 bytes storage space = 800 MB
Effect of Tobs
assuming 10,000 players are online, Tobs = 20 minutes
main memory = 0.8 MB
Player Activity Analysis Proposed Schemes Performance Evaluation …
Authors / Paper Title 21

22. Contribution & Future Work
Contribution:
Propose the RET scheme for user identification from
the aspect of idle time.
With a 20-minute detection time period given a 200-minute
history size achieve higher than 90% accuracy.
Future Plan
Cut down the detection time
Utilizing more aspects of game-play activities.
Analyzing from the way users control the character.
Proposed Schemes Performance Evaluation Contribution & Future Work
Authors / Paper Title 22

23. Thank you!
Chun-Yang Chen
Li-Wen Hong
ACM NetGames 2007