Game Bot Detection
       Based on Avatar Trajectories
                 Kuan-Ta Chen            Academia Sinica
          ...
Game Bots
       AI programs that can perform some tasks in place of
       gamers
       Popular in MMORPG and FPS games
...
Bot Detection

       Detecting whether a character is controlled by a bot is
       difficult since a bot obeys the game ...
Earlier Bot Prevention / Detection Work
       Prevention
            CAPTCHA tests
            (Completely Automated Publ...
CAPTCHA in a Japanese Online Game




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   5
Our Goal for Bot Detection

        Passive detection
           No intrusion in players’ gaming experience
        No cli...
Our Solution: Trajectory-based Detection

       Based on the avatar’s moving trajectory in game
       Applicable for all...
The Rationale behind Our Scheme

       The trajectory of the avatar controlled by a human
       player is hard to simula...
Bot Detection: A Decision Problem
                 Q: Whether a bot is controlling a game client given
                   ...
Talk Progress

                Overview
                Data Description
                Proposed Schemes
                ...
Case Study: Quake 2

       Choose Quake 2 as our case study
            A classic FPS game
            Many real-life hum...
A Screen Shot of Quake 2




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   12
Data Collection
       Human traces downloaded from GotFrag Quake, Planet Quake,
       Demo Squad, and Revilla Quake Site...
Data Representation




            t               (X, Y)                   (X, Y)             (X, Y)



Hsing-Kuo Kennet...
Trails of Human Players




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   15
Trails of CRBot




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   16
Trails of Eraser Bot




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   17
Trails of ICE Bot




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   18
Talk Progress

                Overview
                Data Description
                Proposed Schemes
                ...
Proposed Schemes

       Scheme 1 – Feature Based: Extraction of Important
       Features, Combined with Decision Tree
  ...
Feature Extraction

       Given a segment of a trajectory, {xt, yt}, 1 ≤ t ≤ T, we
       extract the following features ...
Movement Trail Analysis
       Activity
            mean/sd of ON/OFF periods
       Pace (1 step)
            speed/offse...
ON/OFF Activity Features




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   23
Pace-Related Features




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   24
Path-Related Features




                                                                               path length
     ...
Turn-Related Features




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   26
Data Point Mapping using the First Three
                      Principle Components




Hsing-Kuo Kenneth Pao / Game Bot D...
Classification Results using Decision Tree




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   28
Proposed Schemes

       Scheme 1 – Feature Based: Extraction of Important
       Features, Combined with Decision Tree
  ...
The Complete Process: Overview




                                                     Decision


Hsing-Kuo Kenneth Pao /...
Step 1. Pace Vector Construction

       For trace sn , we compute the pace in consecutive two
       seconds by

        ...
Examples of Pace Vectors




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   32
Step 2. Dimension Reduction with Isomap
       Pace vectors have 201 dimensions
       We adopt Isomap for dimension reduc...
A Graphic Representation of Isomap




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   34
Distance Measure between Pace Vectors

   1. Euclidean Distance
   2. Kullback-Leibler divergence (or KL distance)



    ...
PCA (Linear) vs. Isomap (Nonlinear)




Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory   36
Step 3. Classification

       Apply a supervised classifier on the Isomap-reduced
       pace vectors
       To decide wh...
Five Methods for Comparison


         Method                                                   Data Input
         kNN
  ...
Evaluation Results


                                                                        Error Rate



       False Po...
Talk Progress

                Overview
                Data Description
                Proposed Schemes
                ...
Conclusion

       We propose a trajectory-based approach for detecting
       game bots.
       Feature-based method show...
Questions?




ICEC IFIP 2008
Thank You!



                 Hsing-Kuo Kenneth Pao




ICEC IFIP 2008
Addition of Gaussian Noise

       Bot programmers can try to evade from detection by
       adding random perturbation in...
Addition of Gaussian Noise (cont)

                     Human                                              Bots




      ...
Evaluation Results


                                                                        Error Rate



       False Po...
Cross-Maps Validation

       Sometimes human movement may be restricted by the
       environment around him/her.
       ...
Evaluation Results


                                                                         Error Rate



       False P...
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Game Bot Detection Based on Avatar Trajectory

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In recent years, online gaming has become one of the most popular Internet activities, but cheating activity, such as the use of game bots, has increased as a consequence. Generally, the gaming community disagrees with the use of game bots, as bot users obtain unreasonable rewards without corresponding efforts. However, bots are hard to detect because they are designed to simulate human game playing behavior and they follow game rules exactly. Existing detection approaches either interrupt the players’gaming experience, or they assume game bots are run as standalone clients or assigned a specific goal, such as aim bots in FPS games.

In this paper, we propose a trajectory-based approach to detect game bots. It is a general technique that can be applied to any game in which the avatar’s movement is controlled directly by the players. Through real-life data traces, we show that the trajectories of human players and those of game bots are very different. In addition, although game bots may endeavor to simulate players’ decisions, certain human behavior patterns are difficult to mimic because they are AI-hard. Taking Quake 2 as a case study, we evaluate our scheme’s performance based on reallife traces. The results show that the scheme can achieve a detection accuracy of 95% or higher given a trace of 200 seconds or longer.

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Game Bot Detection Based on Avatar Trajectory

  1. 1. Game Bot Detection Based on Avatar Trajectories Kuan-Ta Chen Academia Sinica Andrew Liao NTU Hsing-Kuo Kenneth Pao NTUST Hao-Hua Chu NTU ICEC IFIP 2008
  2. 2. Game Bots AI programs that can perform some tasks in place of gamers Popular in MMORPG and FPS games MMORPGs (Massive Multiplayer Role Playing Games) accumulating rewards efficiently in 24 hours a day breaking the balance of power and economies in games FPS games (First-Person Shooting Games) a) improving aiming accuracy b) fully automated achieving high ranking without proficient skills and efforts Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 2
  3. 3. Bot Detection Detecting whether a character is controlled by a bot is difficult since a bot obeys the game rules perfectly No general detection methods are available today State of practice is identifying via human intelligence Detection: Bots may show regular or peculiar behavior; Confirmation: Bots cannot talk like humans Labor-intensive and may annoy innocent players Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 3
  4. 4. Earlier Bot Prevention / Detection Work Prevention CAPTCHA tests (Completely Automated Public Turing test to tell Computer and Human Apart) Detection Process monitoring at client side Constantly changing the bot program’s signature Traffic analysis at the network Remove bot traffic’s regularity by heavy-tailed random delays Aiming bot detection using Dynamic Bayesian Network Specific to aiming bots that help aim the target accurately Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 4
  5. 5. CAPTCHA in a Japanese Online Game Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 5
  6. 6. Our Goal for Bot Detection Passive detection No intrusion in players’ gaming experience No client software is required Generalizable schemes (for other games and other game genres) Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 6
  7. 7. Our Solution: Trajectory-based Detection Based on the avatar’s moving trajectory in game Applicable for all genres of games where players control the avatar’s movement directly Avatar’s trajectory is high-dimensional (both in time and spatial domains) Use manifold learning to distinguish the trajectories of human players and game bots Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 7
  8. 8. The Rationale behind Our Scheme The trajectory of the avatar controlled by a human player is hard to simulate for two reasons: Complex context information: Players control the movement of avatars based on their knowledge, experience, intuition, and a great deal of information provided in the game. Human is not always logical and optimal How to model and simulate realistic movements is still an open question in the AI field?! Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 8
  9. 9. Bot Detection: A Decision Problem Q: Whether a bot is controlling a game client given the movement trajectory of the avatar? A: Yes / No? Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 9
  10. 10. Talk Progress Overview Data Description Proposed Schemes Feature-based Manifold learning: Dimensionality Reduction using Isomap Conclusion Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 10
  11. 11. Case Study: Quake 2 Choose Quake 2 as our case study A classic FPS game Many real-life human traces are available on the Internet more realistic than traces collected in experiments Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 11
  12. 12. A Screen Shot of Quake 2 Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 12
  13. 13. Data Collection Human traces downloaded from GotFrag Quake, Planet Quake, Demo Squad, and Revilla Quake Sites Bot traces collected on our own Quake server CR BOT 1.14 Eraser Bot 1.01 ICE Bot 1.0 Each cut into 1,000-second segments Totally 143.8 hours of traces were collected Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 13
  14. 14. Data Representation t (X, Y) (X, Y) (X, Y) Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 14
  15. 15. Trails of Human Players Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 15
  16. 16. Trails of CRBot Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 16
  17. 17. Trails of Eraser Bot Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 17
  18. 18. Trails of ICE Bot Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 18
  19. 19. Talk Progress Overview Data Description Proposed Schemes Feature-based Manifold learning: Dimensionality Reduction using Isomap Conclusion Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 19
  20. 20. Proposed Schemes Scheme 1 – Feature Based: Extraction of Important Features, Combined with Decision Tree ON/OFF Activity Pace Path Turn Scheme 2 – Manifold Learning Based High-dimensional data Dimension reduction Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 20
  21. 21. Feature Extraction Given a segment of a trajectory, {xt, yt}, 1 ≤ t ≤ T, we extract the following features from this two-dimensional time series: ON/OFF Activity Pace Path Turn Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 21
  22. 22. Movement Trail Analysis Activity mean/sd of ON/OFF periods Pace (1 step) speed/offset in each time period teleportation frequency Path (several steps) linger frequency/length smoothness detourness Turn frequency of mild turns, wild turns, and U-turns Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 22
  23. 23. ON/OFF Activity Features Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 23
  24. 24. Pace-Related Features Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 24
  25. 25. Path-Related Features path length detourness = effset Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 25
  26. 26. Turn-Related Features Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 26
  27. 27. Data Point Mapping using the First Three Principle Components Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 27
  28. 28. Classification Results using Decision Tree Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 28
  29. 29. Proposed Schemes Scheme 1 – Feature Based: Extraction of Important Features, Combined with Decision Tree ON/OFF Activity Pace Path Turn Scheme 2 – Manifold Learning Based High-dimensional data Dimension reduction Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 29
  30. 30. The Complete Process: Overview Decision Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 30
  31. 31. Step 1. Pace Vector Construction For trace sn , we compute the pace in consecutive two seconds by sn,i+1 − sn,i = (sn,i+1 − sn,i )T (sn,i+1 − sn,i ) We then compute the distribution of pace lengths with a fixed bin size by Fn = (fn,1 , fn,2 , . . . , fn,B ) where B is the number of bins in the distribution. Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 31
  32. 32. Examples of Pace Vectors Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 32
  33. 33. Step 2. Dimension Reduction with Isomap Pace vectors have 201 dimensions We adopt Isomap for dimension reduction for Better performance Reduce computation overhead in classification Isomap Assume data points lie on a smooth manifold Construct the neighborhood graph by kNN (k-nearest neighbors) Compute the shortest path for each pair of points Reconstruct data by MDS (multidimensional scaling) Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 33
  34. 34. A Graphic Representation of Isomap Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 34
  35. 35. Distance Measure between Pace Vectors 1. Euclidean Distance 2. Kullback-Leibler divergence (or KL distance) where P & Q are feature vectors of dimension d Because KL distance is not symmetric, we use a symmetric version instead Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 35
  36. 36. PCA (Linear) vs. Isomap (Nonlinear) Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 36
  37. 37. Step 3. Classification Apply a supervised classifier on the Isomap-reduced pace vectors To decide whether a pace vector belongs to a bot or a human player Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 37
  38. 38. Five Methods for Comparison Method Data Input kNN Linear SVM Pace Vectors Nonlinear SVM Isomap + kNN Isomap-reduced Pace Isomap + Nonlinear SVM Vectors Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 38
  39. 39. Evaluation Results Error Rate False Positive Rate False Negative Rate Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 39
  40. 40. Talk Progress Overview Data Description Proposed Schemes Feature-based Manifold learning: Dimensionality Reduction using Isomap Conclusion Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 40
  41. 41. Conclusion We propose a trajectory-based approach for detecting game bots. Feature-based method shows descent result We can improve the result and efficiency further by Isomap + nonlinear SVM approach. Human’s logic in controlling avatars is hard to simulate we believe this approach has the potential to be a general yet robust bot detection methodology Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 41
  42. 42. Questions? ICEC IFIP 2008
  43. 43. Thank You! Hsing-Kuo Kenneth Pao ICEC IFIP 2008
  44. 44. Addition of Gaussian Noise Bot programmers can try to evade from detection by adding random perturbation into bots’ movement behavior Evaluate the robustness of our scheme by adding Gaussian noise into bots’ trajectories Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 44
  45. 45. Addition of Gaussian Noise (cont) Human Bots Gaussian Noise Bots with Noise Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 45
  46. 46. Evaluation Results Error Rate False Positive Rate False Negative Rate Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 46
  47. 47. Cross-Maps Validation Sometimes human movement may be restricted by the environment around him/her. Whether a classifier trained for a map can be used for detecting bots on another map? The Edge The Frag Pipe Warehouse Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 47
  48. 48. Evaluation Results Error Rate False Postive Rate False Negative Rate Hsing-Kuo Kenneth Pao / Game Bot Detection Based on Avatar Trajectory 48
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