1. Stopping cheaters since 15-06-2012 By: Tigran Gasparian

2.  Motivation  Basics – Protecting highscores  Basics – Online games  Bot Detection – Motivation  Bot Detection – General  Bot Detection – MMOs

3.  You can earn money from it  It’s fun

4.  Less fun for non-cheaters  Damages your game economy  Shortens the lifespan of your game  What about offline games?

5.  Make it difficult to cheat  Make sure it’s too much trouble to cheat.  Encryption  White box cryptography  Send extra information.  Use parallel protocols  Honeypot  Delayed ban

6.  Types of data:  Number of enemies killed  Play time  Number of clicks  Etc.

7.  Handle incorrect data  Plain-text highscores  Incorrect extra info  Incorrect syntax  Etc.

8.  When detecting a false submission  Show it in the highscore table  Only for the cheater  Other players don’t see it  Cheater thinks he succeded  He might stop trying.

9.  Multiple cheating methods available  Ban at a random time  e.g. between 1-2 weeks after detection  What got him caught?  Potential danger?

10.  Never trust the client  The client might not even be a client  Always check some data  Performance vs Security  Where to do physics?

11.  User can change their game client  Usually to gain more information. ▪ Make walls transparent ▪ Make camouflage bright ▪ Make models bigger ▪ Etc.  Check hashes of game data files.

12.  A program that plays the game for you.  Scripts that send input into the game client  Stand-alone programs ▪ Sending packets to the server like the real client  Types:  Aim bots  Player bots  Gold/EXP farmers

13.  Bot’s don’t break the game laws  They just automate player actions  The only thing we can do is detect them  And ban them of course!

14.  Traditional approach – CAPTCHA  Websites use it, it works great!

15.  Something more user friendly.  Detection by behavior  Bots act … weird ▪ It’s very hard to exactly simulate human behavior ▪ Especially the movement

16.  Analyze data you already have  Position  Orientation  Etc.  Compare bots to humans  Define features  Train a neural network to detect bots.  ?????  Profit!

17.  Data we use for our analysis  Position  Orientation  Features ▪ On/off time ▪ Movement speed ▪ Path smoothness, detours, zig-zagness ▪ Rotations 30°, 60°, 90°

18.  Simple learning algorithm  95% detection rate  With 200 seconds of game info  This %&#$ works! See Game Bot Detection Based on Avatar Trajectory for the article

19.  Repetitions in path  Very few detours  Capture position data  Make a simplified path  Count segment passes  Count repeating sub path length  Draw conclusions