Detecting Bad Mouthing Behavior in Reputation Systems
                                        Kuan-Ta Chen (Chun-Yang Chen...
Upcoming SlideShare
Loading in …5
×

Detecting Bad Mouthing Behavior in Reputation Systems

731 views
681 views

Published on

Published in: Technology, News & Politics
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
731
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
10
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Detecting Bad Mouthing Behavior in Reputation Systems

  1. 1. Detecting Bad Mouthing Behavior in Reputation Systems Kuan-Ta Chen (Chun-Yang Chen) , Cheng-Chun Lou , Polly Huang , and Ling-Jyh Chen 1 2 2 1 1 Academia Sinica, 2National Taiwan University Background Hypothesis Votee-Voter-based Collusion Cluster Detection • MMORPGs (Massively Multiplayer Online Role-Playing Games) • The most voters of legitimate players are likely collusion cluster The relationship of victim group is stronger than random votee cluster have become extremely popular • In case if a collusion cluster attacks for several times First take the votees with more common voters 1. Collusion Cluster has more common votees than random • form the victim group • Game bots voter cluster ○ Auto-playing game clients 2. Victims have more common voters than random votee cluster Then take union of the voters of the victim group ○ One of the greatest threats of MMORPGs • form the collusion cluster candidate • Based-on Apply Voter-based Collusion Cluster Detection • Detection of Game Bots ○ The voters & votees id of each player ○ Manual detection (game master) [1] • Note ○ Traffic analysis approach [2] ○ When not attack, a collusive player acts as a legitimate player Performance Evaluation Results ○ Voting-based system [3] Voter-Based Votee-Voter-Based Comparison - Each player votes the suspicious player as a game bot Voter-Based Collusion Cluster Detection # of Attack (Single CC or Multiple CC) More attacks  higher accuracy Votee-Voter > Voter The relationship of collusion cluster is stronger than random voter cluster No influence Need more than 3 Motivation Take the voters with more common votees between each other Collusion Cluster Size high accuracy high accuracy Voter > Votee-Voter Prob. of Collusive Player • form the collusion cluster. Attack Vote Higher Prob.  higher accuracy Votee-Voter > Voter • Problem in voting-based system General Case Malicious Case 3 ID Votee List 4 1 1 11, 12, 13, 14, 17 Voter-Based scheme: 5 4 2 2 11, 12, 13, 14, 15, 18 Robust to the size of collusion cluster 1 4 3 9, 13, 14, 15 4 3 3 4 11, 12, 13, 14 Voter-Votee-Based scheme: BOT BOT 1 Robust to the number of attacks BO BO BO BO 2 4 1 5 12, 13, 14, 15, 25, 60 1 T T T T 2 1 9 17, 25, 30 BO T B OT 1 3 3 18 10 17, 18, 50, 56 Conclusion 10 11 17, 18, 70, 44 • Two mechanisms to detect the collusion cluster BOT User 1 1 12 17, 20, 35 ○ Based on the voting history 1 • Collusion 13 13 40, 45, 50 ○ Single cluster or multiple clusters 1 14 55, 80, 90 ○ A secret agreement between two or more parties for a fraudulent, 17 9 1 illegal, or deceitful purpose [4] 1 1 15 20, 30, 40 • Accuracy - Unfairly low ratings – bad mouthing 14 1 1 17 1 ○ Attack more than three times: 83%+ 1 - Unfairly high ratings – ballot stuffing 18 17, 60 ○ Attack more than five times: 97%+ 15 1 12 11 Weight (A, B): # of common • Only can vote negatively (game bot) 1 votees of A and B • Adjust other experimental factors ○ Only collusion cluster size and prob. of collusive player attack vote ○ This study focuses on bad-mouthing attacks < Algorithm > have the obvious influence to the accuracy while ( edge(G ) ∉ Φ ) Problem Formulation Take the edge with the largest weight • Bad-Mouthing while (not termination condition ) Furture Work Take the outlier edge with the largest weight Detecting players who participate in multiple collusion clusters ○ A malicious group deliberately vote a legitimate player as a end while game bot output [1] I. MacInnes and L. Hu, “Business models and operational issues in the chinese online game • Terms end while industry,” Telematics and Informatics, vol. 24, no. 2, pp. 130-144, 2007 ○ Collusion Cluster: a bad-mouthing group [2] K.-T. Chen, J.-W. Jiang, P. Huang, H.-H. Chu, C.-L. Lei, and W.-C. Chen, “Identifying mmor- Terminal Condition: ○ Victim: the legitimate players who are under bad-mouthing attacks ∑{weight (u,v) | u, v ⊂ S ' } − ∑{weight (u,v) | u, v ⊂ S} < a pg bots: A traffic analysis approach,” in Proceedings of ACM SIGCHI ACE, 2006 • Goal S [3] Blizzard, http://www.blizzard.com/war3/ To Detect the Collusion Clusters [4] http://www.answers.com/collusion?cat=biz-fin

×