A Game-Theoretic Model for Defending Against Malicious Users in RecDroid Bahman Rashidi December 5 th, 2014

1 Overview -Introduction -RecDroid system -Game theoretic model -Nash equilibrium -Discussion -Conclusion

2 RecDroid system -What is RecDroid? -A framework, to improve and assist mobile (smartphone) users to control their resource and privacy through crowd sourcing. -Android OS permission granting All-or-Nothing -Two app installation modes: -Probation -Trusted -Real-time resource granting decisions -Expert and peer recommendation system

3 RecDroid system (cont.) -RecDroid UI Installation ProcessRecommendation

4 RecDroid system (cont.) -RecDroid Functionalities: 1.Collecting permission-request responses 2.Analyzing the responses 3.Recommend low-risk responses to permission requests 4.Expanding expert user base 5.Ranking the apps

5 RecDroid system (cont.) -RecDroid’s Components  Verification system  Environment Knowledge  Expert users  Users  Malicious  Regular

6 RecDroid system (cont.) -Verification system  Environment knowledge  Previous responses  User behavior  App developer  Game model  Users’ type prediction  Security improvement

7 Game Theoretic Model -Normal- Form Representation  2 Players  Users (Malicious, Regular)  RecDroid system  Strategies space  Users  Malicious (Malicious, Not Malicious)  Regular (Not malicious)  RecDroid (Verify, Not verify)

8 Game Theoretic Model (cont.) -Normal- Form Representation  Payoff  Common parameters  Special parameters - Security value - Equal to gain/loss (both of them) -Loss of reputation (RecDroid) -Loss of secrecy (Malicious users) Cost of verification (RecDroid) Cost of responding (Maliciously) Recognition rate (true positive) of the RecDroid False alarm rate (false positive rate)

9 Game Theoretic Model (cont.) -Payoff matrix  Player i is malicious  Player i is regular

10 Game Theoretic Model (cont.) -Extensive form

11 Game Theoretic Model (cont.) -Bayesian Nash equilibrium  (Malicious (malicious user), Not malicious (regular user)) (Malicious, Verify), Not BNE if(Malicious, Verify) (Malicious, Not Verify), Pure strategy BNE

12 Game Theoretic Model (cont.) -Bayesian Nash equilibrium  (Not Malicious (malicious user), Not malicious (regular user))

13 Game Theoretic Model (cont.) -Bayesian Nash equilibrium  We analyzed all the existing strategy combinations  No pure-strategy when  Mixed-strategy

14 Game Theoretic Model (cont.) -Bayesian Nash equilibrium  Mixed-strategy p : user plays Malicious q : RecDroid plays Verify

15 Discussion p is high, RecDroid has a high outcome p is low, User has a high outcome

16 Conclusion

Thank you !!! Question?