A Game-Theoretic Model for Defending Against Malicious User - PowerPoint Presentation

Slide1

A Game-Theoretic Model for Defending Against Malicious Users in RecDroid

Bahman

Rashidi

December 5th, 2014Slide2

1

Overview

Introduction

RecDroid system

Game theoretic model

Nash equilibrium

Discussion

ConclusionSlide3

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-NothingTwo app installation modes:ProbationTrustedReal-time resource granting decisionsExpert and peer recommendation systemSlide4

3

RecDroid system (cont

.

)

RecDroid UI

Installation Process

RecommendationSlide5

4

RecDroid system

(cont.)

RecDroid Functionalities:

Collecting permission-request responses

Analyzing

the

responses

Recommend

low-risk responses

to

permission

requests

Expanding expert user base

Ranking the appsSlide6

5

RecDroid system

(cont

.)

RecDroid’s Components

Verification system

Environment Knowledge

Expert users

Users

Malicious

RegularSlide7

6

RecDroid

system (cont.)

Verification system

Environment knowledge

Previous responses

User behavior

App developer

Game model

Users’ type prediction

Security improvementSlide8

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)Slide9

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 (

R

ecDroid)

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)

 Slide10

9

Game Theoretic Model

(cont.)

Payoff matrix

Player

i

is

malicious

Player

i

is

regular

 Slide11

10

Game Theoretic Model

(cont.)

Extensive form

Node

N

represents a “

nature

” node, who determines the type of player

i

(Attacker or Regular user)

Assumption:

is a common

prior

Player

i knows RecDroid’s belief of

 Slide12

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

 Slide13

12

Game Theoretic Model

(cont.)

Bayesian Nash equilibrium

(

Not

Malicious

(malicious user),

Not

malicious

(regular user))

Regardless of

:

RecDroid’s best strategy:

Not verify (dominant)Malicious user’s best strategy: MaliciousReduces to the previous

case (

Not

BNE)

 Slide14

13

Game Theoretic Model

(cont.)

Bayesian Nash equilibrium

We analyzed all the existing strategy combinations

No pure-strategy when

Mixed-strategySlide15

14

Game Theoretic Model

(cont.)

Bayesian Nash equilibrium

Mixed-strategy

p

: user plays

Malicious

q :

RecDroid plays

Verify

((

if

Malicious

user,

Not malicious

if regular

),

,

) is the mixed-strategy

 Slide16

15

Discussion

Impact of parameters

I

mpact of

: detection rate (true positive rate)

is high

Depends on

Impact of

Impact on

p

is high

Impact of

: false

alarm rate (false positive rate

)

When malicious user plays

Not

malicious

and RecDroid plays

Verify

p

is high, RecDroid has a high outcome

p

is low, User has a high outcome

 Slide17

16

Conclusion

Modeling the RecDroid system as a game

Interaction between the system and users

Making the verification system more effective

Environment knowledge + Game model as a tool

More improvement :

Dynamic Bayesian game

Multi stage game

Improving the

and

 Slide18

Thank you !!!

Question?