Counter-measuring MAC Misbehaviors - PowerPoint Presentation

Slide1

Counter-measuring MAC Misbehaviorsin Ad Hoc Networks using Game Theory

March

25, 2010

EE5723 – Computer &

Network SecuritySlide2

Presentation Outline

Big Picture Topic Introduction

Game Theory Brief Overview

Applications in Ad Hoc Networks

Other Potential Approaches

Additional Considerations & Critiques

Presentation Conclusions

Questions & CommentsSlide3

Big Picture Topic Introduction

Selfish behavior at the MAC layer can have devastating side effects on performance of wireless networks

Communication protocols were designed under the assumption that all nodes would obey given specifications

What happens when these protocols are implemented in an environment that is not trusted?Slide4

Big Picture Topic Introduction

Nodes can deviate from the protocol specifications in order to obtain a given goal – at the expense of honest participants

A selfish user can disobey the rules to access the wireless channel to obtain a higher throughput

Change the congestion avoidance parameters

Refuse to forward packets on behalf of other sourcesSlide5

Big Picture Topic Introduction

Misbehaving nodes will degrade the performance of the network

How should one go about addressing these issues?

Focus on the prevention and detection of unfairness and collision of packets

Catch as soon as possible and punishSlide6

Game Theory Brief Overview [1]

Branch of applied mathematics

Multi-person decision making situations

Used to analyze existing systems -or-

Used as a tool when designing new systems

Implementation theory

Desired outcome is fixed and a game ending in that outcome is conjured

A system fulfilling the properties of the game can be implemented when a suitable game is discovered.Slide7

Game Theory Brief Overview [2]

In-class simple Game Theory example

A “game” (or network, etc.) can be represented as a matrix

Can clearly become more complicated based on certain conditions (number of players, etc.)

Other classical Game Theory examples include the Prisoner’s Dilemma & Battle of the SexesSlide8

Game Theory Brief Overview [3]

A “game” (or network) consists of:

Players (or nodes)

Possible actions of the players (or nodes)

Consequences of the actions

Rational players are assumed to maximize their payoff – justified by von Neumann

But humans don’t always act rationally…Slide9

Game Theory Brief Overview [4]

Maximizing one’s payoff = selfishness

All players try to gain the highest utility

Model behavior with suitable utility function

Keep track of benefit of the player as well as benefit relative to the other players

By modeling these trends, one can come up with a solution to a gameSlide10

Game Theory Brief Overview [5]

Definition: A solution to a game is a set of the possible outcomes

Pure strategies vs. mixed strategies

What is one solution to our in-class example?Slide11

Applications in Ad Hoc Networks [1]

Game theoretic protocols assume all nodes are selfish (worst case scenario)

What is the ideal goal with this approach?

Design distributed protocols that guarantee for each node, the existence of an equilibrium solution with an acceptable throughputSlide12

Applications in Ad Hoc Networks [2]

Game with an honest node

The

network offers to forward the traffic of the node in exchange for

forwarding effort c

The

node either accepts or rejects the

offer

Direct transmission or routed transmission?

If the node uses network resources, it should contribute to the

routing - participation

requires contribution

cSlide13

Applications in Ad Hoc Networks [3]

If the node connects directly to the receiver, the transmission power is

p

d

If the

node uses the network’s resources, i.e. forwards the traffic through other

nodes, the

power is

p

r

If

c

<=

c

0

=

p

d

-

p

r

, the node

transmits through

the network, and otherwise it transmits

directly

The

solution of

the game is that the network requires contribution c

0

and the node participates

in the

networkSlide14

Applications in Ad Hoc Networks [4]

Game with a cheating node

N

etwork

offers to

forward

traffic of the node in exchange for

forwarding contribution c

The

node either cooperates or

free-ridesSlide15

Applications in Ad Hoc Networks [5]

Game with a cheating node

If the required

contribution is

more than c

0

the node

cheats

In a network with an opportunity to cheat, a too

high request

for contribution is more

counter-productive

A

cheating node

consumes the

resources of the network while it contributes

nothingSlide16

Applications in Ad Hoc Networks [6]

The Nash Equilibrium

Each

player is assumed to know the equilibrium strategies of the other

players

No

player has anything to gain by changing only his or her own

strategy to just one side

The

current set of strategy choices and the corresponding payoffs constitute a Nash

equilibrium

Does

not necessarily mean the best cumulative payoff for all the players involvedSlide17

Applications in Ad Hoc Networks [7]

Note: “x”

is the number of

“cars”

travelling via that edge.Slide18

Applications in Ad Hoc Networks [8]

Game Analysis through simulation

Study traffic

of

the

network

and determine

whether a node benefits from joining the AHN using a

game with

an honest

node as a

basis

The

node makes the decision based on the expected

energy savings

and the expected forwarding effort requiredSlide19

Applications in Ad Hoc Networks [9]

Determining the “loser”:

Determine

the energy consumptions using direct connections

Determine

the energy consumptions using the given routing method

Identify

the losers by comparing the energy consumptions of the alternativesSlide20

Applications in Ad Hoc Networks [10] Slide21

Other Potential Approaches

In order for an AHN to work, the nodes need to share their resources

with others

Mechanisms need to be in place to enforce cooperation

in Ad Hoc

Networks

Game Theory is a preventative approach to handling misbehavior

Current

efforts against

node misbehavior using detective & reactive approaches include…Slide22

Potential Approach - Watchdog

Source: “Mitigating Routing Misbehavior in Mobile Ad Hoc Networks” [7]

Watchdog identifies misbehaving nodes and a path-rater helps routing protocols avoid these nodes

Approach increases network throughput, nodes dropping packets can be avoidedSlide23

Potential Issues – Watchdog

Approach does not prevent malicious or selfish nodes from operating – there are no sanctions for the misbehaving nodesSlide24

Potential Approach - Terminodes

Source:

NCCR MICS, http

://www.terminodes.org/

[8]

Terminodes Project – encourage cooperation in AHNs based on virtual currency called nuglets

Each node contains a tamper-proof hardware module to handle the nuglets

When a node forwards a packet, it gains a nuglet

The sender has to pay nuglets needed to forward the packet through the networkSlide25

Potential Issues – Terminodes

A node in the center of the network may gain more nuglets than it needs

Incentive to drop part of the packets

Nodes on the edges of the network may not gain enough nuglets to pay for their own traffic

Situation balances if long time frames are studied and the nodes are mobileSlide26

Potential Approach – Traffic Pricing

Source: “Modeling Incentives for Collaboration

in

Mobile Ad Hoc Networks” [10]

Compensation of traffic forwarding depends on energy consumption of transmission and congestion level of relaying node

Using same mechanism to enforce cooperation and balance traffic loads to avoid congestionSlide27

Potential Issues – Traffic Pricing

Implementing such a mechanism may prove to be challenging

Considerations

The need for updating the link’s cost based

on their bandwidth and power

usage

Investigate

re-routing protocols that

minimize

the routing

information that

needs to be distributed in the networkSlide28

Potential Approach – CONFIDANT

Source:

“Optimized Link State Routing Protocol

” [11]

Detects misbehavior and routes traffic around the misbehaving nodes, isolating them from the network

Each node observes its neighborhood and reports misbehavior to the other nodes

Reputation manager – maintains reputation information based on node’s observations

Path manager – rejects network functions requested by misbehaving nodes

Simulations demonstrate that the protocol performs well even if the fraction of selfish nodes is > 60%Slide29

Potential Approach – CORE

Source: “Core: A Collaborative Reputation Mechanism

to Enforce

Node

Cooperation…”

[

12]

Each node maintains a reputation table profiling other nodes

Reputation value is updated based on the node’s own observations and information provided by other nodes

If the reputation value drops below a threshold, the node does not provide the services requested by the misbehaving node – leads to isolationSlide30

Additional Considerations & Critiques

All of the schemes presented above require the proper use of MAC layer authentication protocols – in order to prevent impersonation

Reputation management system – layered security mechanism in order to provide an educated decision on how to react

The

user probably communicates with several nodes

during the

connection

timeSlide31

Presentation Conclusions [1]

The use of Game Theory can be a very valuable tool when diagnosing a network

Game

theory has been used to analyze the cooperation of

the nodes

There exist various mechanisms designed to prevent selfishness and to

enforce cooperation

Game

theoretic approaches try to analyze the problem

using a

more analytical

viewpointSlide32

Presentation Conclusions [2]

A specific

situation can

be studied

at different

levels through theory and simulations

How the mechanisms effect overall functionality

The faster a cheating

node is

detected and isolated from the network, the more effort can be demanded

from itSlide33

Questions & Comments

Any final questions or comments?Slide34

Resources Utilized [1]

[1] Juha Leino, “Applications of Game Theory in Ad Hoc Networks”

[2] Pietro Michiardi, Refik Molva, “Game Theoretic Analysis of Security in Mobile Ad Hoc Networks”

[3] Allen B. MacKenzie, Stephen B. Wicker, “Selfish Users in Aloha: A Game-Theoretic Approach”

[4] Alvaro A. Cardenas, Svetlana Radosavac, John S. Baras, “Detection and Prevention of MAC layer Misbehavior in Ad Hoc Networks”

[5] Allen B. MacKenzie, Stephen B. Wicker, “Game Theory and Design of Self-Configuring, Adaptive Wireless Networks”

[6] Jin, Tao, “Selfish MAC Misbehaviors in Wireless Networks”

[7] S. Marti, T. J. Giuli, K. Lai, M. Baker, “Mitigating Routing Misbehavior in Mobile Ad Hoc Networks”

[8] National Center of Competence in Research, Mobile Information & Communication Systems, http://www.terminodes.orgSlide35

Resources Utilized [2]

[9] L. Blazevic, L. Buttyan, S. Capkun, S. Giordiano, J.-P. Hubaux, and J.-Y. Le Boudec. “Self-organization in Mobile Ad-Hoc Networks: The Approach of Terminodes”

[10] J. Crowcroft, R. Gibbens, F. Kelly, and S. Östring. “Modeling Incentives for Collaboration in Mobile Ad Hoc Networks”

[11

] T. Clausen and P.

Jacquet, “Optimized Link State Routing Protocol”

[12] P. Michiardi and R.

Molva, “Core

: A

Collaborative Reputation Mechanism

to

Enforce Node Cooperation

in

Mobile Ad Hoc Networks”