Download Counter-measuring MAC Misbehaviors in Ad Hoc Networks using

Counter-measuring MAC Misbehaviors
in Ad Hoc Networks using Game Theory
March 25, 2010
EE5723 – Computer & Network Security
Presentation Outline
I. Big Picture Topic Introduction
II. Game Theory Brief Overview
III. Applications in Ad Hoc Networks
IV. Other Potential Approaches
V. Additional Considerations & Critiques
VI. Presentation Conclusions
VII.Questions & Comments
Big Picture Topic Introduction
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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?
Big Picture Topic Introduction
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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
sources
Big Picture Topic Introduction
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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 punish
Game Theory Brief Overview [1]
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Branch of applied mathematics
Multi-person decision making situations
Used to analyze existing systems -orUsed 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.
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 Sexes
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…
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 game
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?
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 throughput
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 c
Applications in Ad Hoc Networks [3]
• If the node connects directly to the receiver, the
transmission power is pd
• If the node uses the network’s resources, i.e.
forwards the traffic through other nodes, the
power is pr
• If c <= c0 = pd - pr, the node transmits through the
network, and otherwise it transmits directly
• The solution of the game is that the network
requires contribution c0 and the node participates
in the network
Applications in Ad Hoc Networks [4]
• Game with a cheating node
• Network offers to forward traffic of the node
in exchange for forwarding contribution c
• The node either cooperates or free-rides
Applications in Ad Hoc Networks [5]
• Game with a cheating node
• If the required contribution is more than c0 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 nothing
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 involved
Applications in Ad Hoc Networks [7]
Note: “x” is the number of “cars” travelling via that edge.
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 required
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 alternatives
Applications in Ad Hoc Networks [10]
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…
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 avoided
Potential Issues – Watchdog
• Approach does not prevent malicious or selfish
nodes from operating – there are no sanctions
for the misbehaving nodes
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 network
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 mobile
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 congestion
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 network
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%
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 isolation
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 time
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 viewpoint
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 it
Questions & Comments
Any final questions or comments?
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 GameTheoretic 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 SelfConfiguring, 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.org
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”