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Counter-measuring MAC Misbehaviors in Ad Hoc Networks using Game Theory March 25, 2010 EE5723 – Computer & Network Security.

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Presentation on theme: "Counter-measuring MAC Misbehaviors in Ad Hoc Networks using Game Theory March 25, 2010 EE5723 – Computer & Network Security."— Presentation transcript:

1 Counter-measuring MAC Misbehaviors in Ad Hoc Networks using Game Theory March 25, 2010 EE5723 – Computer & Network Security

2 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

3 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?

4 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 sources

5 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 punish

6 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.

7 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

8 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…

9 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

10 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?

11 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

12 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

13 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 network

14 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

15 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 nothing

16 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

17 Applications in Ad Hoc Networks [7] Note: “x” is the number of “cars” travelling via that edge.

18 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

19 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

20 Applications in Ad Hoc Networks [10]

21 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…

22 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

23 Potential Issues – Watchdog Approach does not prevent malicious or selfish nodes from operating – there are no sanctions for the misbehaving nodes

24 Potential Approach - Terminodes Source: NCCR MICS, [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

25 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

26 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

27 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

28 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%

29 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

30 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

31 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

32 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

33 Questions & Comments Any final questions or comments?

34 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,

35 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”

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