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Using Game Theoretic Approach to Analyze Security Issues In Ad Hoc Networks. Term Presentation Name: Li Xiaoqi, Gigi Supervisor: Michael R. Lyu Department: CSE, CUHK Date: 02/05/2006 Time: 2:00-2:45pm Location: HSB 121. Outline.

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using game theoretic approach to analyze security issues in ad hoc networks

Using Game Theoretic Approach to Analyze Security Issues In Ad Hoc Networks

Term Presentation

Name: Li Xiaoqi, Gigi

Supervisor: Michael R. Lyu

Department: CSE, CUHK

Date: 02/05/2006

Time: 2:00-2:45pm

Location: HSB 121

outline
Outline
  • Overview and relevant work
  • Motivation
  • Game theory
  • Our Game and solution
  • Conclusion and future work
attacks on wireless networks
Attacks On Wireless Networks
  • Passive:
    • Not disturb the routing protocol
    • Hard to detect
    • E.g.:
      • Eavesdropping
      • Selfish behavior
        • Refuse to forward packets of other nodes in order to
          • Save own energy
          • Economize own bandwidth
          • ……
attacks on wireless networks1
Attacks On Wireless Networks
  • Active:
    • Disrupt the routing protocol
    • Modification, e.g.:
      • Black hole
      • Grey hole
      • Wormhole
    • Fabrication
      • E.g.: rushing attack
    • Impersonation
      • E.g.: alter MAC/IP address
relevant work
Relevant Work
  • On selfish behaviors
    • Currency-based mechanism
      • Forwarding packets is paid
    • Reputation-based mechanism
      • Use reputation to incent nodes
    • Game theoretic based mechanism
      • Model forwarding as a strategic game
      • Result in a Nash equilibrium with a metric, e.g. best forwarding rate
      • Utility function includes bandwidth, energy, etc.
relevant work1
Relevant Work
  • On malicious attacks
    • For intrusion detection system (IDS) of MANET: use game theory to attempt to decrease false alarm rate
    • Less work on this issue
    • Almost none of them can effectively solve malicious node collusion
motivation
Motivation
  • Game theory is mostly employed as a tool to analyze, formulate or solve selfishness issue.
  • It seldom applied to detect/prevent/deter malicious behavior.
game theory
Game Theory
  • It is a branch of economics that deals with strategic and rational behavior.
  • It has applications in economics, international relations, evolutionary biology, political science, military strategy, and so on.
  • It provides us with tools to study situations of conflict and cooperation.
game theory1
Game Theory
  • Game theory can be divided from three dimensions
    • Noncooperative and Cooperative Games
      • A player may be an individual (noncooperative) or a group of individuals (cooperative)
    • Strategic and Extensive Games
      • also called static and dynamic games
    • Games with Complete and Incomplete Information
      • Players’ moves or types are fully informed or imperfectly informed
game theory2
Game theory
  • Our idea:
    • Security issues in ad hoc network also involve interactions among nodes.
    • So it is possible to use game theory for designing, formulating, and analyzing those interactions.
    • Then we may find some solutions to help detecting, preventing or detering malicious behaviors.
possible formulations
Possible Formulations
  • Basic signaling game:
    • Multi-stage, dynamic, and non-cooperative game with incomplete information
    • It has perfect Bayesian equilibrium (PBE)
  • Cooperative game:
    • Analyze payoffs from individual point of view and social point of view respectively
  • Repeated game:
    • Capture the idea of a player’s current behavior and the other players’ future behavior.
basic signaling game
Basic Signaling Game
  • Two players:
    • Player 1, the sender
    • Player 2, the receiver
  • Player 1 has a type θ, and player 2 believes that the probability of 1 is θ is p(θ).
  • Player 1 observes information about his type θ, and chooses an action a1
  • Player 2 observes a1, chooses an action a2 from her action space.
basic signaling game1
Basic Signaling Game
  • Player i’s payoff is denoted by ui(a1, a2,θ).
  • Player 1’s strategy is a probability distribution σ1(·|θ) over actions a1 for each type θ
  • Player 2’s strategy is a probability distribution σ2(·| a1) over actions a2 for each action a1
basic signaling game2
Basic Signaling Game
  • Player 1’s payoff is:
  • Player 2’s payoff is
  • Player 2 updates her beliefs about θ, and bases her choice of action a2 on the posterior distribution μ(·|a1).
basic signaling game3
Basic Signaling Game
  • A perfect Bayesian equilibrium (PBE) of a signaling game is a strategy profile σ*and posterior beliefs μ(·|a1) such that
some considerations
Some Considerations
  • What are the possible types of nodes?
    • {Malicious, Normal}
    • {Armed, Unarmed}
    • {Sensitive, Regular}
  • What are the possible actions a node may take?
    • {Doubt, Trust}
    • {Defend, Miss}
    • {Cooperate, Not Cooperate}
our direction
Our Direction
  • Establish an expressive, realistic, non-trivial model of interactions between attacker(s) and target(s).
  • Try to solve the model and give a possible and reasonable Nash equilibrium.
  • Obtain some references about value choosing of a design factor.
  • Design a correspond application consistent with the strategies and beliefs in the above equilibrium.
our direction1
Our Direction
  • When establishing interaction model, possible players are:
    • One attacker and one target: 1 vs. 1  simple attack
    • Two attackers and one target: 2 vs. 1  collusion attack
    • One attacker and n targets: 1 vs. n  DIDS
    • N attackers and one target: n vs. 1  DoS
    • N attackers and n targets: n vs. n  DDoS
our direction2
Our Direction
  • When establishing interaction model, possible players are:
    • One attacker and one target: 1 vs. 1  normal attack
    • Two attackers and one target: 2 vs. 1  collusion attack
    • One attacker and n targets: 1 vs. n  DIDS
    • N attackers and one target: n vs. 1  DoS
    • N attackers and n targets: n vs. n  DDoS
our game
Our Game
  • Mixed strategies of the stranger:
    • The stranger may have two types: {Malicious, Regular}. The probability of a stranger is malicious is ε.
    • If the stranger is malicious, his action space is {Attack, Normal}. The probability of he performs attacks is s.
    • If the stranger is regular, he will always behave normally.
our game1
Our Game
  • Mixed strategies of the target:
    • For the target node, she may perform two actions to the stranger: {Doubt, Trust}. The probability of she doubts is t.
    • When she doubts, she may ask for her neighbors’ help to get the trustworthiness of the stranger, or request the stranger to identify himself, or else.
our game2
Our Game
  • Payoff formulation:
    • If the stranger is regular, and the target will get a amount of payoff if she trusts, where a>1.
    • If the stranger is malicious and he attacks successfully, he will cause a amount of harm to the target.
    • If the target doubts the stranger, she will cost 1.
    • If the doubt is deserved, the target will get b amount of feedback, where 0<b<1.
    • If the trust is not worthy, the target will lose b amount of payoff.
our game3
Our Game
  • Payoff formulation:
    • If the stranger is malicious but he pretends to be normal,
      • in the current round, the target will cost more to doubt him than to trust him, but the doubt will induce the stranger to get payoff of -1.
      • in the long run game, the target may threat the stranger by doubting more frequently.
  • We regard the stranger as Player 1, masculine and the target as Player 2, feminine.
our game4
Our Game
  • The stranger knows his type assigned by a virtual player “Nature”.
  • The target doesn’t know the stranger’s type, and is not sure what behavior the stranger has taken.
  • This is a two-player, extensive, non-cooperative game with incomplete information.
our solution
Our Solution
  • This model has no Nash equilibrium on pure strategy.
    • Consider strategy: (Attack, Doubt)
      • If player 1 is malicious and attacks, the best response of player 2 is to doubt.
      • But if player 2 doubts, the best response of player 1 is to behave normal
    • Consider strategy: (Normal, Trust)
      • If player 1 behaves normal, the best response of player 2 is to trust (doubt is costly).
      • But if player 2 trusts, the best response of player 1 is to attack.
    • Both of these two reasonable strategy are not Nash equilibrium strategy.
our solution1
Our Solution
  • The model has Sequential Nash Equilibrium on mixed strategy, that is the actions that the players take is a probability distribution on the action spaces.
  • The strategy profile is
  • When σ is given, Pσ(x) denotes the probability that node x is reached.
  • h is information set containing more than one node. E.g. h={x3, x4, x5}
  • Belief μ(x) specifies the probability the player assigns to x conditional on reaching h.
our solution2
Our Solution
  • The probability distribution on information set h is
  • The expected payoff of player 2 is:
our solution3
Our Solution
  • Differential coefficient on s is
  • So we have the following conclusion:
    • When , (1)>0. That is, if s is increased, the payoff of player 2 will increase.
    • When , (1)<0. That is, if s is decreased, the payoff of player 2 will increase.
our solution4
Our Solution
  • From the above solution, we get a threshold value that can be applied to the design of our corresponding secure routing protocol.
  • In our previous secure routing protocol, if node’s opinion about another node exceeds a threshold, it will exchange opinions with its neighbors to get a more object trustworthiness value.
conclusion and future work
Conclusion and Future Work
  • We give a game theoretic model of stranger-target interactions.
  • We find out a solution of the model and get a helpful threshold value which can be applied to the design of secure routing protocol.
  • We will extend our model from several aspects: long-run game, and 2 vs. 1 collusion attacks.
  • Try to find out other conclusions which will be helpful to secure protocol design.
slide32
Q & A

Thank You!