A Presentation on: Resilient MAC Protocol RESI-MAC. A Game Theoretic Solution. By: Jacob Abegunde Supervisors : 1 Dr. Hannan & Dr.

Transcription

1 A Presentation on: Resilient MAC Protocol RESI-MAC A Game Theoretic Solution By: Jacob Abegunde Supervisors : 1 Dr. Hannan & Dr. Joseph

2 Agenda 2 1. Introduction & Background 2. The Problem: MAC Vulnerabilities 3. Solutions Categories 4. Game Theoretic Solution 5. Our Game Model & Strategy 6. Simulation Result 7. Question Time

3 INTRODUCTION: Wireless Networks Ad hoc Mode Infrastructure Mode 3

4 BACKGROUND: Wireless MAC Protocol 4 IEEE MAC Handshake RTS from Sender to Receiver CTS from Receiver to Sender DATA from Sender to Receiver ACK from Receiver to Sender

5 MAC Protocol Vulnerabilities 5 The Problem MAC Protocol is vulnerable to various misbehaviour techniques such back-off cheating, CW cheating, NAV cheating, de-authentication, de-association etc. The vulnerabilities can be classified into: Share medium vulnerabilities, Identity vulnerabilities, protocol vulnerabilities and Human or Vendor factor The exploitation of these vulnerabilities has led to various version of denial of service (DoS) attack. There is NO single fix solution yet, Unfortunately.

6 Solutions Categories Prevention, Detection & Reaction Some are based on developing a long-term profile of normal activities (Anomaly Detection) They identify misbehaviour by observing deviations from such profile 6 Some are base on systematic evaluation of communication parameters (Heuristic Method) They identify misbehaviour using deviation from known rules or probability method

7 Solutions Categories CONT D 7 Prevention, Detection & Reaction Some are based on principles adapted from the Human immune system (Artificial Intelligent) Low computation overhead but high profiling time OUR APPROACH Our approach is based on the mathematical model of conflicts and resolution (Game Theory) A Game G consist of : {Players, Strategies, Utilities} Assumptions of rationality (which is difficult to avoid and difficult to justify a bit of dilemma)

8 Advantages Of Game Theoretic Solutions 8 It can be use for Analysis of distributed systems The existence, uniqueness and convergence to a steady state operating point (possibility) Design of incentive and disincentives schemes Different strategies of played can be modelled Cooperation between strategic players can be stimulated Complex Interaction (between players) can be modelled

9 The Challenges Of Game Theoretic Solutions Assumption of Rationality (Highly Controversial) Realistic scenarios require complex models Complexity in Choice of Utility functions 9

10 Previous Game Solutions M.Cagalj et al. (2005), showed how a small proportion of selfish node can degrade network performance. 10 Konorski et al (2006), presented a retaliatory game in which nodes probabilistically select a response between cooperation and selfishness whenever selfish behaviour is observed.

11 Previous Game Solutions CONT D Boyer et al. (2012), proposed a game to share available medium among cooperative nodes and retaliate against selfishness nodes They also proposed a reputation-based Tit-for- Tat (TFT) mechanism 11 Shivshankar et al. (2012), applies conditional TFT and unconditional altruism as a solution concept in Vehicular Networks.

12 Previous Game Solutions CONT D Lin Chen and Leneutre, J. (2007) examined the question of how bad IEEE DCF will performs if all nodes are selfish? They demonstrated that: Selfishness is Not Always A Nightmare as long as nodes plays TFT 12

13 Previous Game Solutions CONT D Seredynski et al. (2012), demonstrated that, as long as at least 70 per cent of nodes follow the TFT principle in packet transmission, the benefits of the use of TFT strategy exceed the costs. 13 They suggested that strong retaliators (i.e noncooperative nodes) could be used to enforce the desire cooperation through TFT strategy

14 The Research Gap 14 Unfortunately, the problem remains unsolved according to M. Eian and S.F. Mjolsnes (2012) and Ahmad et al (2011) They demonstrated a deadlock vulnerability against IEEE w (the secured version of MAC protocol) The TFT strategy being employed as a solution has its own deadlock vulnerability and hence the need for a research work of this nature.

15 Our Motivation: Why the choice of Game? 15 EACH SOLUTION METHOD HAS ITS OWN CHALLENGE Prevention methods have their limitations Detection methods are somehow awkward, error prone and subject to evasion Punishment schemes are like aiming to shoot moving targets: It s easier said than done! Reputation is based on past track record, but WLAN environment is dynamic and volatile So, we think Game has good prospect here

16 Our Game Solution 16 Is it a Misbehaviour Prevention method? ==> NO, not at all, that is NOT our goal (Realistic?) Is it a Misbehaviour Detection method? ==> NO, but we are interested in transmission failure Is it a Punishment Scheme? ==> NO, not really, we are not after cheating nodes What is it then? Just as its named : IT S A GAME IT IS A RETALIATORY GAME A Strategic Interaction between players (Nodes)

17 The Game Model RESI-MAC = G { N, S i, {U i }} Where N denote a set of nodes, N = {1,2, n}, S i = set of strategies or actions for player i S = S 1 S 2 S 3 S n is the Cartesian product of the sets of strategies available to each player {U i } = {U 1, U 2,,U n } is the set of utility functions that each player i wishes to maximize, where U i : S R. 17

18 The Game Model CONT D For every player i, the utility function is a function of the strategy chosen by player i, i.e s i, and those chosen by other players in the game, denoted as s i Together, s i and s i make up the action or strategy tuple S(s i, s -i ). A strategy tuple is a unique choice of actions by each player 18

19 The Game Model CONT D Best Response Strategy (BSR) of a player is a strategy that maximizes her utility function, given the strategies being played by others. Symbolically, S is the best response by player i to S i if S { arg max U i (s i, s -i ) } 19

20 The Game Model CONT D 20 A Nash equilibrium (NE) is an action tuple that corresponds to the mutual best response, i.e for each player i, the action selected is a best response to the actions of all others. In other words, a NE is an strategy tuple where no individual player can benefit from unilateral deviation S* = (s 1 *, s 2 *,..., s* n ) is a NE if U i (s* i, s* -i ) >= U i (s i, s* -i ) s i S i and i N

21 The Game Model CONT D An outcome is Pareto optimal if there is no other outcome that makes every player at least as well off while making at least a player better off. Symbolically, an action tuple S = (s 1, s 2, s 3,...,s n ) is Pareto optimal if and only if there exists no other action tuple A = (a 1, a 2, a 3,...,a n ) such that u i (A) u i (S) for i N. 21

22 The Strategy Model 22 A Model Not all nodes are bad, neither are all good. Large proportion are good while a few are bad. Our Aim: Using a good strategy of play, we want to neutralize the effect of misbehaving node(s) Typical Scenario

23 The Strategy Model CONT D Multi-Strategies Formulation Our game mechanism depends on formulation of strategy list: {S 0,S 1,S 2... S n } And their corresponding dynamic equilibria points: {E 0, E 1,E 2... E n } (we suppose the exist) If we denotes the order pair (S 0, E 0 ) as the default strategy (the normal form of DCF) 23

24 The Strategy Model CONT D 24 Then Alternative strategies and their equilibria are denoted as ordered pairs (S 1, E 1 ), (S 2, E 2 ),..., (S n, E n ) which represent some variants (i.e deviations from) of the standard strategy and their corresponding alternative equilibria points. Then, in the absence of a misbehaving node, all nodes will be playing (S 0,E 0 ), the normal DCF which is Pareto efficient

25 The Strategy Model CONT D 25 However, If a number of nodes behaves selfishly, the effect will be felt by other nodes in the sense that it will lead to transmission failure and hence lowers their utility. The affected nodes will react to this situation by incrementing k, an integer representing the number of failed attempt, initially k=0, k=k+1 and CW=CW 0 / k. This give rise to CW 1 = CW 0 / 1, CW 2 = CW 0 / 2... CW n = CW 0 / n.

26 The Strategy Model CONT D When a threshold n is reached, the variable k is decremented progressively back to 1 and the CW is calculated for each value of k and then used instead of CW 0 for the medium access CW = CW 0 / k, (k= 1 n, k++), (k= n 1, k--) => CW = CW 0 / 1, CW 0 / 2, CW 0 / 3... CW 0 / n, CW 0 / n, CW 0 / (n-1), CW 0 / (n-2), CW 0 / (n-3) CW 0 / n-(n-1) 26

27 The Default Tit for Tat Strategy 27 Be Nice cooperate, do not be the first to defect Do, what the others did last, cooperate if they cooperate and defect if they defect. Analysis of the TFT shows that it is vulnerable to deadlock scenario Previous Improvement on TFT includes: TF2T, GTFT, TFTWPr, TFTFWB We are proposing (PTFT) Progressive TFT with Exit, Strategy

28 28 Our Proposal: Progressive Tit For Tat (PTFT)

29 Initial Simulation Result CONT D Comparison of the cheater s strategy with our PTFT 29

30 Initial Simulation Result CONT D Comparison of the cheater s strategy with our PTFT 30

31 Initial Simulation Result CONT D Comparison of the 3 common strategies with our PTFT 31

32 Initial Simulation Result CONT D Comparison of the 3 common strategies with our PTFT 32

33 REFERENCES 33 [1] M. Cagalj, S. et al, On selfish behaviour in csma /ca networks," in INFOCOM th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE, vol. 4. IEEE, 2005, pp. 2513{2524}. [2] S. Boyer et al, A novel reputation-based tit-for-tat strategy for IEEE csma/ca protocol," in Consumer Communications and Networking Conference (CCNC), 2012 IEEE. IEEE, 2012, pp. 143{148}. [3] L. Chen et al, Selfishness Not always a nightmare: Modelling selfish mac behaviours in wireless mobile ad hoc networks," in Distributed Computing Systems, ICDCS '07. 27th International Conference on, June 2007, pp. 16{16}. [4] S. Shivshankar et al, Effect of altruism and punishment on selfish behaviour for cooperation in vehicular networks," in Communications in China (ICCC), st IEEE International Conference on. IEEE, 2012, pp. 653{658}. [5] S.Boyer et al, An adaptive tit{for{tat strategy for ieee csma/ca protocol," International Journal of Security and Networks, vol. 7, no. 2, pp. 95{106, [6] M. Seredynski et al, The necessity for strong reciprocators in mobile ad hoc networks," in Parallel and Distributed Processing Symposium Workshops PhD Forum (IPDPSW), 2012 IEEE 26th International, May 2012, pp. 609{616}.

34 QUESTION TIME Questions? Yes, Please. Anwers? Not Guarranted 34 Game Over!!! Thank you.