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Adversarial Models for Wireless Communication

Adversarial Models for Wireless Communication

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Adversarial Models for Wireless Communication

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  1. Adversarial Models for Wireless Communication Andrea W. RichaArizona State University SIROCCO'13, Andrea Richa

  2. Motivation Channel availability hard to model: • Mobility • Packet injection • Temporary Obstacles • Background noise • Physical Interference • Co-existing networks • Jammer SIROCCO'13, Andrea Richa

  3. Physical layer jamming • A physical jammerlistens to the open medium and broadcasts in the same frequency band as network • can lead to significant disruption of communication at low cost for the jammer jammer honest nodes SIROCCO'13, Andrea Richa

  4. Motivation Channel availability hard to model: • Mobility • Packet injection • Temporary Obstacles • Background noise • Physical Interference • Co-existing networks • Jammer all contribute to some form of “background noise” SIROCCO'13, Andrea Richa

  5. Motivation Ideal world: Usual approach adopted in theory. : noise level Background noise 0 time SIROCCO'13, Andrea Richa

  6. Motivation Ideal world: OR Usual approach adopted in theory. : noise level Background noise 0 time SIROCCO'13, Andrea Richa

  7. Motivation Ideal world: OR (bounded, predictable) Usual approach adopted in theory. : noise level Background noise 0 time SIROCCO'13, Andrea Richa

  8. Motivation : noise level Real world: How to model this??? background noise 0 time SIROCCO'13, Andrea Richa

  9. Our Approach: Adversarial Jamming X X X Background noise (microwave, radio signal, etc.) Intentional jammer Temporary Obstacles (cars etc.) Co-existing networks … X X SIROCCO'13, Andrea Richa

  10. Our Approach: Adversarial Jamming • Idea: model unpredictable behaviors via adversary (a.k.a. adversarial jammer)! X X X X X SIROCCO'13, Andrea Richa

  11. Overview • Adaptive adversary • Single-hop scenario • Simple (yet powerful) idea • MAC protocol • Reactive adversary • Fairness • Adaptive adversary in multi-hop networks • Application: Leader Election • Other adversarial models • Future work SIROCCO'13, Andrea Richa

  12. Wireless communication model • single frequency: e.g., sensor nodes • at each time step, a node may decide to transmit a packet (nodes continuously contend to send packets) • a node may transmit or sense the channel at any time step (half-duplex) • when sensing the channel a node v may • sense an idle channel • receive a packet • sense a busy channel (due to interference or adversarial jamming) v SIROCCO'13, Andrea Richa

  13. Single-hop wireless network • [Awerbuch, R., Scheideler, PODC’08] • nreliable honest nodes and ajammer (adversary); all nodes within transmission range of each other and of the jammer jammer SIROCCO'13, Andrea Richa

  14. Adaptive adversary • knows protocol and entire history • (T,λ)-bounded adversary, 0 < λ < 1: in any time window of size w ≥ T, the adversary can jam ≤ λw time steps steps jammed by adversary other steps w 0 1 … SIROCCO'13, Andrea Richa

  15. Constant-competitive protocol • a protocol is called constant-competitive against a(T,λ)-bounded adversary if the nodes manage to perform successful transmissions in at least a constant fraction of the steps (w.h.p. or on expectation), for any sufficiently large number of steps successful transmissions steps jammed by adversary other steps (idle channel, message collisions) w 0 1 … SIROCCO'13, Andrea Richa

  16. Our main contribution • symmetric local-control MAC protocol that is constant-competitive against any (T,1-ε)-bounded adaptive adversary after Ω (T / ε) steps w.h.p., for any constant 0<ε<1and anyT. • energy efficient: • converges to bounded amount of energy consumption due to message transmissions by nodes under continuous adversarial jamming (ε=0) • fast recovery from any state ~ SIROCCO'13, Andrea Richa

  17. Pros and Cons Pros: • no prior knowledge of global parameters • nodes do not know ε • no IDs needed Cons: • nodes know rough estimate γ=O(1/(log T + loglog n)) • allow for superpolynomial change in n and polynomial change in T over time • fair channel use is not guaranteed • we will see how to fix that later SIROCCO'13, Andrea Richa

  18. Physical Layer Traditional Jamming Defenses • spread spectrum & frequency hopping: • Many references in the literature (specially more applied work)… • rely on broad spectrum (large number of available frequencies). However, sensor nodes or common wireless devices based on 802.11 have narrow spreading factors • Our approach is orthogonal to broad spectrum techniques, and can be used in conjunction with those. SIROCCO'13, Andrea Richa

  19. MAC Layer Jamming Defenses • random backoff: • adaptive adversary too powerful for MAC protocols based on random backoff\tournaments (including the 802.11 standard [Bayrataroglu, King, Liu, Noubir, Rajaraman, Thapa, INFOCOM’08]) • [Gilbert, Guerraoui, Newport, OPODIS’06]: cannot handle adaptive adversaries with high jamming rate • more general scenario (adversary can also introduce malicious messages) • nodes know n • not energy efficient • Others (channel surfing, coding strategy,etc.): also cannot handle adaptive adversary SIROCCO'13, Andrea Richa

  20. Overview • Adaptive adversary • Single-hop scenario • Simple (yet powerful) idea • MAC protocol • Reactive adversary • Fairness • Adaptive adversary in multi-hop networks • Application: Leader Election • Other adversarial models • Future work SIROCCO'13, Andrea Richa

  21. Simple (yet powerful) idea • each node v sends a message at current time step with probability pv≤pmax, for constant 0 < pmax<< 1.p = ∑ pv(aggregateprobability) qidle=probability the channel is idleqsucc=probability that only one node is transmitting (successful transmission) • Claim.qidle. p ≤qsucc≤(qidle. p)/ (1-pmax) if (number of times the channel is idle)=(number of successful transmissions)p = θ(1)qsucc= θ(1) ! (what we want!) ~ SIROCCO'13, Andrea Richa

  22. Basic approach • a node v adapts pv based only on steps when an idle channel or a successful message transmission are observed, ignoring all other steps (including all the blocked steps when the adversary transmits!) time idle steps successful transmissions steps jammed by adversary steps where collision occurred but no jamming SIROCCO'13, Andrea Richa

  23. Basic approach • a node v adapts pv based only on steps when an idle channel or a successful message transmission are observed, ignoring all other steps (including all the blocked steps when the adversary transmits!)! time idle steps successful transmissions steps jammed by adversary steps where collision occurred but no jamming SIROCCO'13, Andrea Richa

  24. Naïve protocol Each time step: • Node vsends a message with probability pv . If v does not send a message then • if the wireless channel is idle then pv = (1+ γ) pv • if vreceived a message thenpv = pv /(1+ γ) (Recall thatγ = O(1/(log T + loglog n)).) SIROCCO'13, Andrea Richa

  25. Problems • Basic problem:Aggregate probability p could be too large. • all time steps blocked due to message collisions w.h.p. time idle steps successful transmissions steps jammed by adversary steps where collision occurred but no jamming SIROCCO'13, Andrea Richa

  26. Problems • Basic problem:Cumulative probability pcould be too large. • all time steps blocked due to message collisions w.h.p. time idle steps successful transmissions steps jammed by adversary steps where collision occurred but no jamming SIROCCO'13, Andrea Richa

  27. Problems • Basic problem:Cumulative probability p could be too large. • all time steps blocked due to message collisions w.h.p. • Idea: If more than T consecutive time stepswithout successful transmissions (or idle time steps), then reduce probabilities, which results in fast recovery of p. • Problem: Nodes do not know T. How to learn a good time window threshold? • It turns out that additive-increase additive-decrease is the right strategy! SIROCCO'13, Andrea Richa

  28. MAC protocol • each node vmaintains • probability valuepv, • time window thresholdTv, and • countercv • Initially, Tv = cv = 1andpv = pmax (< 1/24). • synchronized time steps (for ease of explanation) • Intuition:wait for an entire time window (according to current estimate Tv) until you can increase Tv SIROCCO'13, Andrea Richa

  29. MAC protocol In each step: • node v sends a message with probability pv . If v decides not to send a message then • if v senses an idle channel, then pv= min{(1+γ)pv , pmax} • if vsuccessfully receives a message, then pv= pv /(1+ γ)and Tv= max{Tv - 1, 1} • cv= cv + 1. If cv> Tvthen • cv= 1 • ifvdidnot receive a messagesuccessfullyin thelast Tv steps thenpv= pv /(1+ γ)andTv= Tv +1 SIROCCO'13, Andrea Richa

  30. Our results • Let N = max {T,n} • Theorem. Our MAC protocol is constant-competitive under any (T,1-ε)-bounded adversary if the protocol is executed for Ω(log N . max{T,log3N/(εγ2)}/ε) steps w.h.p., for any constant 0<ε<1and anyT. SIROCCO'13, Andrea Richa

  31. Proof sketch • Show competitiveness for time frames of F = θ((log N . max{T,log3N/(εγ2)}/ε) many steps If we can show constant competitiveness for any such time frame of size F, the theorem follows • Use induction over the number of sufficiently large time frames seen so far. We subdivide each frame: I’ I f =θ(max{T,log3N/(εγ2)}) F = (log N / ε) . f SIROCCO'13, Andrea Richa

  32. Proof sketch • p > 1/(f2(1+γ)2√f) and Tv< √F, in each subframe I’ w.h.p. • p<12 and p>1/12 within subframe I’ with moderate probability (so that adaptive adversarial jamming not successful) • Constant throughput in I’ with moderate probability • Over a logarithmic number of subframes, constant throughput in frame I of size F w.h.p. SIROCCO'13, Andrea Richa

  33. Continuous jamming Moreover, under a more powerful adversary that can perform continuous jamming (afterΩ(T)steps): Lemma. The total energy consumption (sending out messages) during an entire continuous jamming attack is O(√T), independent of the length of the attack. Exhaust adversary’s energy resources ~ ~ SIROCCO'13, Andrea Richa 33

  34. Overview • Adaptive adversary • Single-hop scenario • Simple (yet powerful) idea • MAC protocol • Reactive adversary • Fairness • Adaptive adversary in multi-hop networks • Application: Leader Election • Other adversarial models • Future work SIROCCO'13, Andrea Richa

  35. Reactive adversary • [R.,Scheideler, Schmid, Zhang, ICDCS’11] • Fully adaptive adversary that in addition can quickly observe the channel at the current time step, before deciding to jam • i.e., the adversary has some knowledge about the random choices at current time step • Distinguishes between idle and non-idle time steps, but cannot distinguish between successful transmissions and collisions (e.g., if packets are encrypted) SIROCCO'13, Andrea Richa

  36. AntiJam: Reactive jamming-resistant MAC protocol • Need to “synchronize” transmission probabilities pv, as well as counters cv and Tv • Piggyback pv, cv, Tvto a message sent by v • Better understanding on how aggregate probability changes every time step • Achieve fairness for free (basically all nodes have the same transmission probability)! • Once nodes are synchronized (plus some other smaller changes), one can show that the basic protocol is also robust against reactive adversaries SIROCCO'13, Andrea Richa

  37. Our results Theorem.The AntiJam protocol achieves: • fairness: the channel access probabilities among nodes do not differ by more than a factor of (1+γ) after the first message was sent successfully. • eθ(1/ ε )-competitiveness w.h.p., under any (T,1-ε)-bounded reactive adversary if the protocol is executed forΩ(T/ε) steps w.h.p., for any constant 0<ε<1and anyT(constant in throughput now depends on ε) 2 ~ SIROCCO'13, Andrea Richa

  38. Proof sketch: Fairness • Fact: • Right after u sends a message successfully along with the tuple (pu ,cu ,Tu), (pv, cv, Tv)= (pu / (1+ γ), cu,Tu) for all receiving nodes v, while the sending node values stay the same. In particular, for any time step tafter a successful transmission by node u, (cv, Tv) = (cw, Tw) for all nodes v and w V • This implies that after a successful transmission, the access probabilities of any two nodes in the network will never differ by more than a factor in the future. SIROCCO'13, Andrea Richa

  39. AntiJam: Throughput for ε = 0.5 ε = 0.5 SIROCCO'13, Andrea Richa

  40. AntiJam: Convergence Time SIROCCO'13, Andrea Richa

  41. AntiJam: Fairness SIROCCO'13, Andrea Richa

  42. AntiJam vs. Non-reactive protocol: Fairness SIROCCO'13, Andrea Richa

  43. AntiJam vs 802.11 SIROCCO'13, Andrea Richa

  44. Overview • Adaptive adversary • Single-hop scenario • Simple (yet powerful) idea • MAC protocol • Reactive adversary • Fairness • Adaptive adversary in multi-hop networks • Application: Leader Election • Other adversarial models • Future work SIROCCO'13, Andrea Richa

  45. Multihop wireless networks Physical interference Jammed SIROCCO'13, Andrea Richa Powerful jammer

  46. Signal-to-Interference-Noise-Ratio (SINR) • A message sent by nodeu is received at node v iff- N: Gaussian variable for background noise- S: set of transmitting nodes- : constant that depends on transmission scheme- d(x,y):Euclidean distance between x and y • Well-accepted model (in theory, at least  ) for physical interference P/d(u,v) >  N + w inSP/d(w,v) SIROCCO'13, Andrea Richa

  47. Multihop Adversarial Model • (B,T)-bounded adaptive adversary: has an overall noise budget of BT that it can use to increase the noise level at node v and that it can distribute among the time steps as it likes. • At any point in time, the adversary makes independent decisions for each node on whether to jam it (provided it does not exceed its noise budget over a window of size T). • many noise phenomena can be covered under this model SIROCCO'13, Andrea Richa

  48. Throughput in multihop networks • [Ogierman, R., Scheideler, Schmid, Zhang, 2013]: SINR model • Also achieve constant throughput : Throughput = (Earlier, [R., Scheideler, Schmid, Zhang, DISC’10]: unit-disk graph model.) SIROCCO'13, Andrea Richa

  49. Overview • Adaptive adversary • Single-hop scenario • Simple (yet powerful) idea • MAC protocol • Reactive adversary • Fairness • Adaptive adversary in multi-hop networks • Application: Leader Election • Other adversarial models • Future work SIROCCO'13, Andrea Richa

  50. Leader Election in Adversarial (Single-hop) Networks • [R., Scheideler, Schmid, Zhang, MobiHoc’11] • Our goal: select a leader among the nodes • Challenges: we may start in any state; there is adaptive adversary leader SIROCCO'13, Andrea Richa