Adversarial models for wireless communication
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Adversarial Models for Wireless Communication. Andrea W. Richa Arizona State University. Motivation. Channel availability hard to model: Mobility Packet injection Temporary Obstacles Background noise Physical Interference Co-existing networks Jammer. Physical layer jamming.

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Adversarial models for wireless communication

Adversarial Models for Wireless Communication

Andrea W. RichaArizona State University

SIROCCO'13, Andrea Richa


Motivation

Motivation

Channel availability hard to model:

  • Mobility

  • Packet injection

  • Temporary Obstacles

  • Background noise

  • Physical Interference

  • Co-existing networks

  • Jammer

SIROCCO'13, Andrea Richa


Physical layer jamming

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


Motivation1

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


Motivation2

Motivation

Ideal world:

Usual approach adopted in theory.

: noise level

Background noise

0

time

SIROCCO'13, Andrea Richa


Motivation3

Motivation

Ideal world:

OR

Usual approach adopted in theory.

: noise level

Background noise

0

time

SIROCCO'13, Andrea Richa


Motivation4

Motivation

Ideal world:

OR (bounded, predictable)

Usual approach adopted in theory.

: noise level

Background noise

0

time

SIROCCO'13, Andrea Richa


Motivation5

Motivation

: noise level

Real world:

How to model this???

background

noise

0

time

SIROCCO'13, Andrea Richa


Our approach adversarial jamming

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


Our approach adversarial jamming1

Our Approach: Adversarial Jamming

  • Idea: model unpredictable behaviors via adversary (a.k.a. adversarial jammer)!

X

X

X

X

X

SIROCCO'13, Andrea Richa


Overview

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


    Wireless communication model

    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


    Single hop wireless network

    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


    Adaptive adversary

    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


    Constant competitive protocol

    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


    Our main contribution

    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


    Pros and cons

    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


    Physical layer traditional jamming defenses

    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


    Mac layer jamming defenses

    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


    Overview1

    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


    Simple yet powerful idea

    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


    Basic approach

    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


    Basic approach1

    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


    Na ve protocol

    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


    Problems

    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


    Problems1

    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


    Problems2

    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


    Mac protocol

    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


    Mac protocol1

    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


    Our results

    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


    Proof sketch

    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


    Proof sketch1

    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


    Continuous jamming

    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


    Overview2

    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


    Reactive adversary

    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


    Antijam reactive jamming resistant mac protocol

    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


    Our results1

    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


    Proof sketch fairness

    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


    Antijam throughput for 0 5

    AntiJam: Throughput for ε = 0.5

    ε = 0.5

    SIROCCO'13, Andrea Richa


    Antijam convergence time

    AntiJam: Convergence Time

    SIROCCO'13, Andrea Richa


    Antijam fairness

    AntiJam: Fairness

    SIROCCO'13, Andrea Richa


    Antijam vs non reactive protocol fairness

    AntiJam vs. Non-reactive protocol: Fairness

    SIROCCO'13, Andrea Richa


    Antijam vs 802 11

    AntiJam vs 802.11

    SIROCCO'13, Andrea Richa


    Overview3

    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


    Multihop wireless networks

    Multihop wireless networks

    Physical interference

    Jammed

    SIROCCO'13, Andrea Richa

    Powerful jammer


    Signal to interference noise ratio sinr

    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


    Multihop adversarial model

    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


    Throughput in multihop networks

    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


    Overview4

    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


    Leader election in adversarial single hop networks

    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


    Self stabilizing leader election

    Self-stabilizing Leader Election

    • Goal: design a self-stabilizing protocol that elects a single node as the leader, irrespective of the jamming activity

    • Challenges:

      • a leader node should let the others (followers or other leaders) know that he is still around

      • the followers should be able to notice when there is no leader in the network

    SIROCCO'13, Andrea Richa


    Leader election

    Leader Election

    Why is leader election difficult under an adaptive jammer?

    Example: exponential/polynomialbackoff

    : jammingactivity

    channel

    activity

    (expected)

    : messages

    0

    time

    constantsuccessprobability

    SIROCCO'13, Andrea Richa


    Why is leader election difficult under an adaptive jammer

    Why is leader election difficult under an adaptive jammer?

    Example 1: exponential/polynomial backoff

    : jammingactivity

    channel

    activity

    (expected)

    : messages

    0

    time

    constantsuccessprobability

    SIROCCO'13, Andrea Richa


    Why is leader election difficult under an adaptive jammer1

    Why is leader election difficult under an adaptive jammer?

    Example 2: reserved leader slot to notify nodes about leader

    : jammingactivity

    channel

    activity

    (expected)

    : leadermessage

    0

    time

    SIROCCO'13, Andrea Richa


    Why is leader election difficult under an adaptive jammer2

    Why is leader election difficult under an adaptive jammer?

    Example 2: reserved leader slot to notify nodes about leader

    : jammingactivity

    channel

    activity

    (expected)

    : leadermessage

    0

    time

    SIROCCO'13, Andrea Richa


    Other adversarial modeling in wireless networks

    Other adversarial modeling in wireless networks

    • Adversary: used to model external world

    • More bening:

      • Control packet injection rates

      • Control mobility

    • Intentionally disruptive:

      • jammers

    • More disruptive: malicious adversaries

      • Undermine security

      • Control Byzantine nodes (introduce fake messages)

    SIROCCO'13, Andrea Richa


    Other adversarial modeling in wireless networks1

    Other adversarial modeling in wireless networks

    • Adversarial packet injection/queueing:

      • [Chlebus, Kowalski, Rokicki, PODC’06],[Andrews, Jung, Stolyar, STOC’07],[Anantharamu, Chlebus, Rokicki, OPODIS’09],[Chlebus, Kowalski, Rokicki, Distributed Computing’09],[Lim, Jung, Andrews, INFOCOM’12]

    • Multi-channel access with adversarial jamming:

      • [Dolev, Gilbert, Guerraoui, Newport, DISC’07], [Anantharamu, Chlebus, Kowalski, Rokicki, SIROCCO’11],[Dolev,Gilbert, Khabbazian, Newport, DISC’11], [Daum, Gilbert, Kuhn, Newport, PODC’12], [Ghaffari, Gilbert, Newport, Tan, OPODIS’12]

    SIROCCO'13, Andrea Richa


    Other adversarial modeling in wireless networks2

    Other adversarial modeling in wireless networks

    • Broadcasting\Gossiping with adversarial jamming:

      • [Dolev, Gilbert, Guerraoui, Newport, DISC’07], [Dolev,Gilbert, Khabbazian, Newport, DISC’11], [Daum, Gilbert, Kuhn, Newport, PODC’12], [Ghaffari, Gilbert, Newport, Tan, OPODIS’12]

    • Capacity Maximization with adversarial jamming: [Dams, Hoefer, Kesselheim, unpublished]

    • Malicious adversary:

      • [Dolev, Gilbert, Guerraoui, Newport, DISC’07], [Gilbert, Young, PODC’12]

    • Infection spreading with adversarial mobility: [Wang,Krishnamachari, ]

    • Etc.

    SIROCCO'13, Andrea Richa


    Future work adversarial jamming

    Future work: Adversarial Jamming

    • Jamming-resistant protocols with power control:

      • Increasing power increases chance that signal will overcome jamming activity, however…

      • Increasing power also generates more interference…

      • Also adapt noise threshold level?

    • How about reactive jammers in multihop environments, under SINR?

    • Can the protocols be modified so that no rough bound on n and T are required?

      • stochastic/oblivious jammers: Simpler to handle? E.g., a constant gamma seems to work fine here.

    • Other applications of the MAC protocol (e.g., broadcast)?

    SIROCCO'13, Andrea Richa


    Future work

    Future Work

    • What would be your application of an adversary in wireless communication? 

    SIROCCO'13, Andrea Richa


    Collaborators

    Collaborators

    • Baruch Awerbuch (John Hopkins); Stefan Schmid (TU Berlin\Telekom Labs); Christian Scheideler and Adrian Ogierman (U. of Paderborn); Jin Zhang (Google)

    • Papers available from my webpage (for recent – and maybe not so recent  -- submissions, please send me email) at [email protected]

    SIROCCO'13, Andrea Richa


    Adversarial models for wireless communication

    Thank you!

    Questions?

    SIROCCO'13, Andrea Richa


    Adversarial models for wireless communication

    SIROCCO'13, Andrea Richa


    Adversarial models for wireless communication

    SIROCCO'13, Andrea Richa


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