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Optimum Routing Protection against Cumulative Eavesdropping in Multihop Wireless Networks Shafi Bashar, Zhi Ding Department of Electrical and Computer Engineering University of California, Davis Oct. 19, 2009

Optimum Routing Protection against Cumulative Eavesdropping

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Introduction

Wireless Communication : Wireless Medium Characteristics of Wireless Medium I

Fading I Good link : Difficult maintenance

I

Shared Medium I Eavesdropping I Jamming

I

Creates security challenges.


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Introduction

Multihop Wireless Network I

Ad-Hoc Network, Mesh Network I I

I

Difference in wireless structure I I I

I

Recently gaining attention Commercial and military Applications Cellular : Point to multi-point Radio : Point to point Ad-Hoc, Mesh : Hop to hop

Figure: Cellular Sytem

Security Challenges : Newer Issues I I I

Securing one hop → Not enough Cumulative leakage : A new issue PHY centric solution


Figure: Multihop

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Introduction

Cumulative Security Leakage : Alice and Bob Traditional Wireless Network

Alice

Bob LB-E

Eve Leakage=LB-E


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Introduction

Cumulative Security Leakage : Alice and Bob Multihop Wireless Network

Carol

Charlie

LCa-E

Alice

LCh-E

Bob LB-E Dave

Eve Leakage=LB-E + LCh-E + LCa-E


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Introduction

Securing Wireless Link : Traditional Approach Encryption Mechanism I Makes packet decoding harder I Does not prevent eavesdropping I Active Attack : Jamming, Denial of service etc. I Secure Routing : [Yu, Perrig ’04], [Djenouri, et.al. ’05] etc. I Passive Attack : Eavesdropping I [Kao, Marculesu ’07] I Eavesdropping Risk = f (Transmission Power). I limits on single hop transmission power. I [Lou, Liu, Fang ’03] I Eavesdropping Risk = Probabilistic value at each node. I Our Approach : I Eavesdropping Risk = PHY-centric measure I Coherent joint detection at Eve with multihop consideration. I


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Introduction

Eavesdropping Risk : Secrecy Measure Information Theory : Equivocation Rate I [Shanon ’49] : Amount of uncertainty left in a message in presence of Eve’s observation. I More Practical Measure I [Rodrigues, Almeida ’08]: Mean-square Error (MSE) of received signal. I [Bloch et.al. ’08]: Secrecy Outage Probability Pr (Rbob − Reve ≤ R) I Our Approach : I Consider leakage of information. I Measured by Received Signal-to-Noise Ratio (SNR) at Eve. I


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Introduction

Why SNR ?

I

More practical PHY-centric measure; understood by all. At low receive SNR → Decoded message unreliable.

I

Conclusion : Link is secured when

I

Eve’s SNR ≤ threshold I

Model coherent eavesdropping risk from multihop transmission well.


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Coherent and Joint Eavesdropping

Coherent Eavesdropping from Multihop Transmission es Eav

per drop

t1 Tx 1

tK

t2

Tx K

√ t1 , Tx 1 : y1 = p1 h1 s + n1 √ t2 , Tx 2 : y2 = p2 h2 s + n2 .. . √ tK , Tx K : yK = pK hK s + nK

Tx 2


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Coherent Eavesdropping from Multihop Transmission es Eav

per drop

t1 Tx 1

tK

t2

Tx K

√ t1 , Tx 1 : y1 = p1 h1 s + n1 √ t2 , Tx 2 : y2 = p2 h2 s + n2 .. . √ tK , Tx K : yK = pK hK s + nK

Tx 2

Eve stores y1 , . . . , yK and performs joint detection. I Signals → coherent, Noises → not coherent.

I

I I

Received SNR from Tx i : SNRi = Cumulative received SNR :

SNRc

pi |hi |2 σi2 PK

=

2 2 i=1 pi |hi | PK 2 2 i=1 pi |hi | σi K pi |hi |2 i=1 σ2

{

P Equal noise variance : SNRc = Ki=1 I Observation : SNRc depends on hi ’s.

I


}

=

P

(

SNRi

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Eavesdropper Channel Information

I

In practice, hi ’s → Not available.

I

Obtain partial channel information I I

Use information obtained from prior transmission Identify high-risk region a priori I High-risk region : Presence of Eve highly probable I e.g. Battlefield : Eve resides in unswept areas.


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Scenario 1 s

s

s


s

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Scenario 2 s s

s

s

s s


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Eavesdropper Channel Information

I

In practice, hi ’s → Not available.

I

Obtain partial channel information I I

I

Use information obtained from prior transmission Identify high-risk region a priori I High-risk region : Presence of Eve highly probable I e.g. Battlefield : Eve resides in unswept areas.

Distribute monitoring sensors.


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Scenario 1 s

s

s


s

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Scenario 2 s s

s

s

s s


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Problem Formulation

Multihop Routing Problem

s

Find S − D Path

s

s

s

Such that : Condition 1 : Routing metric → minimized / maximized Condition 2 : SNRi−e ≤ α [Individual Node Leakage] Condition 3 : SNRc ≤ β [Cumulative Leakage]


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Problem Formulation

Multihop Routing Problem

Condition 1 : Routing metric minimized/maximized Routing Metric Minimum Hop Count : AODV [Perkins,Royer’99], DSR [Johnson,Maltz’96], DSDV [Perkins,Bhagwat’94] I Extended Transmission Count (ETX) I Round Trip Time (RTT) I Link SNR I

Proposed Routing Algorithm can use any metric


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Problem Formulation

Routing Problem Condition 2 : Individual Node Leakage Ensures leakage from each node is low I Local constraint : Each node checks its own

I

Condition 3 : Cumulative Leakage Ensures overall transmission leakage is low I Global constraint : Depends on route selection

I


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Problem Formulation

Revised Routing Problem No Cond. 2, Cond. 3 : Shortest path problem I Consider nodes with Condition 2 satisfied

I


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Problem Formulation


I

  minimize hop count maximize min SNRlink Find S-D path to  minimize / maximize other metric subject to :

SNRc ≤ β


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Problem Formulation


I

  minimize hop count maximize min SNRlink Find S-D path to  minimize / maximize other metric subject to :

SNRc ≤ β

Resource Constrained Shortest Path Problem


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Routing Algorithm

Resource Constrained Shortest Path Problem

Resource Constrained Shortest Path I NP-complete problem [Lawler ’01] I Quantize SNR values I Dynamic programming algorithm with pseudo-polynomial runtime


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Routing Algorithm

Resource Constrained Shortest Path Problem I G = (V , E ) : Network as a graph I wij : link quality of arc (i, j) I `ij : leakage on arc (i, j) I Ui (j, `) : Value of optimum link quality from i to j such that total

leakage on the path does not exceed `

Recursive relation :

Ui(j,l-1)

i

j

Ui(j,l) = ?

k∈V \j

Ui ( k,llkj )

,l kj w kj

l (j, Ui

k

)j

w

-l m

m j ,l m j

Ui (j, `) = min Ui (j, ` − 1) , min {Ui (k, ` − `kj ) + wkj }

m


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Routing Algorithm

Proposed Algorithms

Algorithm 1 Centralized Algorithm I Runtime O(n3 L) I L : No. of quantization state I Similar to Bellman-Ford with additional dimension I


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Routing Algorithm

Proposed Algorithms Algorithm 2 I I I I I

Distributed Algorithm Similar to distance vector (DV) algorithm Each node use localized information Occasionally transmits table to neighbors Use sequence number to avoid looping


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Numerical Result

Numerical Simulation I

I

Simulation Setup I

10×10 Grid

I

Source at (0, 5)

I

Destination as (10, 5)

I

Intermediate node randomly distributed

Performance Comparison I

Optimum routing without security concern

I

Most secured path without link quality concern

I

Proposed solution


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Numerical Result

Successful secured routing (%)

Minimum hop routing : Successful secured routing 1

0.8

0.6 0.4

(i) Minimum hop routing w/o security concern (ii) Most secured path w/o concern on no. of hop (iii) Proposed algorithm

0.2

0

5

10

15

20

25

30

No. of intermediate nodes


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Numerical Result

Minimum hop routing : Hop count 10 9

Average no. of hops

8 7 6 5

(i) Minimum hop routing w/o security concern (ii) Most secured path w/o concern on no. of hop (iii) Proposed algorithm

4 3 2 1 0

5

10

15

20

25

30



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Numerical Result

Max-min SNR routing : Successful secured routing

Successful secured routing (%)

1 0.9

(i) Best max−min SNR path w/o security concern (ii) Most secured routing path w/o link quality concern (iii) Proposed algorithm

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

5

10

15

20

25

30



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Numerical Result

Max−min SNR (dB) of the routing path

Max-min SNR routing : Max-min SNR 9 8 7 6 5 4 3

(i) Best max−min SNR path w/o security concern (ii) Most secured routing path w/o link quality concern (iii) Proposed algorithm

2 1 0

5

10

15

20

25

30



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Conclusion

Conclusion

I

Multihop wireless networks : Newer security challenges

I

Newer approaches : PHY-centric solution

I

Cumulative security leakage

I

Routing involving physical layer parameters

I

Trade-off : Pseudo-polynomial rather than polynomial time solution; Improved security.


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Conclusion

Thank You


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