Song Fang Yao Liu Wenbo Shen Haojin Zhu 1 Content Location d istinction Virtual m ultipath attacks Defense Experiment Summary 2 Goal of l ID: 269441
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Where Are You From? Confusing Location Distinction Using Virtual Multipath Camouflage
Song Fang,
Yao Liu
Wenbo Shen, Haojin Zhu
1Slide2
Content
Location
distinction
Virtual m
ultipath attacksDefense Experiment Summary 2Slide3
Goal
of location distinction
Detect a wireless user’s location change, movement or facilitate location-based authentication.
3Slide4
Wireless
sensor
network: Location distinction can
prevent an unauthorized person from
moving the sensors away from the area of interest
Applications:
4Slide5
Example
1:
5Slide6
E
xample
1
(Cont’d):6Slide7
Applications:
Wireless
sensor network: Location
distinction can prevent an
unauthorized person from moving the sensors away from the area of interest Sybil attack: Location distinction can detect identities originated from the same location
7Slide8
E
xample
2:
8Slide9
X
E
xample
2 (Cont’d):From the same l
ocation
9Slide10
Applications:
Wireless
sensor
network: Location distinction can prevent an unauthorized person from moving the sensors away from the area of interest
Sybil
attack:
Location
distinction
can
detect
identities
originated
from
the
same
location
RFID:
Provide
a
warning
and
focus resources
on
moving
objects (Location Distinction [MobiCom’ 07]).
10Slide11
E
xample
3:
MoveControl11Slide12
E
xample
3:
MoveControl12Slide13
Existing ways to
realize location distinction
Wireless
channel characteristics
Change
Location
change
Spatial
uncorrelation
property
Attack
:
Generate
“
arbitrary”
characteristic
FAIL
!!
13Slide14
Multipath components
Component response:
Characterizes the distortion that each path has on the multipath component
Channel impulse response
: The superposition of all component responses
Multipath
effect
Received signal
Transmitted signal
14Slide15
The
channel impulse response changes as the receiver or the transmitter changes location
Channel impulse response
Channel impulse responses
can be utilized to provide
location distinction.
Calculate the difference
15Slide16
Training sequence based channel estimation
Channel Estimation
Training Sequence
x
x
y
Estimator
x
h
Training Sequence
x
Channel Impulse response
16Slide17
Channel
Estimation (Cont’d)
Rewrite the received symbols
A
Toeplitz
matrix
Least-square (LS) estimator
17Slide18
Content
Location
distinction
Defense
Experiment Summary Virtual multipath attacks18Slide19
Example
: Creating a
virtual multipath
19Slide20
Attack
Overview: delay-
and-sum process.
The
i
th
delayed
signal
copy
Virtual channel impulse response
The attacker’s aims to make
20Slide21
Send the aggregated
signal
to the real multipath channelTechnical Challenge: Obtaining the weights
21Slide22
Content
Location
distinction
Defense
Experiment Summary Virtual multipath attacks22Slide23
Defending
against
the attack: Adding a helper
23Slide24
Defending
against
the attack: Adding a helper
In this case, the attacker must know the real channel impulse response between herself and the helper.
24Slide25
Defending
against
the attack: Adding a helper
For
Receiver:
For
Helper:
25Slide26
Attackers
with
helperCan be set passively: it doesn’t actively send out wireless signals to channel
To fool both the receiver and the receiver’s helper, the attacker needs to know the real channel impulse responses:
Fail to launch attacks
Unknown
26Slide27
Content
Location
distinction
Defense
Experiment Summary Virtual multipath attacks27Slide28
Experiment
floorplan
Transmitter
: RXReceiver:
10
locations
Each
node:
a
USRP
connected
with
a
PC
Trials: 100
per
location
M
ultipath:
L
=5
28Slide29
Example
attacks I
Randomly
chosen channel impulse response Euclidean distance:29Slide30
Example
attacks II
Euclidean
distance
:Recover another channel impulse response in another building (CRAWDAD data set[1])
[1] SPAN, “Measured channel impulse response data set,”
http://span.ece.utah.edu/pmwiki/pmwiki.php?n=Main.MeasuredCIRDataSet
.
30Slide31
Overall attack i
mpact
95%
is much
larger
than
with
high
probability
5
%
d
est
=
||
estimated CIR
under
attacks
-
chosen CIR
||
0.25
0.9
d
real
=
||
estimated CIR under attacks
-
real
CIR
||
31Slide32
Experiment
floorplan
Place the attacker and
the helper
at
each
pair
of
the
10
locations:
10×9=90
pairs
.
Attacker
Helper
32Slide33
Defense
feasibility evaluation
Receiver
Receiver’s
helper (Location 8)
The Euclidean distance between both estimates:
Attacker: Location 2
33Slide34
Defense
performance evaluation
Conclusion:
The
helper node is effective to help detect virtual multipath attacks.34Slide35
Content
Location
distinction
Defense
Experiment Summary Virtual multipath attacks35Slide36
Summary
We
identified a new attack against existing location distinction approaches that built on the spatial uncorrelation property of wireless channels.
We proposed a detection technique that utilizes a helper receiver to identify the existence of virtual channels.
36Slide37
Thank
you!
Any questions?
37