Securing
Wireless Sensor Networks
Wenliang (Kevin) Du
Department of Electrical Engineering
and Computer Science
Syracuse University
Overview
Overview of Wireless Sensor Networks (WSN).
Security in wireless sensor networks.
Why is it different?
Our recent work on securing WSN using deployment
knowledge
Authenticating public keys (Mobihoc’05)
Robust Location discovery (Infocom’05)
Summary
Wireless Sensors
Berkeley Motes
Mica Motes
Mica Mote
:
Processor: 4Mhz
Memory: 128KB Flash and 4KB RAM
Radio: 916Mhz and 40Kbits/second.
Transmission range: 100 Feet
TinyOS
operating System: small, open
source and energy efficient.
Wireless Sensor Networks
(WSN)
Deploy
Sensors
Applications of WSN
Battle ground surveillance
Enemy movement (tanks, soldiers, etc)
Environmental monitoring
Habitat monitoring
Forrest fire monitoring
Hospital tracking systems
Tracking patients, doctors, drug administrators.
Securing WSN
•
Motivation: why security?
•
Why not use existing security
mechanisms?
–
WSN features that affect security.
Why Security?
•
Protecting
confidentiality
,
integrity
, and
availability
of the communications and
computations
•
Sensor networks are vulnerable to
security attacks due to the broadcast
nature of transmission
•
Sensor nodes can be
physically
captured or destroyed
Why Security is Different?
•
Sensor Node Constraints
–
Battery,
–
CPU power,
–
Memory.
•
Networking Constraints and Features
–
Wireless,
–
Ad hoc,
–
Unattended.
Sensor Node Constraints
•
Battery Power Constraints
–
Computational Energy Consumption
•
Crypto algorithms
•
Public key vs.
Symmetric key
–
Communications Energy Consumption
•
Exchange of keys, certificates, etc.
•
Per

message additions (padding, signatures,
authentication tags)
Memory Constraints
•
Program Storage and Working Memory
–
Embedded OS, security functions (Flash)
–
Working memory (RAM)
•
Mica Motes:
•
128KB Flash and 4KB RAM
An Efficient Scheme for
Authenticating Public Keys
in Sensor Networks
Wireless Sensor Networks
Deploy
Sensors
Key Distribution in WSN
Deploy
Sensors
Secure Channels
Existing Approaches
Key Pre

distribution Schemes
Eschenauer and Gligor, CCS’02
Chan, Perrig, and Song, S&P’03
Du, Deng, Han, and Varshney, CCS’03
Du, Deng, Han, Chen, Varshney, INFOCOM’04
Liu and Ning, CCS’03
Assumption
Public Keys
are impractical for WSN
We need to use
Symmetric Keys
Three Years Later
Has Public

Key Cryptography (PKC) became
practical yet?
The answer might still be NO, but …
Recent Studies on using PKC on sensors
PKC is feasible for WSN
ECC signature verification takes 1.6s on
Crossbow motes (Gura et al.)
The Advantage of PKC
Resilience versus Connectivity
SKC

based schemes have to make tradeoffs
between resilience and connectivity
PKC

based Key Distribution
100% resilience
100% connectivity
Let’s Switch to PKC?
Sorry, I forgot to mention one thing:
The gap between SKC and PKC is not going to
change much unless a breakthrough in PKC
occurs.
Computation costs
RC5 is 200 times faster than ECC
Communication costs
Signatures: ECC (320 bits), RSA (1024 bits),
SHA1 (160 bits)
New Focuses
My observation:
We will be able to use PKC, but we will use SKC if
that can save energy.
We are doing this in traditional networks
Example: session keys
Research Problem
Can we reduce the amount of PKC
computations with the help of SKC?
Public Key Authentication
Before a public key is used, it must be
authenticated
In traditional networks: we use certificates.
Verifying certificates is a public key
operation
Authenticating Public Keys
in Traditional Networks
1. What is your public key?
2. Here is my public key PK
2. Here is my public key PK and
certificate
3. Verify the certificate: a public key operation
A
B
Authenticating Public Keys
in Sensor Networks
Naïve Solution 1: preload all the public keys
Memory cost:
(N

1)*320
bits for 160

bit ECC
Naïve Solution 2: preload the
hash
of all the
public keys
Hash is the commitment.
Memory cost:
(N

1)*160
bits for SHA1
Can We Improve Memory Usage?
Much less than
N

1
commitments
Hash everything together: need
1
commitment
Communication cost:
O(N)
A standard technique: Merkle Tree
Memory cost:
O(log N)
Communication cost:
O(log N)
Using Merkle Trees
Performance
Memory Usage
1 + log(N)
hash values (compared to
N

1
)
Computation Cost
Log(N)
hash operations
Communication Overhead
If we use 160

bit SHA1
160 * log(N)
bits
When N=10,000, cost=2080 bits, worse than PKC
We need to reduce the height
Trimming the Merkle Tree
A Smarter Trimming
A
B
C
Deployment Knowledge
How do we know that some nodes might
more likely be neighbors than others?
Deployment knowledge model.
A Group

Based Deployment
Scheme
A Group

Based Deployment
Scheme
Modeling of The Group

Based
Deployment Scheme
Deployment Points
Trimming Strategy
Deployment

based Trimming
Finding Optimal
a
,
b
,
c
, and
d
The optimization problem:
S
:
number of sensors in each deployment group
m
max
:
maximum amount of memory that can be used
W
i
:
percentage of nodes that are in the i group.
This is decided by the deployment model
We assume the Gaussian Distribution
Minimize
C = w
0
• a + w
1
• b + w
2
• c + w
3
• d
Subject to
Evaluation
Communication Overhead vs.
Memory Usages
Communication Overhead vs.
Network Size
Impact of
Deployment Knowledge:
σ
Deployment Model:
Gaussian Distribution
Impact of Modeling Accuracy
Energy consumption
Comparing Energy cost with
RSA / ECC
Performance of authenticating public keys
using various algorithms
Summary
Public Key Cryptography (PKC)
Will soon be available for sensor networks
Intel Motes: very powerful.
Usage of PKC should still be minimized
We propose an efficient scheme to achieve public
key authentication.
A Beacon

Less
Location Discovery Scheme
for Wireless Sensor Networks
Location Discovery in WSN
Sensor nodes need to find their locations
Rescue missions
Geographic routing protocols
Many other applications
Constraints
No GPS on sensors
Cost must be low
Existing Positioning Schemes
Beacon Nodes
Two Important Elements
Reference points
They must know their locations.
e.g. beacon nodes, satellites.
Relationship
between nodes and reference
points
Distance
Angle of arrival
Time of arrival
Time difference of arrival
The Beacon

Less Scheme
Without using beacon nodes
Beacon nodes are more expensive
They can be the main target of attacks
Nonetheless, we still have to find
reference
points
and the corresponding
relationships
.
Remember: the locations of the reference points
must be known.
Modeling of The Group

Based
Deployment Scheme
We still need another important element:
The
relationship
between nodes and reference points.
Deployment Points:
Their locations are known.
The Relationships
A
The Relationships
A
B
Modeling of the Deployment
Distribution
Using pdf function to
model the node
distribution.
Example: two

dimensional Gaussian
Distribution.
Other distribution can
also be used.
The Idea
Observation at location O
See more nodes from A and D
than from H and I.
Observation at location P
Quite different from location O.
See more nodes from H and I
than from A and D.
Given a location, we can
derive the observation.
Given the observation, can we
derive the location?
The Problem Formulation
Location
θ
=
(x, y)
Observation
a
=
(a
1
, a
2
, … a
n
)
Location
Estimation
A Solution
Definitions
a
=
(a
1
, a
2
, … a
n
)
:
The observation.
f
n
(
a

θ
)
:
The probability of observing
a
at location
θ
.
Maximum

Likelihood

Estimation (MLE)
Principle:
find
θ
, such that
f
n
(
a

θ
)
is
maximized
.
Maximum Likelihood Estimation
Likelihood Function
f
n
(
a

θ
) =
Pr (
X
1
=a
1
, …, X
n
=a
n

θ
)
=
Pr (
X
1
=a
1

θ
) ∙ ∙ ∙ Pr (
X
1
=a
n

θ
)
L(
θ
)
=
log
f
n
(
a

θ
)
Find
θ
:
Gradient Descent Method
0
)
(
0
)
(
y
L
x
L
Evaluation
Setup
A square plane: 1000 meters by 1000 meters
10 by 10 grids (each is 100m X 100m)
σ
= 50 (Gaussian Distribution)
What to evaluate?
Accuracy vs. Density
Accuracy vs. Transmission Range
Boundary Effects
Computation Costs.
Effect of Density m
An Improvement:
Dummy Nodes
m: number of sensors in each group
Effect of Transmission Range R
Effect of Boundary
Comparing the Three Numeric
Approaches (Cost)
Comparing the Three Numeric
Approaches (Accuracy)
Comparisons
Beacon

Less
Beacon

Based
Communication Overhead
Low
Low
Computation Cost
High
Low
Device Cost
Low
High
Robustness/Security
High
Low
Mobility
None
Good
Conclusion and Future Work
Two Applications of Deployment Knowledge
Authenticating Public Keys
Beacon

Less Location Discovery
IPDPS’05 paper: Location Anomaly Detection
Future Work
Optimizing public

key protocols for sensor
networks
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