1
A Secure Communication
Protocol For Wireless
Biosensor Networks
Masters Thesis by
Krishna Kumar Venkatasubramanian
Committee:
Dr. Sandeep Gupta
Dr. Rida Bazzi
Dr. Hessam Sarjoughian
2
Overview
Introduction
Problem Statement
System Model
Proposed Protocols
Security Analysis
Implementation
Conclusions & Future Work
3
Biomedical Smart Sensors
Miniature wireless systems.
Worn or implanted in the body.
Prominent uses:
Health monitoring.
Prosthetics.
Drug delivery.
Each sensor node has:
Small size.
Limited
memory
processing
communication capabilities
Environment
(Human Body)
sensors
Base
Station
Communication
links
4
Motivation for biosensor security
Collect sensitive medical data.
Legal requirement (HIPAA).
Attacks by malicious entity:
Generate fake emergency warnings.
Prevent legitimate warnings from being reported.
Battery power depletion.
Excessive heating in the tissue.
5
Problem Statement
Direct communication to the BS can be prohibitive.
To minimize communication costs, biosensors can be organized
into specific topologies.
Cluster topology is one of the energy
-
efficient communication
topologies for sensor networks [HCB00].
Traditional cluster formation protocol is not secure.
We want to develop protocols which allow for secure
cluster formation in biosensor networks.
6
Cluster Topology
Cluster head
Cluster
Cluster Member
Base Station
7
Traditional Cluster Formation
Protocol
CH1
CH2
CH3
1
2
3
4
5
Environment
Weaker signal
8
Security Flaws
HELLO Flood and Sinkhole Attack
1
2
3
Malicious Entity
acting as a
SINKHOLE
Weaker signal
CH2
CH1
The sinkhole can
now mount
selective
forwarding
attacks
on the
biosensors in its
“cluster”.
Malicious entity
can mount a
Sybil attack
where it presents
different
identities to
remain CH in
multiple rounds.
9
Security Flaws contd..
Node with
surrounding
tissue at
above normal
temperature.
Node with
surrounding tissue at
normal temperature.
tissue
Node with
dead
battery
Network Partitioning.
Malicious
entity sending bogus messages to sensor and depleting its energy.
Malicious
entity
having unnecessary communication with a sensor
causing heating in the nearby tissue.
10
System Model
ADVERSARIES:
Passive:
Eavesdrop on
communication and
tamper with it.
Active:
Physically
compromise the
external biosensors.
Temperature
sensor
Glucose sensor
11
Trust Assumptions
The wireless communication is
broadcast in nature and not trusted.
The biosensors do not trust each other.
Base Station is assumed not to be
compromised.
12
Key Pre
-
Deployment
Each biosensor shares a unique pair
-
wise key (
master
key
) with the BS. This key is called
NSK
We do not use NSK directly for communication, we
derive 4 keys from it (
derived keys
):
Encryption Keys
MAC Keys
K
N
-
BS
= H(NSK,1)
K’
N
-
BS
= H(NSK,2)
K
BS
-
N
= H(NSK,3)
K’
BS
-
N
= H(NSK,4)
13
Biometrics
Physiological parameters like
heart rate and body
glucose.
Used for securing/authenticating communication
between two biosensors which do not share any
secret.
Usage Assumptions:
Only biosensors in and on the body can measure biometrics.
There is a specific pre
-
defined biometric that all biosensors can measure.
14
Issues with Biometrics
Biometric value data
-
space is not large enough.
Possible Solutions
:
Combine multiple biometric values.
Take multiple biometric measurements at each time.
Limit the validity time of a biometric value.
Biometric values at different sites produce different
values.
Solution Proposed in Literature
:
These differences are independent. [Dau92]
Can be modeled as channel errors. [Dau92]
Fuzzy commitment scheme based on [JW99] used to correct differences.
Can correct up to two bit errors in the biometric value measured at the
sender and receiver.
15
Biometric Authentication
BMT
1
2
3
4
5
ST
6
Time
-
Period
Measure biometric:
BioKey
Generate
data
Compute Certificate:
Cert [data] = MAC ( KRand, data),
γ
γ
= KRand
䉩潋ey
Send Msg:
data, Cert [data]
Measure biometric:
BioKey’
Receive Msg:
data, Cert [data]
Compute MAC Key:
KRand’ =
γ
†
BioKey’
f
(KRand’) = KRand
Compute Certificate MAC
And compare with received:
MAC (KRand, data)
SENDER
RECEIVER
Biometric Measurement Schedule
16
Centralized Protocol Execution
Node
j
䅬l:
ID
j
, NonceN
j
, MAC(K’N
j
–
BS, ID
j
| NonceN
j
), Cert[ID
j
, NonceN
j
]
CH
p
䉓B
ID
j
, NonceNi ,
MAC
(K’N
j
–
BS, ID
j
| NonceN
i
),
CH
p
, SS, E<K CH
p
-
BS, Cntr>(KCH
-
N),
MAC
(K’CH
p
–
BS,
CH
p
|
SS | E<K CH
p
-
BS, Cntr>(KCH
-
N) | Cntr)
BS
乯de
j
:
CH
p
,
E<K BS
-
N
j
, Cntr’> (KCH
-
N), Cntr’, MAC(K’BS
-
N
j
,
CH
p
|
NonceN
j
| Cntr’ | E<K BS
-
N
j
, Cntr’> (KCH
-
N))
CH 1
Sensor Node
Base Station
CH 2
CH 3
CH1
CH 2
CH 3
CH 3
17
Distributed Protocol Execution
CH
j
䅬氺
CH
j,
NonceCH
j
, E<KRand, Cntr>(Ktemp), Cert[ID
j
, Cntr, NonceCH
j
],
λ
λ
= BioKey
KRand
Node
k
䍈
z
:
ID
k
, MAC (Ktemp, ID
k
| NonceCH
z
| Cntr | CH
z
)
CH 1
CH 2
CH 3
Sensor Node
18
Extensions
Distribute keys based on attributes.
Allows efficient data communication.
The BS distributes the keys.
For centralized ABK, sent during cluster formation.
For distributed separate step needed.
19
Security Analysis (Passive Adversary)
Hello Flood and Sinkhole Attack
Centralized:
Malicious entity does not have appropriate keys
to pose as legitimate CH.
Distributed:
Malicious entity cannot compute biometric
certificate.
20
Security Analysis (Passive Adversary)
Sybil Attack
No entity can become part of network without
having appropriate keys.
Identity Spoofing
Cannot pose as BS, no pair
-
wise (derived) keys.
Cannot pose as CH, no keys to authenticate data
to BS.
Cannot pose as sensor node, cannot measure
biometric to fool CH.
21
Security Analysis (Active Adversary)
CH compromise
Centralized: Security policy at BS to limit number
of sensor nodes in a cluster.
Distributed: Need intruder monitoring scheme.
Sensor Node compromise
Intruder monitoring scheme needed for both
protocols.
22
Implementation
We have implemented the two cluster
formation protocols and their extensions.
The implementation was done on the Mica2
sensor motes.
We used TinyOS sensor operating system for
writing our programs.
For security primitives TinySec used.
23
Implementation contd..
Encryption
–
SkipJack
Message Authentication Code
–
CBC
-
MAC
We had 4 sensor nodes 3 CH and 1 BS in our
implementation.
We simulated two main attacks on our
implementation, both of which failed:
HELLO Flood attack.
Identity spoofing of sensor node to infiltrate the
network.
24
Comparison
Security adds a overhead to
the protocol.
We compared overhead in
terms of energy consumption.
To compare the protocols, we
analyzed them using the
communication model given in
[HCB00].
E
trans
= E
tx
* k + E
cx
* k * d
2
E
recp
= E
rx
* k
Node ID = 8 bits
Nonce = Counter =
128 bits
Key = 128 bits
Signal Strength = 16
bits
E
trans
= E
recp
= 50 nJ/bit
E
cx
= 100pJ/bit/m
2
Number of Nodes = 100
-
1500
Sensor
-
BS distance =
0.75 m
Inter
-
sensor distance
= 0.1 m
MAC size = 64 bits
25
Security Overhead
Comparison of Secure (without extension) and Non
-
secure
Cluster Formation Protocols (CH = 5%)
26
Extension Overhead
Comparison for Secure Cluster Formation Protocols with
their extensions (CH = 5%)
27
Conclusions & Future Work
Protocols developed successfully prevent many of the
potent attacks on the traditional cluster formation
protocol.
Biometric based authentication used for ensuring
authentication without previous key exchange.
Biometrics not traditionally random and schemes are
needed to randomize them.
Better error correction schemes are needed which
can correct larger differences in measured
biometrics.
28
Reference
[JW99] Ari Juels and Martin Wattenberg
. “A fuzzy commitment scheme”
.
1999.
[Dau92] J. Daugman,
“High Confidence personal identification by rapid
video analysis of iris texture”
, IEEE International Carnahan Conference on
Security Technology, pp 50
-
60, 1992.
[LGW01] L. Schwiebert, S. K. S. Gupta, J. Weinmann et al.,
“Research
Challenges in Wireless Networks of Biomedical Sensors”
, The Seventh
Annual International Conference on Mobile Computing and Networking, pp
151
-
165, Rome Italy, July 2001
.
[HCB00] W. Rabiner Heinzelman, A. Chandrakasan, and H. Balakrishnan,
“Energy
-
Efficient Communication Protocol for Wireless Microsensor
Networks”, Proceedings of the 33rd International Conference on System
Sciences (HICSS '00), January 2000.
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