1. Memory Efficient Protocols for Detecting Node Replication Attacks in Wireless Sensor Networks
Ming Zhang, Vishal Khanapure, Shigang Chen, Xuelian Xiao
University of Florida
Presented by Ming Zhang
ICNP’09
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2. Outline Introduction Related Work Our Solutions Simulation Conclusion
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3. Introduction Node Replication Attack [1] Introduction Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Node Replication Attack [1]
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4. Introduction Identity-Based Public Key System [2]
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Identity-Based Public Key System [2]
Master public key & master private key
Unique ID & private key
However, the problem is not solved
Bind node’s ID and its location
Cα = <IDα, lα, [H(IDα, lα)]K-1α >
where,
IDα = unique ID of α
lα = location of α
H = hash function
K-1α = private key of α
How to find two conflicting location claims
Both location claims are verifiable
Same ID & different locations
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5. Related Work Distributed Detection Line-Selected Multicast (LSM) [1]
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion α
γ receives Cα and Cα' γ
replicas
witness nodes α'
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6. Related Work Memory Overhead Problem Crowded Center Problem
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Memory Overhead Problem
Each node needs to store location claims
Crowded Center Problem
The nodes in the central area experience much higher memory and communication overhead than the nodes near borders
Cross Over Problem
Two line segments may not intersect at a common node
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7. B-MEM Memory Efficient Multicast using Bloom filters (B-MEM)
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Memory Efficient Multicast using Bloom filters (B-MEM)
ID Bloom filter & Location Bloom filter 1 1 1 1 1 1
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8. Cα' conflicts with the two Bloom filters at γ
B-MEM
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Memory Efficient Multicast using Bloom filters (B-MEM)
Only the first and the last nodes (witness nodes) store a complete copy of the location claim
Other nodes (watcher nodes) only record the fingerprint of the location claim in the two compact Bloom filters
Do claim chase if conflict is detected β
Cα' conflicts with the two Bloom filters at γ α
β has both Cα and Cα' γ w
w has both Cα and Cα'
replicas
β '
witness nodes α'
watcher nodes
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9. BC-MEM
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Memory Efficient Multicast using Bloom filters and Cell forwarding (BC-MEM)
The network is divided into virtual cells
Each node is mapped to an anchor node in each cell
The location claim is relayed by the anchor nodes in each cell that the line segment intersects w β α γ w'
replicas
β '
witness nodes α'
watcher nodes
& anchor nodes
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10. C-MEM Memory Efficient Multicast using Cross forwarding (C-MEM)
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Memory Efficient Multicast using Cross forwarding (C-MEM)
First forward the location claim to a random point (cross point)
Then, forward the location claim horizontally and vertically to form a cross
Overhead is evenly distributed among horizontal and vertical line segments α β γ w w'
γ '
replicas
witness nodes
β '
watcher nodes α'
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11. CC-MEM
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Memory Efficient Multicast using Cell forwarding and Cross forwarding (CC-MEM)
Use the Bloom filters to solve Memory Overhead Problem
Use the Cell forwarding to solve Cross Over Problem
Use the Cross forwarding to solve Crowded Center Problem β α γ w w'
replicas
γ '
witness nodes
β ' α'
watcher nodes
& anchor nodes
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12. Simulation Simulation Setup
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Simulation Setup
Deploy 1000 to nodes in a 1000 ×1000 area
Each location claim is 46 bytes
2 bytes for ID
4 bytes for location
40 bytes for digital signature as DSS [3]
Insert two replicas
Run on 200 different random networks
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13. Simulation Detection Probability B-MEM is comparable to LSM
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Detection Probability
B-MEM is comparable to LSM
BC-MEM and C-MEM beat LSM in all network sizes
CC-MEM achieves nearly 100% detection probability
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14. Simulation Memory Overhead Average memory consumption
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Memory Overhead
Average memory consumption
CC-MEM reduces by up to 91%
Maximal memory consumption
CC-MEM reduces by up to 97%
Memory overhead is evenly distributed
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15. Simulation Energy Overhead Average energy consumption
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Energy Overhead
Average energy consumption
B-MEM BC-MEM C-MEM use a little more energy (about 3% on average)
CC-MEM reduces by up to 15%
Maximal energy consumption
C-MEM reduces by up to 41%
CC-MEM reduces by up to 47%
Energy consumption is balanced
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16. Conclusion
Introduction
Related Work
B-MEM
BC-MEM
C-MEM
CC-MEM
Simulation
Conclusion
Proposed four new protocols to detect node replication attacks
Reduce memory overhead significantly
Balance the memory and energy overhead
Improve the detection probability to near 100%
Thanks! & Questions?
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17. Reference
Introduction
R-ISR
MRU-ISR
I-ISR
ID-ISR
Simulation
Conclusion
[1] B. Parno, A. Perrig, and V. Gligor, “Distributed Detection of Node Replication Attacks in Sensor Networks,” Proc. of IEEE Symposium on Security and Privacy (S&P), Pages 49-63, 2005.
[2] A. Shamir, “Identity-based Cryptosystems and Signature
Schemes,” Proc. Of CRYPTO’84 on Advances in cryptology, Springer-Verlag, Pages 47-53, 1985
[3] “Digital Signature Standard,” FIPS PUB 186-3, March 2006
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