Localization-Free Detection of Replica Node Attacks in Wireless Sensor Networks Using Similarity Estimation with Group Deployment Knowledge
Abstract
:1. Introduction
2. Related Works
3. Preliminaries
3.1. Network Assumptions
3.2. Sensor Deployment Strategies
3.3. Attack Model
4. Localization-Free Replica Detection Based on Similarity Estimation
4.1. Problem Statement
4.2. Neighborhood-Based Detection Metric
4.3. Protocol Description
4.4. Obtaining the Replica Detection Threshold
- Step 1.
- We obtain the actual physical position , where , and derive the actual DNV for the selected nodes, represented as:
- Step 2.
- We collect the field of LCQ message from the N selected nodes, and compute the corresponding estimated position where . Then we derive the estimated DNV for the selected nodes, represented as:
- Step 3.
- We collect the IDs field of the LCQ message from the selected nodes, and derive the ONV for them: .
- Step 4.
- We derive the NV-LS of and , denoted by and , respectively, where , and . Then we can compute the deviation between and caused by the network uncertainties and measurement errors, represented as:
5. Security Analysis
5.1. Limitation of the Impact Range of Node Replica Attack
5.2. Defense Capacity Analysis on Location Claim-Based Detection
6. Performance Evaluation
6.1. Communication, Computation, and Storage Overhead
6.2. Experimental Setup and Methodology
- Step 1.
- After deployment, we randomly pick k () nodes from the network topology, and mark them as compromised nodes. Let denote the locations of these k compromised nodes.
- Step 2.
- For each compromised node, we generate r replica nodes and place them D meters away from their original compromised nodes. denote the locations of compromised node i’s (i = 1, 2, …, k) replicas, where .
- Step 3.
- In attack strategy I, the replica node modifies its group identity GID to the nearest group while keeping its NID the same with its origins. In attack strategy II, the replica nodes keep their field consistent with the IDs field in their LCQ message. In attack strategy III, the replica nodes make their field the same with their original compromised nodes, and keep their IDs field consistent with the field in the LCQ message.
6.3. Results and Discussion
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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the communication radius of sensor nodes and beacons | secret key shared between node i and j | ||
the trust threshold | secret key shared between node i and BS | ||
the threshold of confliction detection | the certification signed by BS | ||
the threshold of replica detection | LAQ/LAR | the location authentication request/reply | |
the derived neighboring vector | NAN | the node authentication needed request | |
the observed neighboring vector | LCQ/LCD | the location claim request/decision | |
private/public key of BS |
Scheme | Communication Overhead |
---|---|
Randomized Multicast [6] | |
Line-selected Multicast [6] | |
Location Claim Scheme I [11] | Negligible |
Location Claim Scheme II [11] | |
Location Claim Scheme III [11] | |
Our proposed scheme |
Scheme | Computation Overhead |
---|---|
Randomized Multicast [6] | |
Line-selected Multicast [6] | |
Location Claim Scheme I [11] | Negligible |
Location Claim Scheme II [11] | |
Location Claim Scheme III [11] | |
Our proposed scheme |
Scheme | Claim Storage Overhead |
---|---|
Randomized Multicast [6] | |
Line-selected Multicast [6] | |
Location Claim Scheme I [11] | Negligible |
Location Claim Scheme II [11] | |
Location Claim Scheme III [11] | |
Our proposed scheme | Negligible |
Parameter | Value |
---|---|
Power decay in reference distance (A) | 55 dB |
Maximum data rate | 250 Kbps |
Packet size | 36 Bytes |
Average radio noise floor | −110 dBm |
Standard deviation for WGN | 4.0 dB |
Receiving Sensitivity | −105 dBm |
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Ding, C.; Yang, L.; Wu, M. Localization-Free Detection of Replica Node Attacks in Wireless Sensor Networks Using Similarity Estimation with Group Deployment Knowledge. Sensors 2017, 17, 160. https://doi.org/10.3390/s17010160
Ding C, Yang L, Wu M. Localization-Free Detection of Replica Node Attacks in Wireless Sensor Networks Using Similarity Estimation with Group Deployment Knowledge. Sensors. 2017; 17(1):160. https://doi.org/10.3390/s17010160
Chicago/Turabian StyleDing, Chao, Lijun Yang, and Meng Wu. 2017. "Localization-Free Detection of Replica Node Attacks in Wireless Sensor Networks Using Similarity Estimation with Group Deployment Knowledge" Sensors 17, no. 1: 160. https://doi.org/10.3390/s17010160