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An Adaptive Trust Evaluation Model for Detecting Abnormal Nodes in Underwater Acoustic Sensor Networks.
Changtao Liu1,2, Jun Ye1,2, Fanglin An1,2
1School of Cyberspace Security, Hainan University, Haikou 570228, China.
Sensors (Basel, Switzerland)
|May 11, 2024
Summary
This study introduces an adaptive fuzzy logic model to enhance security in underwater acoustic sensor networks. The model improves abnormal node detection, especially in unstable conditions, by refining trust evaluation accuracy.
Area of Science:
- Computer Science
- Network Security
- Signal Processing
Background:
- Underwater acoustic sensor networks (UASNs) face significant security challenges due to the dynamic environment.
- Existing trust mechanisms require enhancement for accurate abnormal node detection.
Purpose of the Study:
- To propose an adaptive trust evaluation model for UASNs using fuzzy logic.
- To improve the accuracy of trust evaluation and the detection rate of abnormal nodes.
Main Methods:
- Developed a variable weight fuzzy comprehensive evaluation algorithm for direct trust.
- Implemented fuzzy closeness to filter unreliable recommendation trust.
- Adjusted recommendation trust weights based on deviation for indirect trust.
Main Results:
- The proposed model significantly enhances trust evaluation accuracy.
- Demonstrated a notable increase in the detection rate of abnormal nodes.
- Achieved over 10% improvement in detecting abnormal nodes under unstable link quality.
Conclusions:
- The adaptive fuzzy logic model effectively addresses security vulnerabilities in UASNs.
- The model offers superior performance in identifying malicious or faulty nodes compared to existing methods.
