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A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks.

Jiamin Hu1, Xiaofan Yang1, Lu-Xing Yang2

  • 1School of Big Data & Software Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
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This study introduces a new framework for detecting false data injection attacks (FDIAs) in large-scale sensor networks. The method effectively identifies malicious sensor nodes by analyzing spatiotemporal correlations and temporal patterns.

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Cybersecurity

Background:

  • False data injection attacks (FDIAs) pose significant risks in sensor networks by corrupting data and leading to incorrect decisions.
  • The increasing scale of sensor networks amplifies the challenge of detecting these sophisticated attacks.

Purpose of the Study:

  • To propose a novel framework for the distributed detection of FDIAs in large-scale sensor networks.
  • To enhance the security and reliability of sensor network data.

Main Methods:

  • Extracting spatiotemporal correlation information from sensor data to group sensors.
  • Utilizing autoregressive integrated moving average (ARIMA) models to capture temporal correlations within groups.
  • Establishing a consistency criterion to identify anomalous sensor nodes indicative of FDIAs.
Keywords:
detection frameworkdistributed solutionfalse data injection attackslarge-scale sensor networks

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Main Results:

  • The proposed framework effectively categorizes sensors and identifies abnormal nodes.
  • Validation using a U.S. smart grid dataset demonstrated the framework's capability against both simple and stealthy FDIAs.
  • The method shows promise in detecting FDIAs in complex, large-scale environments.

Conclusions:

  • The developed distributed detection framework is effective for identifying FDIAs in large-scale sensor networks.
  • The approach leverages spatiotemporal correlations and time-series analysis for robust anomaly detection.
  • This research contributes to securing critical infrastructure reliant on sensor networks.