ArticleSensors (Basel, Switzerland)2024
A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Asset-Aware and Resilient Trust Management Framework for Industrial IoT Edge Networks.Sensors (Basel, Switzerland) · 2026Article
- Anomaly Detection and Segmentation in Measurement Signals on Edge Devices Using Artificial Neural Networks.Sensors (Basel, Switzerland) · 2025Article
- Event-Triggered Secure Control Design Against False Data Injection Attacks via Lyapunov-Based Neural Networks.Sensors (Basel, Switzerland) · 2025Article
- Secure Fusion with Labeled Multi-Bernoulli Filter for Multisensor Multitarget Tracking Against False Data Injection Attacks.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale sensor networks becomes more challenging. In this paper, we propose a framework for the distributed detection of FDIAs in large-scale sensor networks. By extracting the spatiotemporal correlation information from sensor data, the large-scale sensors are categorized into multiple correlation groups. Within each correlation group, an autoregressive integrated moving average (ARIMA) is built to learn the temporal correlation of cross-correlation, and a consistency criterion is established to identify abnormal sensor nodes. The effectiveness of the proposed detection framework is validated based on a real dataset from the U.S. smart grid and simulated under both the simple FDIA and the stealthy FDIA strategies.
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