ArticleSensors (Basel, Switzerland)2023
Tamper Detection in Industrial Sensors: An Approach Based on Anomaly Detection.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Artificial Intelligence.PeerJ. Computer science · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Industrial Revolution 4.0 has catapulted the integration of advanced technologies in industrial operations, where interconnected systems rely heavily on sensor information. However, this dependency has revealed an essential vulnerability: Sabotaging these sensors can lead to costly and dangerous interruptions in the production chain. To address this threat, we introduce an innovative methodological approach focused on developing an anomaly detection algorithm specifically designed to track manipulations in industrial sensors. Through a series of meticulous tests in an industrial environment, we validate the robustness and accuracy of our proposal. What distinguishes this study is its unique adaptability to various sensor conditions, achieving high detection accuracy and prompt response. Our algorithm demonstrates superiority in accuracy and sensitivity compared to previously established methodologies. Beyond detection, we incorporate a proactive alert and response system, guaranteeing timely action against detected anomalies. This work offers a tangible solution to a growing challenge. It lays the foundation for strengthening security in industrial systems of the digital age, harmonizing efficiency with protection in the Industry 4.0 landscape.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.