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Cited by 378Anomaly Detection Techniques and ApplicationsFault Detection and Control SystemsDistributed Sensor Networks and Detection Algorithms

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Smart anomaly detection in sensor systems: A multi-perspective review

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Erhan, L., Ndubuaku, M., Di Mauro, M., Song, W., Chen, M., Fortino, G., Bagdasar, O., Liotta, A.·2021·Information Fusion
DOI 10.1016/j.inffus.2020.10.001
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  • Most existing research and industrial practice in railway cybersecurity focus on incident response, supply chain security, or high-level governance models, with less attention given to sensor-level risk mechanisms that underpin system reliability.

    “However, most existing research and industrial practice in railway cybersecurity focus on incident response, supply chain security, or high-level governance models, with less attention given to the sensor-level risk mechanisms that underpin system reliability [11,12,13].”

    From Sensor-Based Cyber Risk Management in Railway Infrastructure Under the NIS2 Directive (2025) · cites this paper as [13]

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Metadata

Authors
Erhan, L., Ndubuaku, M., Di Mauro, M., Song, W., Chen, M., Fortino, G., Bagdasar, O., & Liotta, A.
Affiliations
  • University of Derby (GB)
  • Shanghai Ocean University (CN)
  • Huazhong University of Science and Technology (CN)
  • University of Calabria (IT)
  • Free University of Bozen-Bolzano (IT)
Corresponding author
Erhan, L.
ORCID
Venue
Information Fusion
Publisher
Elsevier BV
Published
2021-03
Volume
67
Pages
64-79
ISSN
1566-2535 (ISSN-L)
DOI
10.1016/j.inffus.2020.10.001
OpenAlex
W3094345499
Cited by
378
References
4
Citations by year
2026: 44 · 2025: 104 · 2024: 88 · 2023: 71 · 2022: 47 · 2021: 22
FWCI
11.99
Type
journal article
Language
en
Open access
Licence
Locations
  • Information Fusion (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Open Research (University of Surrey) (submittedVersion, cc-by)
  • University of Derby Online Research Archive. (University of Derby) (submittedVersion)
  • Archive of Doctoral Theses and Digital Collections (University of Calabria) (submittedVersion)
  • Open Research (University of Surrey) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Sensor Networks and Detection Algorithms
Keywords
anomaly detection, sensor systems, smart sensing, energy efficiency, cloud computing, fog computing, edge computing, deep learning, reinforcement learning, supervised learning, time series analysis, statistical analysis, signal processing, constrained environments

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