The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study
Open accessAbstract
The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these systems has become a necessity. These cyber-physical systems improve the monitoring and control features of these systems but also introduce several security challenges. Intrusion detection is a convenient second line of defence in case of the failure of normal network security protocols. Anomaly detection is a branch of intrusion detection that is resource friendly and provides broader detection generality making it ideal for IWSN applications. These schemes can be used to detect abnormal changes in the environment where IWSNs are deployed. This paper presents a literature survey of the work done in the field in recent years focusing primarily on machine learning techniques. Major research gaps regarding the practical feasibility of these schemes are also identified from surveyed work and critical water infrastructure is discussed as a use case.
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Cites
Attacking Fieldbus Communications in ICS: Applications to the SWaT Testbed
David, U., Jairo, G., Ole, T. N., & Alvaro, C. (2016) · Cryptology and Information Security Series · doi:10.3233/978-1-61499-617-0-75 · Cited by 63
Cited by
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
A Unifying Review of Deep and Shallow Anomaly Detection
Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 914
CNN-Based Network Intrusion Detection against Denial-of-Service Attacks
Kim, J., Kim, J., Kim, H., Shim, M., & Choi, E. (2020) · Electronics · doi:10.3390/electronics9060916 · Cited by 420
Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects
Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019) · Sensors · doi:10.3390/s20010109 · Cited by 365
Cybersecurity of industrial cyber-physical systems: A review
Kayan, H., Nunes, M., Rana, O., Burnap, P., & Perera, C. (2022) · ACM Computing Surveys · doi:10.1145/3510410 · Cited by 261
Metadata
- Authors
- Ramotsoela, D., Abu-Mahfouz, A., & Hancke, G.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-08-01
- Volume
- 18
- Issue
- 8
- Pages
- 2491
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2885316547
- Cited by
- 158
- References
- 1
- FWCI
- 11.05
- Type
- journal article
- Language
- en
- Open access
- gold
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