The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
DÏoT: A Federated Self-learning Anomaly Detection System for IoT
Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00080 · Cited by 663
Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT
Mills, J., Hu, J., & Min, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2956615 · Cited by 436
Internet of Things Intrusion Detection: Centralized, On-Device, or Federated Learning?
Rahman, S. A., Tout, H., Talhi, C., & Mourad, A. (2020) · IEEE Network · doi:10.1109/mnet.011.2000286 · Cited by 414
Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018) · Applied Sciences · doi:10.3390/app8122663 · Cited by 323
MQTTset, a New Dataset for Machine Learning Techniques on MQTT
Vaccari, I., Chiola, G., Aiello, M., Mongelli, M., & Cambiaso, E. (2020) · Sensors · doi:10.3390/s20226578 · Cited by 292
Rodríguez-Barroso, N., Stipcich, G., Jiménez-López, D., Ruiz-Millán, J. A., Martínez-Cámara, E., González-Seco, G., Luzón, M. V., Veganzones, M. A., & Herrera, F. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.07.009 · Cited by 148
Cited by
A review of deep learning techniques used in agriculture
Attri, I., Awasthi, L. K., Sharma, T. P., & Rathee, P. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102217 · Cited by 336
Ferrag, M. A., Ndhlovu, M., Tihanyi, N., Cordeiro, L. C., Debbah, M., Lestable, T., & Thandi, N. S. (2024) · IEEE Access · doi:10.1109/access.2024.3363469 · Cited by 312
Self-citationA Systematic Review of Data-Driven Attack Detection Trends in IoT
Haque, S., El-Moussa, F., Komninos, N., & Muttukrishnan, R. (2023) · Sensors · doi:10.3390/s23167191 · Cited by 38
Metadata
- Authors
- Friha, O., Ferrag, M. A., Shu, L., Maglaras, L., Choo, K.-K. R., & Nafaa, M.
- Venue
- Journal of Parallel and Distributed Computing
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 165
- Pages
- 17-31
- ISSN
- 0743-7315 (ISSN-L)
- OpenAlex
- W4220886040
- Cited by
- 314
- References
- 9
- FWCI
- 29.38
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things. https://app.cochat.ai/papers/10.1016/j.jpdc.2022.03.003
- The paper
- Friha, O., Ferrag, M. A., Shu, L., Maglaras, L., Choo, K.-K. R., & Nafaa, M. (2022). FELIDS: Federated learning-based intrusion detection system for agricultural Internet of Things. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2022.03.003
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
