The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0
Open accessAbstract
Smart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture provides new opportunities for migration from factory agriculture to the future generation, known as Agriculture 4.0. However, since the deployment of thousands of IoT based devices is in an open field, there are many new threats in Agriculture 4.0. Security researchers are involved in this topic to ensure the safety of the system since an adversary can initiate many cyber attacks, such as DDoS attacks to making a service unavailable and then injecting false data to tell us that the agricultural equipment is safe but in reality, it has been theft. In this paper, we propose a deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural networks, and recurrent neural networks. Each model’s performance is studied within two classification types (binary and multiclass) using two new real traffic datasets, namely, CIC-DDoS2019 dataset and TON_IoT dataset, which contain different types of DDoS attacks.
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
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Deep learning in agriculture: A survey
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.016 · Cited by 5,133
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
Intrusion detection system: A comprehensive review
Liao, H.-J., Richard Lin, C.-H., Lin, Y.-C., & Tung, K.-Y. (2013) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2012.09.004 · Cited by 1,630
Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy
Sharafaldin, I., Lashkari, A. H., Hakak, S., & Ghorbani, A. A. (2019) · 2019 International Carnahan Conference on Security Technology (ICCST) · doi:10.1109/ccst.2019.8888419 · Cited by 1,275
Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges
Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., & Yin, B. (2018) · IEEE Access · doi:10.1109/access.2017.2783682 · Cited by 1,135
Self-citationDistributed attack detection scheme using deep learning approach for Internet of Things
Diro, A. A., & Chilamkurti, N. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.043 · Cited by 1,053
Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., & Anwar, A. (2020) · IEEE Access · doi:10.1109/access.2020.3022862 · Cited by 892
From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges
Liu, Y., Ma, X., Shu, L., Hancke, G. P., & Abu-Mahfouz, A. M. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3003910 · Cited by 808
Self-citationFriha, O., Ferrag, M. A., Shu, L., Maglaras, L., & Wang, X. (2021) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2021.1003925 · Cited by 633
Self-citation
Cited by
Enhancing smart farming through the applications of Agriculture 4.0 technologies
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022) · International Journal of Intelligent Networks · doi:10.1016/j.ijin.2022.09.004 · Cited by 573
Exploring Edge TPU for Network Intrusion Detection in IoT
Hosseininoorbin, S., Layeghy, S., Sarhan, M., Jurdak, R., & Portmann, M. (2021) · arXiv · doi:10.48550/arxiv.2103.16295 · Cited by 45
Metadata
- Authors
- Ferrag, M. A., Shu, L., Djallel, H., & Choo, K.-K. R.
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2021-05-25
- Volume
- 10
- Issue
- 11
- Pages
- 1257
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3164987805
- Cited by
- 253
- References
- 49
- FWCI
- 19.78
- Type
- journal article
- Language
- en
- Open access
- gold
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: Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0. https://app.cochat.ai/papers/10.3390/electronics10111257
- The paper
- Ferrag, M. A., Shu, L., Djallel, H., & Choo, K.-K. R. (2021). Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0. Electronics. https://doi.org/10.3390/electronics10111257
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.
