A Lightweight Edge-CNN-Transformer Model for Detecting Coordinated Cyber and Digital Twin Attacks in Cooperative Smart Farming

Deepti Gupta; Lopamudra Praharaj; Maanak Gupta

REVIEW 4 major objections 6 minor 36 references

A Lightweight Edge-CNN-Transformer Model for Detecting Coordinated Cyber and Digital Twin Attacks in Cooperative Smart Farming

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A quantized CNN-Transformer model detects coordinated cyber and digital twin attacks in cooperative smart farming networks, reaching 97% accuracy while using roughly 90% less memory than the uncompressed model.

desk verdict A useful testbed and two new smart-farming datasets, but the paper's central comparison is undercut by its own contradictory numbers, so the results as stated cannot be trusted. read the letter →

arxiv 2411.14729 v1 pith:E6ZFNL54 submitted 2024-11-22 cs.CR

Lopamudra Praharaj , Deepti Gupta , Maanak Gupta This is my paper · ORCID

classification cs.CR

keywords cooperativesmartfarmingdigitaltwinsecuritynetworkanomalydetectionCNN-Transformerpost-trainingquantizationedgecomputingIoTcyberattacks

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a lightweight, quantized CNN-Transformer can be deployed at the edge to detect network attacks on cooperative smart farms, including attacks that corrupt digital twins. The authors build a two-farm testbed, run eight types of network attacks across physical and digital twin layers, and capture two WiFi traffic datasets. On those datasets, the model with five transformer encoder layers reaches 97% accuracy, and post-training quantization cuts memory by about 90% with only a 1-2% accuracy drop. The central claim is that this compressed model outperforms traditional machine learning baselines while remaining small enough for edge devices.

What carries the argument

The central object is a hybrid CNN-Transformer architecture: a 1D CNN with batch normalization extracts local spatial patterns from network flow features, and a transformer encoder with multi-head self-attention and layer normalization captures long-range dependencies in the sequence. A global average pooling layer, a fully connected layer, and softmax perform the final attack classification. The load-bearing mechanism is the combination of local feature extraction by the CNN with the transformer's self-attention over distant time steps or features, which the paper argues is what makes the model effective at network anomaly detection. Post-training quantization to 8-bit integers is the mechanism that makes the model edge-deployable by shrinking memory usage roughly tenfold.

What would settle it

Capture WiFi traffic while an attacker directly corrupts the Azure Digital Twin model state through its API rather than injecting out-of-distribution sensor values, and run the trained model on that traffic; if no anomaly is flagged, the assumption that DT corruption changes network traffic is false.

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Extended reading notes

Core claim

On the paper's own terms, the core discovery is that a hybrid CNN-Transformer network anomaly detector, trained on WiFi traffic features from a cooperative smart farming testbed, can distinguish benign traffic from coordinated cyber and digital twin attacks with up to 97% accuracy. The model's detection ability improves as the number of transformer encoder layers increases from one to five, at the cost of greater memory use. Post-training quantization converts the weights from 32-bit floats to 8-bit integers, reducing memory from 4081.62 KB to 383.76 KB in the reported one-encoder case, while lowering accuracy by only about 1-2%. The compressed model is then shown to beat Random Forest, Logistic Regression, CNN, LSTM, and CNN-LSTM baselines in both accuracy and F1 score on the two self-generated datasets.

Load-bearing premise

The load-bearing premise is that a compromised digital twin's corruption shows up in the WiFi network traffic captured on the testbed; if a real digital-twin attack leaves network traffic unchanged, the detector cannot see it.

Editorial extensions

If this is right

If the accuracy results are correct, each farm's edge server can run the detector and isolate a compromised farm before corrupted sensor data reaches the cooperative cloud.
Increasing transformer encoder layers from 1 to 5 improves attack detection on both datasets, suggesting model depth is a useful lever even in edge settings once quantization offsets the memory cost.
The 1-2% accuracy loss under 8-bit quantization is small relative to the roughly 90% memory reduction, so the compressed model is the version suitable for edge deployment.
On the paper's two datasets, the compressed model outperforms RF, LR, CNN, LSTM, and CNN-LSTM in accuracy and F1 score, indicating the hybrid architecture retains an advantage after compression.
The two generated datasets and the eight attack types provide a reusable testbed for future cooperative smart farming security research.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper: the detector only sees network traffic, so its ability to catch digital twin corruption depends on the attack changing WiFi traffic patterns; a DT attack that tampers with the virtual model internally without generating network chatter would be invisible to this approach.
Beyond the paper: training a single detector on traffic from many farms with different sensor configurations would require domain adaptation or federated learning, since the current model is trained per farm on farm-specific features.
Beyond the paper: the same architecture and quantization pipeline could be tested on public IoT or agricultural intrusion datasets to determine whether the 97% accuracy reflects the model's general capability or the specific testbed traffic.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The paper builds a real testbed for cooperative smart farming, collects two new WiFi network datasets under a sensible set of attacks (deauth, SYN flood, ARP poisoning, DNS spoofing, evil twin, SQL injection, port scanning), and applies a standard CNN-Transformer with post-quantization for edge deployment. That is real work, and for a community drowning in pure simulation, the testbed and dataset collection are the most valuable part of the paper.

The problems start with the central empirical claim. In Section VII.D the authors write that the compressed CNN-Transformer 'is higher than that of the CNN-LSTM model, with 90% compared to 92%.' Those numbers say the opposite. Since the conclusion that the compressed model 'outperformed them all' rests on exactly this comparison, the conclusion is not supported by the reported data. No error bars, no repeated runs, no statistical tests anywhere else either; the figures plot single trajectories. The two datasets are not released, and no code is provided, so the main positive artifact—the datasets—cannot be used or checked.

The title and abstract promise detection of coordinated cyber and digital twin attacks, but the model only sees network traffic. The DT attack is simulated by injecting out-of-distribution values into the Azure DT, and no evidence shows those attacks alter the WiFi traffic that the model classifies. In fact, the future-work section says 'including DT datasets,' which reads as an admission that DT-specific data is not in the current evaluation.

There is also a small citation slip: reference [35] is titled 'AWS Managed Grafana' but points to the Azure Digital Twins overview. Minor, but symptomatic of the hurried write-up.

Who gets value from this? Readers working on IoT/agricultural testbeds and attack implementation might mine the setup section. As a scientific claim about a lightweight detector, the paper is not currently defensible. The reported numbers contradict the headline result, and the empirical protocol is single-run and opaque. I would not cite it in its current form, and I would not use the numbers in a comparison. If the authors correct the comparison, release the datasets and code, and add uncertainty quantification, the testbed alone could support a useful paper. As it stands, it deserves a serious referee only because the testbed and dataset collection are genuine contributions that could be salvaged.

Referee Report

4 major / 6 minor

Summary. The paper proposes a secure architecture for Cooperative Smart Farming (CSF) with edge-based digital twins, builds a two-farm testbed, collects two WiFi network datasets under normal and attack conditions, and develops a CNN-Transformer anomaly detector that is compressed with post-training quantization. The authors report accuracy and F1-score comparisons against Random Forest, Logistic Regression, CNN, LSTM, and CNN-LSTM, and claim that the compressed CNN-Transformer outperforms these baselines and that detection accuracy improves as the number of encoder layers increases.

Significance. If the empirical claims held, the paper would demonstrate a lightweight, edge-deployable network anomaly detector tailored to cooperative smart farming, with a concrete testbed and two new datasets. The testbed construction, the use of Azure Digital Twin at the edge, and the explicit reporting of model memory before and after compression are useful contributions. However, the central comparative claim is currently undermined by an internal contradiction in the reported accuracy numbers and by the absence of any uncertainty quantification, and the external validity of the private, self-generated datasets is not established. These issues must be resolved before the claimed superiority over baselines can be accepted.

major comments (4)

[Section VII.D, Figs. 9-10, Section VIII] The manuscript states in Section VII.D: 'For Smart Farm A, the accuracy of the compressed CNN-Transformer model is higher than that of the CNN-LSTM model, with 90% compared to 92%.' Since 90% is lower than 92%, this sentence contradicts itself, and the conclusion in Section VIII that the compressed CNN-Transformer 'outperformed them in both accuracy and F1 score' is not supported by the reported numbers as written. Please correct the values, the figure labels, or the conclusion, and provide the F1 scores for both farms in a table so the comparison can be verified.
[Section VII.C and VII.D] All accuracy and F1 results are single-run point estimates presented without error bars, confidence intervals, or statistical significance tests. The claims that the compressed model 'outperforms' CNN-LSTM, that accuracy 'slightly improved' or 'gradually declined' with epochs, and that more encoder layers improve detection are not statistically grounded. Please run multiple trials with different random seeds and report means and standard deviations, or use appropriate significance tests, for the comparisons in Figures 6, 7, 9, and 10.
[Section V.B and V.C] The digital twin attack is implemented by a Python script that generates 'simulated data vastly different from the physical counterpart,' but the manuscript does not show that this corruption is reflected in the WiFi network traffic features used by the CNN-Transformer model. If DT corruption does not alter the captured network traffic, the model cannot detect it. Please provide an analysis of how the DT attack changes the network-traffic features, or evaluate the model directly on DT data, to support the claim that the proposed approach detects digital twin attacks.
[Section VII.C and VIII] The conclusion that 'the model's ability to detect the network attack increases with the increasing number of encoder layers' is based on only 1, 3, and 5 encoder layers, and the reported results are not monotonic: for Smart Farm A with 3 encoder layers, accuracy 'slightly improved to 91% at 30 epochs but gradually declined as the number of epochs increased.' Please report complete results for all encoder-layer counts and epochs, with uncertainty, before making this general claim.

minor comments (6)

[Section VII.B] The sentence 'The training and evaluation are conducted on two edge devices using Google Colab' is contradictory, because Google Colab is a cloud-hosted notebook service, not an edge device. Please clarify which stages ran on the edge hardware and which ran in Colab.
[Section V.C] The dataset split into training, validation, and testing sets is mentioned but no split ratio is given. Please state the exact proportions used.
[Section II.B, reference [14]] Reference [14] is cited for a 'robust intrusion detection system for DDoS attacks in smart agriculture,' but the listed title is about spatial prediction of soil organic carbon, which appears unrelated. Please verify the citation.
[Section VI.A] The phrase 'a normalized layer' should likely be 'a normalization layer' when describing the feed-forward block of the Transformer encoder.
[Reference [35]] Reference [35] is titled 'AWS Managed Grafana' but the URL points to Azure Digital Twins documentation. Please correct the reference title and URL.
[Section VII.C] The phrase 'various performance matrices' should be 'various performance metrics.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the model's performance claims are empirical measurements on held-out test splits, with no fitted constant or self-cited theorem doing load-bearing work.

full rationale

The paper reports an empirical ML study: two self-generated WiFi traffic datasets are preprocessed, split into training/validation/testing sets, and the CNN-Transformer model is trained and then evaluated on held-out test data. Every headline quantity (accuracy, F1, memory size, compression ratio) is a direct measurement, not a derived quantity that reduces to its own inputs. No parameter is fitted to the evaluation metric, and no 'uniqueness theorem' or prior analytical result is invoked to force the model choice. The authors' self-citations, including [26] and [36], appear only in related-work and background discussion; they do not supply any equation, model constraint, or benchmark result that the paper's conclusions depend on. The most notable issue in the paper is an internal inconsistency in Section VII.D, where the text says the compressed CNN-Transformer accuracy is 'higher' than CNN-LSTM 'with 90% compared to 92%' — the stated numbers contradict the claim. That is a factual/correctness error about reported empirical values, not a circularity: the two percentages are independently measured and are not defined in terms of one another. Because the central claims are self-contained empirical observations rather than a derivation chain, the circularity score is 0.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the representativeness of two self-generated datasets and on the assumption that DT attacks appear in network traffic. The model hyperparameters are hand-picked, and no code or data is released, so the experimental evidence cannot be independently checked.

free parameters (5)

Number of encoder layers = 1, 3, 5
Chosen by hand and varied to show performance trend; the paper concludes that 5 layers is best, but no selection criterion is given.
Training epochs = 10 to 50
The model is trained for varying epochs; best results are generally at 50 epochs, but early stopping is not used and the choice affects accuracy.
Learning rate = 0.001
Fixed hyperparameter, stated in Section VII.B, with no justification or sensitivity analysis.
Batch size = 32
Fixed hyperparameter from Section VII.B with no experimentation.
Post-quantization bit width = 8-bit
The compression uses 8-bit integers for weights; the choice of bit width is a design decision affecting the accuracy drop.

assumptions (3)

domain assumption The two datasets generated from the testbed are representative of real cooperative smart farming network traffic.
The entire evaluation rests on Dataset 1 and Dataset 2; if the testbed does not capture real-world conditions, the reported accuracies do not generalize. Section V.C.
domain assumption Digital twin corruption attacks produce observable anomalies in network traffic.
The model detects attacks from network features only, while the DT attack is simulated as data injection; the paper never demonstrates that this attack changes network traffic patterns. Section V.B and VI.
domain assumption Manually parsed Wireshark features contain sufficient signal for attack classification.
Feature extraction is manual and limited to BSSID-filtered frames; no validation of feature completeness is provided. Section V.C.

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Cite this review

Pith. "Pith review of A Lightweight Edge-CNN-Transformer Model for Detecting Coordinated Cyber and Digital Twin Attacks in Cooperative Smart Farming." pith.science (2026). https://pith.science/paper/E6ZFNL54

@misc{pith2026241114729,
  author       = {Pith},
  title        = {Pith review of: A Lightweight Edge-CNN-Transformer Model for Detecting Coordinated Cyber and Digital Twin Attacks in Cooperative Smart Farming},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E6ZFNL54}},
  note         = {Machine review of arXiv:2411.14729}
}

read the original abstract

The agriculture sector is increasingly adopting innovative technologies to meet the growing food demands of the global population. To optimize resource utilization and minimize crop losses, farmers are joining cooperatives to share their data and resources among member farms. However, while farmers benefit from this data sharing and interconnection, it exposes them to cybersecurity threats and privacy concerns. A cyberattack on one farm can have widespread consequences, affecting the targeted farm as well as all member farms within a cooperative. In this research, we address existing gaps by proposing a novel and secure architecture for Cooperative Smart Farming (CSF). First, we highlight the role of edge-based DTs in enhancing the efficiency and resilience of agricultural operations. To validate this, we develop a test environment for CSF, implementing various cyberattacks on both the DTs and their physical counterparts using different attack vectors. We collect two smart farming network datasets to identify potential threats. After identifying these threats, we focus on preventing the transmission of malicious data from compromised farms to the central cloud server. To achieve this, we propose a CNN-Transformer-based network anomaly detection model, specifically designed for deployment at the edge. As a proof of concept, we implement this model and evaluate its performance by varying the number of encoder layers. Additionally, we apply Post-Quantization to compress the model and demonstrate the impact of compression on its performance in edge environments. Finally, we compare the model's performance with traditional machine learning approaches to assess its overall effectiveness.

Figures

Figures reproduced from arXiv: 2411.14729 by the authors.

**Figure 2.** Test Bed Environment for Cooperative Smart Farming [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Attacks in Digital Twin the tools used, associated vulnerabilities, and their impact on other smart farms. B. Executing Attacks on Digital Twin at the Edge There are various possible attacks [8], [12] on DT, where the attacker defeats the DTs’ security by manipulating their behaviour or exploiting the update from the physical system to the DTs to steer the CSF into an insecure state. The model corruption technique i… view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Proposed Network Anomaly Detection Architecture for Cooperative Smart Farming [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: CNN-Transformer Model overfitting ( [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: Comparison Accuracy In Smart Farm A when En [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: Comparison Accuracy In Smart Farm B when En [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

**Figure 8.** Figure 8: Comparison of Memory Size in KB of trained CNN [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]

**Figure 10.** Figure 10: Comparison of Accuracy and F1 score of Compressed [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

36 extracted references · 33 canonical work pages

[35]

https://learn.microsoft. com/en-us/azure/digital-twins/overview/

A WS Managed Grafana . [Online]. Available: "https://learn.microsoft. com/en-us/azure/digital-twins/overview/"

work page
[1]

World population growth,

M. Roser et al. , “World population growth,” Our world in data , 2013

work page 2013
[2]

Food security: the challenge of feeding 9 billion people,

H. C. J. Godfray et al., “Food security: the challenge of feeding 9 billion people,” science, vol. 327, no. 5967, pp. 812–818, 2010

work page 2010
[3]

Ontologies and artificial intelligence systems for the cooperative smart farming ecosystem,

S. S. L. Chukkapalli et al., “Ontologies and artificial intelligence systems for the cooperative smart farming ecosystem,” Ieee Access , vol. 8, pp. 164 045–164 064, 2020

work page 2020
[4]

A privacy preserving anomaly detection framework for coop- erative smart farming ecosystem,

——, “A privacy preserving anomaly detection framework for coop- erative smart farming ecosystem,” in 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) . IEEE, 2021, pp. 340–347

work page 2021
[5]

https://edgelabs.ai/blog/ cyber-attacks-on-the-rise-in-the-agriculture-industry/

Cyber Attacks On the Rise in the Agricul- ture Industry . [Online]. Available: "https://edgelabs.ai/blog/ cyber-attacks-on-the-rise-in-the-agriculture-industry/"

work page
[6]

https://www.ien.com/food-beverage/news/22379342/ rise-of-precision-agriculture-exposes-food-system-to-new-threats

Rise of Precision Agriculture Exposes F ood System to New Threats. [On- line]. Available: "https://www.ien.com/food-beverage/news/22379342/ rise-of-precision-agriculture-exposes-food-system-to-new-threats"

work page arXiv
[7]

Agrilora: a digital twin framework for smart agricul- ture

P. Angin et al. , “Agrilora: a digital twin framework for smart agricul- ture.” J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl. , vol. 11, no. 4, pp. 77–96, 2020

work page 2020

Show all 36 references

[8]

A digital twin for smart farming,

R. G. Alves et al. , “A digital twin for smart farming,” in 2019 IEEE Global Humanitarian Technology Conference (GHTC) . IEEE, 2019, pp. 1–4

2019
[9]

Digital twins in agriculture: A state-of- the-art review,

W. Purcell and T. Neubauer, “Digital twins in agriculture: A state-of- the-art review,” Smart Agricultural Technology, vol. 3, p. 100094, 2023

2023
[10]

Edge computing and digital twin based smart manu- facturing,

J. Protner et al. , “Edge computing and digital twin based smart manu- facturing,” IF AC-PapersOnLine, vol. 54, no. 1, pp. 831–836, 2021

2021
[11]

Security attacks and solutions for digital twins,

S. Suhail, R. Jurdak, and R. Hussain, “Security attacks and solutions for digital twins,” arXiv preprint arXiv:2202.12501 , 2022

2022 arXiv
[12]

Digital twin security threats and countermeasures: An introduction,

E. Karaarslan and M. Babiker, “Digital twin security threats and countermeasures: An introduction,” in 2021 International Conference on Information Security and Cryptology (ISCTURKEY) . IEEE, 2021, pp. 7–11

2021
[13]

Network abnormal traffic detection model based on semi-supervised deep reinforcement learning,

S. Dong, Y . Xia, and T. Peng, “Network abnormal traffic detection model based on semi-supervised deep reinforcement learning,” IEEE Transactions on Network and Service Management , vol. 18, no. 4, pp. 4197–4212, 2021

2021
[14]

Spatial prediction of soil organic carbon using machine learning techniques in western iran,

H. Mahmoudzadeh, H. R. Matinfar, R. Taghizadeh-Mehrjardi, and R. Kerry, “Spatial prediction of soil organic carbon using machine learning techniques in western iran,” Geoderma Regional , vol. 21, p. e00260, 2020

2020
[15]

Overview of the transformer-based models for nlp tasks,

A. Gillioz, J. Casas, E. Mugellini, and O. Abou Khaled, “Overview of the transformer-based models for nlp tasks,” in 2020 15th Conference on computer science and information systems (FedCSIS) . IEEE, 2020, pp. 179–183

2020
[16]

Ammus: A survey of transformer-based pretrained models in natural language processing,

K. S. Kalyan, A. Rajasekharan, and S. Sangeetha, “Ammus: A survey of transformer-based pretrained models in natural language processing,” arXiv preprint arXiv:2108.05542 , 2021

2021 arXiv
[17]

Intrusion detection model based on improved transformer,

Y . Liu and L. Wu, “Intrusion detection model based on improved transformer,” Applied Sciences , vol. 13, no. 10, p. 6251, 2023

2023
[18]

Rtids: A robust transformer- based approach for intrusion detection system,

Z. Wu, H. Zhang, P. Wang, and Z. Sun, “Rtids: A robust transformer- based approach for intrusion detection system,” IEEE Access , vol. 10, pp. 64 375–64 387, 2022

2022
[19]

Unified anomaly detection methods on edge device using knowledge distillation and quantization,

S. Jena, A. Pulkit, K. Singh, A. Banerjee, S. Joshi, A. Ganesh, D. Singh, and A. Bhavsar, “Unified anomaly detection methods on edge device using knowledge distillation and quantization,” arXiv preprint arXiv:2407.02968, 2024

2024 arXiv
[20]

Ai model compression for edge devices using optimization techniques,

U. Kulkarni, S. Meena, S. V . Gurlahosur, P. Benagi, A. Kashyap, A. Ansari, and V . Karnam, “Ai model compression for edge devices using optimization techniques,” in Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough: Latest Trends in AI, V olume 2. Spr...

2021
[21]

Qae-ids: Ddos anomaly detection in iot devices using post-quantization training,

B. Sharmila and R. Nagapadma, “Qae-ids: Ddos anomaly detection in iot devices using post-quantization training,” Smart Science , vol. 11, no. 4, pp. 774–789, 2023

2023
[22]

Hierarchi- cal vector quantized transformer for multi-class unsupervised anomaly detection,

R. Lu, Y . Wu, L. Tian, D. Wang, B. Chen, X. Liu, and R. Hu, “Hierarchi- cal vector quantized transformer for multi-class unsupervised anomaly detection,” Advances in Neural Information Processing Systems , vol. 36, pp. 8487–8500, 2023

2023
[23]

A game theoretic analysis for cooperative smart farming,

D. Gupta et al. , “A game theoretic analysis for cooperative smart farming,” in 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020, pp. 2350–2359

2020
[24]

Learner’s dilemma: Iot devices training strategies in collaborative deep learning,

D. Gupta, O. Kayode, S. Bhatt, M. Gupta, and A. S. Tosun, “Learner’s dilemma: Iot devices training strategies in collaborative deep learning,” in 2020 IEEE 6th World F orum on Internet of Things (WF-IoT) . IEEE, 2020, pp. 1–6

2020
[25]

Game theory based privacy preserving approach for collaborative deep learning in iot,

D. Gupta, S. Bhatt, P. Bhatt, M. Gupta, and A. S. Tosun, “Game theory based privacy preserving approach for collaborative deep learning in iot,” in Deep Learning for Security and Privacy Preservation in IoT . Springer, 2022, pp. 127–149

2022
[26]

Hierarchical federated transfer learning and digital twin enhanced secure cooperative smart farming,

L. Praharaj et al. , “Hierarchical federated transfer learning and digital twin enhanced secure cooperative smart farming,” in 2023 IEEE Interna- tional Conference on Big Data (BigData) . IEEE, 2023, pp. 3304–3313

2023
[27]

Deep learning- based intrusion detection for distributed denial of service attack in agriculture 4.0,

M. A. Ferrag, L. Shu, H. Djallel, and K.-K. R. Choo, “Deep learning- based intrusion detection for distributed denial of service attack in agriculture 4.0,” Electronics, vol. 10, no. 11, p. 1257, 2021

2021
[28]

Anomaly detection for internet of things time series data using generative adversarial networks with attention mechanism in smart agriculture,

W. Cheng, T. Ma, X. Wang, and G. Wang, “Anomaly detection for internet of things time series data using generative adversarial networks with attention mechanism in smart agriculture,” Frontiers in Plant Science, vol. 13, p. 890563, 2022

2022
[29]

Experimental review of neural-based approaches for network intrusion management,

M. Di Mauro, G. Galatro, and A. Liotta, “Experimental review of neural-based approaches for network intrusion management,” IEEE Transactions on Network and Service Management , vol. 17, no. 4, pp. 2480–2495, 2020

2020
[30]

Edgesec: Design of an edge layer security service to enhance iot security,

K. Sha et al. , “Edgesec: Design of an edge layer security service to enhance iot security,” in 2017 IEEE 1st International Conference on F og and Edge Computing (ICFEC)

2017
[31]

The extreme edge at the bottom of the internet of things: A review,

J. Portilla et al. , “The extreme edge at the bottom of the internet of things: A review,” IEEE Sensors Journal
[32]

Toward the next generation of digital- ization in agriculture based on digital twin paradigm,

A. Nasirahmadi and O. Hensel, “Toward the next generation of digital- ization in agriculture based on digital twin paradigm,” Sensors
[33]

https://grafana.com/ docs/grafana/latest/setup-grafana/installation//

Install Grafana at the Edge . [Online]. Available: "https://grafana.com/ docs/grafana/latest/setup-grafana/installation//"
[34]

https://aws.amazon.com/ grafana/

A WS Managed Grafana. [Online]. Available: "https://aws.amazon.com/ grafana/"
[36]

Security and privacy in smart farming: Challenges and opportunities,

M. Gupta et al., “Security and privacy in smart farming: Challenges and opportunities,” IEEE access , vol. 8, pp. 34 564–34 584, 2020

2020

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [35]

https://learn.microsoft. com/en-us/azure/digital-twins/overview/

A WS Managed Grafana . [Online]. Available: "https://learn.microsoft. com/en-us/azure/digital-twins/overview/"

work page

[2] [1]

World population growth,

M. Roser et al. , “World population growth,” Our world in data , 2013

work page 2013

[3] [2]

Food security: the challenge of feeding 9 billion people,

H. C. J. Godfray et al., “Food security: the challenge of feeding 9 billion people,” science, vol. 327, no. 5967, pp. 812–818, 2010

work page 2010

[4] [3]

Ontologies and artificial intelligence systems for the cooperative smart farming ecosystem,

S. S. L. Chukkapalli et al., “Ontologies and artificial intelligence systems for the cooperative smart farming ecosystem,” Ieee Access , vol. 8, pp. 164 045–164 064, 2020

work page 2020

[5] [4]

A privacy preserving anomaly detection framework for coop- erative smart farming ecosystem,

——, “A privacy preserving anomaly detection framework for coop- erative smart farming ecosystem,” in 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) . IEEE, 2021, pp. 340–347

work page 2021

[6] [5]

https://edgelabs.ai/blog/ cyber-attacks-on-the-rise-in-the-agriculture-industry/

Cyber Attacks On the Rise in the Agricul- ture Industry . [Online]. Available: "https://edgelabs.ai/blog/ cyber-attacks-on-the-rise-in-the-agriculture-industry/"

work page

[7] [6]

https://www.ien.com/food-beverage/news/22379342/ rise-of-precision-agriculture-exposes-food-system-to-new-threats

Rise of Precision Agriculture Exposes F ood System to New Threats. [On- line]. Available: "https://www.ien.com/food-beverage/news/22379342/ rise-of-precision-agriculture-exposes-food-system-to-new-threats"

work page arXiv

[8] [7]

Agrilora: a digital twin framework for smart agricul- ture

P. Angin et al. , “Agrilora: a digital twin framework for smart agricul- ture.” J. Wirel. Mob. Networks Ubiquitous Comput. Dependable Appl. , vol. 11, no. 4, pp. 77–96, 2020

work page 2020