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Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis
Open accessAbstract
In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This paper surveys federated deep learning for IoT cyber security and adds an experimental comparison of centralized vs. federated DNN, CNN, and RNN intrusion detection models on three IoT traffic datasets. It concludes that federated learning improves privacy and can approach or outperform centralized learning, though its own reported centralized accuracies are often higher than the federated global-model results.
Key findings
- The review covers federated learning-based security and privacy systems across Industrial IoT, edge computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, cloud computing, 5G-enabled IoT, mobile crowdsensing, and cyber-physical systems; it also discusses federated learning with blockchain and federated intrusion/malware detection.
- In centralized experiments, the highest accuracies were RNN on Bot-IoT at 96.76%, DNN on MQTTset at 90.06%, and RNN on TON_IoT at 99.98%; the lowest were DNN on Bot-IoT at 95.76%, RNN on MQTTset at 89.29%, and CNN on TON_IoT at 98.87%.
- In federated experiments, IID global models approximated centralized performance; Non-IID models struggled initially but after 50 rounds approached centralized performance. For Bot-IoT with CNN and K=15, the worst client had 01.00% accuracy at the first round, 52.98% at round 50, while the global model achieved 90.35%.
- The paper states that federated deep learning approaches outperform classic/centralized machine learning in assuring IoT data privacy and provide higher accuracy in detecting attacks, although the experimental comparison text reports that federated global models “managed to approach” centralized performance.
Methods
- Design: comprehensive survey plus experimental analysis training three deep learning IDS models—DNN, CNN, and RNN—under centralized and federated learning settings.
- Data: three real IoT traffic datasets—Bot-IoT (69.3 GB PCAP, over 72 million records; CSV files used), MQTTset, and TON_IoT—with preprocessing including dropping selected features, one-hot encoding of `proto`, and Z-score normalization.
- Federated setup and analysis: Sherpa.AI framework on Google Colaboratory with NumPy, Pandas, TensorFlow, and Keras; client distributions K=5, K=10, and K=15; IID and Non-IID data distributions; 50 federated rounds; metrics include accuracy, precision, recall, F1-score, and ROC curves.
Limitations
- Paper-stated open challenges include: privacy can still be revealed by analyzing the global model; differential privacy can degrade performance or require high computing power; IoT network constraints such as bandwidth, interference, noise, and limited reliability; data bias and unequal data sizes across devices; and lack of FL-specific IoT frameworks.
- Evident from the text: the experiments use pre-existing public datasets with engineered partitions to mimic federations because of a lack of FL-specific datasets; experiments were run on Google Colab; source code is available only upon request; and the conclusion that federated learning provides higher attack-detection accuracy is not fully supported by the paper’s own centralized-vs-federated accuracy results, which show federated global models approaching rather than consistently exceeding centralized models.
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In the paper
“In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > Abstract
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In the paper
“In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > Abstract
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In the paper
“Almost all of the surveys on federated learning for IoT applications present security and privacy countermeasures without focusing on an experimental analysis.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > B. RELATED SURVEYS AND OUR CONTRIBUTIONS
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In the paper
“All these related surveys did not cover the application of federated deep learning for cyber security in IoT applications with focusing on experimental analysis.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > B. RELATED SURVEYS AND OUR CONTRIBUTIONS
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In the paper
“For the IID data distribution strategy, the federated deep learning global models were able to approximate the performance of the centralized learning models.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 2) FEDERATED LEARNING MODELS
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In the paper
“Fig 15 presents the ROC curves for five classes in the MQTTset dataset, namely: class Bruteforce, class DoS, class Flood, class Benign, and class Slowite.All values are between 0.94 and 0.98.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > D. EXPERIMENTAL RESULTS
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In the paper
“The second observation is that at the 50 th rounds of federated deep learning, the performance of all global models managed to approach the performance of the centralized model.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 2) FEDERATED LEARNING MODELS
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In the paper
“Fig 14 presents the Receiver Operating Characteristic (ROC) curves for five classes in the BoT-IoT dataset, namely: class DDoS, class DoS, class Benign, class Reconnaissance, and class Theft.All values are between 0.99 and 1.00.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > D. EXPERIMENTAL RESULTS
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In the paper
“The first observation is that in the IID data distribution strategy, the Best, Worst, and Global models are closely related to each other consistently across all settings and datasets, even though the clients are trained from different class samples.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 2) FEDERATED LEARNING MODELS
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In the paper
“In the Non-IID case, at the first FL round, the Best, Worst, and Global models are nowhere near one another, and this is quite expected since not all clients were trained from all classes.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 2) FEDERATED LEARNING MODELS
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- It may state two separate claims as one. 20
- Probably not a substantial claim. 8
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In the paper
“For the non-IDI data distribution strategy, the global models struggled a bit to perform the same as in IID, which is quite normal since the data samples were randomly distributed for all clients, however after 50 FL runs, the overall performance was pretty good.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 2) FEDERATED LEARNING MODELS
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In the paper
“The highest accuracy for the TON_IoT dataset was obtained using the RNN classifier which achieved 99.98%, while the lowest accuracy was obtained using the CNN classifier with 98.87%.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > D. EXPERIMENTAL RESULTS
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In the paper
“The highest accuracy for the Bot-IoT dataset was obtained using the RNN classifier which achieved 96.76%, while the lowest accuracy was obtained using the DNN classifier with 95.76%.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > D. EXPERIMENTAL RESULTS
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In the paper
“The simulation results show that the proposed CrowdSFL protocol can resist the following malicious behaviors: Malicious miners, Malicious workers, and Malicious requesters.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > B. PERMISSIONLESS BLOCKCHAIN-BASED SOLUTIONS
What the sources say
- Not checked yetcites [100] source
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In the paper
“Compared to the conventional FedAvg approach [83], the proposed system can reduce significantly the computation time of the users as well as the latency of the system.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > I. SECURE INTERNET OF VEHICLES
What the sources say
- Not checked yetcites [82] Zhang, 2021, FedSky: An efficient and privacy-preserving scheme for federated mobile crowdsensing, IEEE Internet Things J.
- Not checked yetcites [83] McMahan, 2016, Communication-efficient learning of deep networks from decentralized data, arXiv:1602.05629
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In the paper
“Accordingly, many existing systems already contain vulnerable IoT devices that are open to being compromised, which is furthermore harmful in sensitive tasks such as surveillance, as shown by the work of Ciuonzo et al. [59], which focused on the issue of distributed detection of a non-cooperative object in a wireless sensor network.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > A. DETECTING COMPROMISED IoT DEVICES
What the sources say
- Not checked yetcites Ciuonzo et al. [59] source
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- Rests on an assumption: Many IoT devices are susceptible to attack through unsafe design, deployment, and configurations.
In the paper
“the results show that the FedAGRU system provides less communication overhead with higher detection accuracy.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > VOLUME 9, 2021
What the sources say
- Not checked yetcites [96] source
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In the paper
“In the absence of a credible security defense systems implemented on the IoT devices, they can be attacked by hackers [2] and are representing a large attack surface that is actively exploited.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > I. INTRODUCTION
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- Not checked yetcites [2] source
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In the paper
“The federated learning achieves great success and is widely used in many fields, e.g., mobile edge network optimization [18], Google keyboard query suggestions and prediction [19], [20], COVID-19 detection [21]- [23], vehicles communications [24], Internet of Drones [25], Augmented reality [26], Intrusion detection [27]- [29] etc.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > I. INTRODUCTION
What the sources say
- Not checked yetcites [18] source
- Not checked yetcites [19] 2021
- Not checked yetcites [20] Hard, 2018, Federated learning for mobile keyboard prediction, arXiv:1811.03604
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In the paper
“The simulation results on two real-world data sets (i.e., Reuters dataset and 20 newsgroups dataset) show that the proposed system can guarantee the quality of shared data as well as differential privacy.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > A. PERMISSIONED BLOCKCHAIN-BASED SOLUTIONS
What the sources say
- Not checked yetcites [106] source
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In the paper
“The performance evaluation under the gas pipeline system dataset show the following results: F-score = 98.14 %, recall = 97.47 %, precision = 98.85 %, accuracy = 99.20 %, which are better compared to three related works [57], [95], and [77]”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > A. FEDERATED LEARNING-BASED ANOMALY DETECTION
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In the paper
“Although some techniques have been used to overcome this problem, including differential privacy [166], these approaches degrade model performance or require additional conditions that are not suitable for IoT networks, especially high computing power [167].”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > B. OPEN CHALLENGES AND CONSIDERATIONS 1) SECURITY AND PRIVACY CHALLENGES
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In the paper
“They are chosen specifically because they are build from heterogeneous data sources as well as collected from IoT and IIoT sensor telemetry datasets.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > B. DATASETS DESCRIPTION AND PRE-PROCESSING
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In the paper
“This combination reduces the threat of data leakage and enables data owners to have more control over the access to stored and shared data.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 3) WHEN FEDERATED LEARNING MEETS BLOCKCHAIN
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In the paper
“The results demonstrate that federated deep learning approaches can outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provides the highest accuracy in detecting attacks.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > VIII. CONCLUSION
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In the paper
“The performance evaluation on two real-world datasets, namely, MovieLens and Netflix, demonstrates that the proposed idea can not only identify shilling hackers but also improve the performance of recommendations significantly.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > G. SHILLING ATTACK
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- Not checked yetcites [131] source
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In the paper
“The study shows that the federated deep learning-based security and privacy systems can be applied for several types of IoT applications, including, Industrial Internet of Things, Edge Computing, Internet of Drones, Internet of Healthcare Things, Cloud Computing, 5G-enabled IoT, Internet of Vehicles, Mobile Crowdsensing, etc.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > A. IMPORTANCE OF THE STUDY 1) IoT APPLICATIONS
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In the paper
“First, and most importantly, privacy issues, since it requires data to be collected at a single entity, thus making it easier for an attacker to target a single location for all data, if that single entity is compromised, all sensitive data will be breached.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 3) COMPARISON
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In the paper
“In addition, communication channels and computational power are constrained in terms of capacity, as well as the presence of various network issues such as bandwidth, interference, and noise [167].”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 2) IoT NETWORK SETTINGS CHALLENGES
What the sources say
- Not checked yetcites [167] source
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In the paper
“This dataset is introduced by the IoT Lab of the UNSW Canberra Cyber, the School of Engineering and Information technology (SEIT), UNSW Canberra at the Australian Defence Force Academy (ADFA) [150] for the collection and analysis of mixed data sources from IoT and Industrial IoT (IIoT).”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 3) TON_IoT DATASET
What the sources say
- Not checked yetcites [150] source
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In the paper
“Federated learning-based intrusion detection systems, on the other hand, significantly decrease the previous issues with decent detection accuracy, and in many cases, it approached the performance of a centralized approach as we showed with our federated deep learning models.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 3) COMPARISON
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In the paper
“Throughout the performance evaluation on a SCADA dataset, the results show that the proposed system can classify with a binary cross-entropy loss score of 0.47 and an accuracy of 95.55 %.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > E. ADVERSARIAL ATTACK
What the sources say
- Not checked yetcites [122] source
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In the paper
“The BoT-IoT dataset was produced at the Cyber Range Lab at UNSW Canberra as a result of building a real-life network environment integrating a mix of normal and botnet traffic [154], [160]- [164].”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > B. DATASETS DESCRIPTION AND PRE-PROCESSING
What the sources say
- Not checked yetcites [154] source
- Not checked yetcites [160]-[164]
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In the paper
“For example, if IoT devices have varying data sizes, the FL-based system may give more importance to the contributions of the populations.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > 3) DATA-RELATED CHALLENGES
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In the paper
“2) MQTTset DATASET introduced by Vaccari et al. [156] to address the lack of support for specific protocols that IoT environments are currently using.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > B. DATASETS DESCRIPTION AND PRE-PROCESSING
What the sources say
- Not checked yetcites [156] source
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“However, due to the lack of FL-specific datasets, any pre-existing public dataset with engineered partitions can be used to mimic data federations, as employed in our experiment.”
§ Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis > B. DATASETS DESCRIPTION AND PRE-PROCESSING
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The paper relies on this without support.
Many cyber security researchers have difficulty in finding the best learning type (centralized or federated) to test and evaluate their proposed security methods in IoT applications.
“Therefore, many cyber security researchers have difficulty in finding the best learning type (i.e., centralized or federated learning) to test and evaluate their proposed security methods in IoT applications, and selecting an appropriate federated deep learning method is an essential issue in this field.”
The paper relies on this without support.
Selecting an appropriate federated deep learning method is an essential issue in this field.
“Therefore, many cyber security researchers have difficulty in finding the best learning type (i.e., centralized or federated learning) to test and evaluate their proposed security methods in IoT applications, and selecting an appropriate federated deep learning method is an essential issue in this field.”
The paper relies on this without support.
Traditional centralized learning has issues such as privacy, latency, bandwidth, and connectivity.
“However, there are some issues for traditional centralized learning such as privacy, latency, bandwidth, and connectivity.”
The paper relies on this without support.
Many IoT devices are susceptible to attack through unsafe design, deployment, and configurations.
“Many of those devices, however, are susceptible to attack through unsafe design, deployment, and configurations.”
The paper relies on this without support.
Federated learning can suffer from machine learning attacks against the locally created models.
“Since it is trained on sensitive user data, federated learning can suffer from machine learning attacks against the locally created models.”
The paper relies on this without support.
FL-based tasks require the data distribution to be Non-IID and unbalanced, reflecting the properties of the real-world scenario.
“FL-based tasks require the data distribution to be Non-Independent and Identically Distributed (Non-IID) and unbalanced, which reflects the properties of the real-world scenario.”
The paper relies on this without support.
Adversaries can use cyber security attacks such as adversarial attacks or poisoning attacks to degrade the accuracy of a machine learning model or deduce if an IoT device has been involved in some mission from their local model updates.
“These adversaries can use cyber security attacks such as adversarial attacks or poisoning attacks to degrading the accuracy of a machine learning model or deduce if an IoT device has been involved in some mission from their local model updates.”
Claims other papers make about this one
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In the citing paper
“Furthermore, the communication cost is reduced by uploading only gradients rather than larger datasets [48].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [48]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
FedAGRU is attributed to different cited authors
MinorOne passage attributes the proposed FedAGRU system to Cvitic et al. [29] [88], while an earlier passage says Cetin et al. [96] proposed FedAGRU.
“Based on a boosting method of logistic model trees, Cvitic et al. [29] [88] proposed a federated learning-based intrusion detection system, named FedAGRU, which employs gated recurrent units (GRUs) models.”
“Cetin et al. [96] proposed an IDS, named FedAGRU, which is based on federated learning.”
Dataset citations do not match the numbered references
MinorThe dataset discussion cites [149] for TON_IoT, but reference 149 is titled "LEAF: A benchmark for federated settings"; the TON_IoT reference is numbered 150. The same one-number mismatch affects the cited Bot-IoT and MQTTset references.
“For evaluating FL-based cyber security solutions in IoT networks, the security research community uses the following three datasets: TON_IoT [149], Bot-IoT [154], and MQTTset [156].”
“149. S. Caldas, S. M. K. Duddu, P. Wu, T. Li, J. Konečnỳ, H. B. McMahan, V. Smith, and A. Talwalkar, ''LEAF: A benchmark for federated settings,'' 2018, arXiv:1812.01097.”
TON_IoT figure lists six classes after stating five
MinorThe discussion says Fig 16 presents ROC curves for five TON_IoT classes, but the figure caption enumerates classes 0 through 5, including XSS as a sixth class.
“Fig 16 presents the ROC curves for five classes in the TON_IoT dataset, namely: class Backdoor, class Injection, class Benign, class Password, and class Scanning.”
“Figure: 16 FIGURE 16 . FIGURE 16.The Receiver Operating Characteristic (ROC) curves for TON_IOT dataset.(class 0: Backdoor, class 1: Injection, class 2: Benign, class 3: Password, class 4: Scanning, class 5: XSS.)”
Research questions are attributed to the learning-performance figure
MinorThe text points to Fig 17 for the research questions, but the caption identifies Fig 17 as learning performances.
“In this context, we highlight the following research questions (i.e., Fig 17 that need to be solved:”
“Figure: 17 FIGURE 17 . FIGURE 17. Learning performances.”
MQTTset figure lists six classes after stating five
MinorThe discussion says Fig 15 presents ROC curves for five MQTTset classes, but the figure caption enumerates classes 0 through 5, including Malformed as a sixth class.
“Fig 15 presents the ROC curves for five classes in the MQTTset dataset, namely: class Bruteforce, class DoS, class Flood, class Benign, and class Slowite.”
“Figure: 15 FIGURE 15 . FIGURE 15.The Receiver Operating Characteristic (ROC) curves for MQTTset datasets.(class 0: Bruteforce, class 1: DoS, class 2: Flood, class 3: Benign, class 4: Slowite, class 5: Malformed.)”
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Metadata
- Authors
- Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L.
- Affiliations
- Edith Cowan University (AU)
- Badji Mokhtar-Annaba University (DZ)
- De Montfort University (GB)
- Nanjing Agricultural University (CN)
- Corresponding author
- Ferrag, Mohamed Amine
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 138509-138542
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3203932036
- Semantic Scholar
- 82f305d3cb1a82fb6d4a5fcabb4fda40cb2b05e0
- Cited by
- 342
- References
- 174
- Citations by year
- 2026: 53 · 2025: 94 · 2024: 80 · 2023: 70 · 2022: 44 · 2021: 1
- FWCI
- 17.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DMU Open Research Archive (De Montfort University) (submittedVersion, cc-by)
- Australasian Journal of Paramedicine (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion, other-oa)
- Greater South Information System
- Funders
- Cyber Security Cooperative Research Centre (CSCRC) and Edith Cowan University, Australia
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- federated deep learning, Internet of Things security, IoT privacy, malware detection, intrusion detection, blockchain, Industrial Internet of Things, edge computing, Internet of Drones, Internet of Medical Things, Internet of Vehicles, Bot-IoT dataset, ToN-IoT, recurrent neural networks, convolutional neural networks, deep neural network
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.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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 ›
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