Fed-IIoT: A Robust Federated Malware Detection Architecture in Industrial IoT

Generative Adversarial Nets

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Federated learning for malware detection in IoT devices

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A trustworthy security model for IIoT attacks on industrial robots

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Dynamic Weighted Federated Learning: A Scalable and Privacy‐Centric Approach to Android IoT Malware Detection

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Detection of Model Poisson Attack in Federated Learning System Using AAE in IIoT Systems

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Resource Aware Long Short-Term Memory Model (RALSTMM) Based On-Device Incremental Learning for Industrial Internet of Things

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Federated Intrusion Detection on Non-IID Data for IIoT Networks Using Generative Adversarial Networks and Reinforcement Learning

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Federated Learning: Challenges, SoTA, Performance Improvements and Application Domains

Schoinas, Ι., Triantafyllou, A., Ioannidis, D., Tzovaras, D. K., Drosou, A., Votis, K., Λάγκας, Θ., Argyriou, V., & Sarigiannidis, P. G. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3458088 · Cited by 10

Enhanced lion swarm optimization and elliptic curve cryptography scheme for secure cluster head selection and malware detection in IoT-WSN

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BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning

Chimeleze, C. U., Jamil, N., Ismail, R., Lam, K., Teh, J. S., Samual, J., & Okeke, C. A. (2022) · Security and Communication Networks · doi:10.1155/2022/5339926 · Cited by 10

Privacy-Preserving Federated Learning for Pneumonia Diagnosis

Sarkar, S., Agrawal, S., Gadekallu, T. R., Mahmud, M., & Brown, D. J. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-1648-1_29 · Cited by 9

Byzantine-Robust Multimodal Federated Learning Framework for Intelligent Connected Vehicle

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Quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning

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YarowskyDroid: Semi-supervised based Android malware detection using federation learning

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LSTM-Autoencoder-Based Incremental Learning for Industrial Internet of Things

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An Adversarial Attack on Artificial Intelligence Malware Detection in Consumer Internet of Things

Yang, C.-H., Mwangi, M. B., Cheng, S., & Lee, H.-M. (2024) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2024.3482700 · Cited by 8

Federated Learning: Data Privacy and Cyber Security in Edge-Based Machine Learning

White, J., & Legg, P. (2023) · Springer eBooks · doi:10.1007/978-3-031-34006-2_6 · Cited by 8

Federated Anomaly Detection With Sparse Attentive Aggregation for Energy Consumption in Buildings

Shi, Y., Gao, B., Xu, F., Xu, X., & Jiang, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3331351 · Cited by 7

FedRDA: Representation Deviation Alignment in Heterogeneous Federated Learning

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System call frequency analysis-based generative adversarial network model for zero-day detection on mobile devices

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ADCAS: Adversarial Deep Clustering of Android Streams

Katebi, M., Rezakhani, A., & Joudaki, S. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2021.107443 · Cited by 7

A Conditional Privacy-Preserving Identity-Authentication Scheme for Federated Learning in the Internet of Vehicles

Xu, S., & Liu, R. (2024) · Entropy · doi:10.3390/e26070590 · Cited by 6

Zero-Tolerance Security Paradigm for Enterprise-Specific Industrial Internet of Things

Tariq, U. (2022) · Electronics · doi:10.3390/electronics11233953 · Cited by 7

Federated Multidiscriminators Multigenerators for Heterogeneous Industrial IoT

Li, B., Gao, W., Xie, J. F., Gong, M., Wang, L., & Li, H. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3468446 · Cited by 5

Dynamic IoT Malware Detection in Android Systems Using Profile Hidden Markov Models

Abanmi, N., Kurdi, H. A., & Alzamel, M. (2022) · Applied Sciences · doi:10.3390/app13010557 · Cited by 5

Federated Learning Using the Particle Swarm Optimization Model for the Early Detection of COVID-19

Kandati, D. R., Srivastava, G., Zhu, Y., Supriya, Y., Yenduri, G., Victor, N., Anusha, S., & Gadekallu, T. R. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8132-8_32 · Cited by 5

A High-Capacity Reversible Data-Hiding Scheme for Medical Image Transmission Using Modified Elias Gamma Encoding

Manikandan, V. M., Murthy, K. S. R., Siddineni, B., Victor, N., Maddikunta, P. K. R., & Hakak, S. (2022) · Electronics · doi:10.3390/electronics11193101 · Cited by 5

A Dynamic Weighted Federated Learning for Android Malware ClassificationPreprint

Chaudhuri, A., Nandi, A., & Pradhan, B. (2023) · doi:10.36227/techrxiv.22209670.v1 · Cited by 5

Softwarized Edge Intelligence for Advanced IIoT Ecosystems: A Data-Driven Architecture Across the Cloud/Edge Continuum

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FedDRC: A Robust Federated Learning-based Android Malware Classifier under Heterogeneous Distribution

Jiang, C., Xia, C., Liu, M., Chen, C., Li, H., Wang, T., & Li, P. (2024) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd61410.2024.10580300 · Cited by 4

SecFedMDM-1: A Federated Learning-Based Malware Detection Model for Interconnected Cloud Infrastructures

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Security Relevant Methods of Android's API Classification: A Machine Learning Empirical Evaluation

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Risk Assessment and Security of Industrial Internet of Things Network Using Advance Machine Learning

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Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

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Intrusion Detection Using Federated Learning

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Hybrid Multimodal Machine Learning Driven Android Malware Recognition and Classification Model

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FWICSS-Federated Watermarked Ideal Client Selection Strategy for Internet of Things (IoT) Intrusion Detection System

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Heterogeneity-Aware Federated Learning for Device Anomaly Detection in Industrial loT

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Analytical Study on Advanced Persistent Threat Detecting, Defending and Mitigating

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uitAnDiNeFed: android malware classification on distributed networks by using federated learning

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A Collaboration Federated Learning Framework with a Grouping Scheme against Poisoning Attacks

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A Study on the Efficiency of ML-Based IDS with Dimensional Reduction Methods for Industry IoT

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RAPSAMS: Robust affinity propagation clustering on static android malware stream

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Federated Learning for Grading Oil Palm Fruit Ripeness in the Oil Palm Industry

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FedCognis: An Adaptive Federated Learning Framework for Secure Anomaly Detection in Industrial IoT-Enabled Cognitive Cities

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Defending federated learning-based intrusion detection systems against model poisoning attacks

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Malware Searching Methods at FML-Architecture

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Evolving IoT and Green IoT in Healthcare Perspective

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SFMD: A Semi-Supervised Federated Malicious Traffic Detection Approach in IoT

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FedDroidADP: An Adaptive Privacy-Preserving Framework for Federated-Learning-Based Android Malware Classification System

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Disparity-Aware Federated Learning for Intrusion Detection Systems in Imbalanced Non-IID Settings

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Exploiting smartphone defence: a novel adversarial malware dataset and approach for adversarial malware detection

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Dynamic Malware Pattern Analysis with Rapid Node Behaviour Analysis Using Self Replication Model for Network Intrusion Detection

Mokkapati, R., & Dasari, V. L. (2024) · Ingénierie des systèmes d information · doi:10.18280/isi.290432 · Cited by 2

FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protection Method for Federated Android Malware Classifier

Jiang, C., Xia, C., Liu, M., Fang, R., Li, P., Li, H., & Wang, T. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650527 · Cited by 2

Evaluating Data Distribution Strategies in Federated Learning: A Trade-Off Analysis Between Privacy and Performance for IoT Security

White, J., & Legg, P. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3973-8_2 · Cited by 2

FedL_DBNFSpinalNet based malware detection in IoT devices

Bhavani, R., & Sankaradass, V. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02557-5 · Cited by 2

Internet of Things-Enabled Logistic Warehouse Scheduling Management With Human Machine Assistance

Zhang, Z. (2022) · International Journal of Information Systems and Supply Chain Management · doi:10.4018/ijisscm.305852 · Cited by 2

Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

Alsubaei, F. S., Alshahrani, H. M., Tarmissi, K. S., & Motwakel, A. (2023) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.034907 · Cited by 2

FedDroidMeter: A Privacy Risk Evaluator for FL-Based Android Malware Classification Systems

Jiang, C., Xia, C., Liu, Z., & Wang, T. (2023) · Entropy · doi:10.3390/e25071053 · Cited by 2

A Modular Encryption Framework in Cloud and Mobile Environments for Cybersecurity Solutions in Health Information

Gyawali, Y. P., & Karyakarte, M. S. (2023) · Research Journal of Computer Systems and Engineering · doi:10.52710/rjcse.64 · Cited by 2

2021 Index IEEE Transactions on Industrial Informatics Vol. 17

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Secured IoT Malware Detection Framework using AI based Fuzzy Logic Systems

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Design and Analysis of an Individual-Based Model for Malware Propagation on IoT Networks

del Rey, A. M. (2023) · Mathematics · doi:10.3390/math12010058 · Cited by 1

Privacy-Preserving Federated Learning in Oil Palm Industry for Fresh Fruit Bunch Ripeness Grading

Laddawong, P., Pianroj, Y., Chotikawanid, P., Punvichai, T., Jumrat, S., & Muangprathub, J. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.3.434-446 · Cited by 1

Efficient and secure multiparty summation without semi-honest third-party

Liu, Z., Li, F., Han, M., & Zhu, S. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111690 · Cited by 1

A Dynamic Hybrid IDS for APT Detection in ICS Using Deep Neural Networks

Battula, R. R., Al-Sinayyid, A., Kadiyala, S., Mannuru, V., & Adewumi, S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-12930-7_13 · Cited by 1

Immune-Inspired Malware Detection Algorithm for Edge-Based Industrial IoT Cybersecurity Protection

Adhikari, D. D., Karki, H. K., & Shrestha, N. (2025) · International Conference on Intelligent Innovations in Engineering and Technology (ICIIET) · doi:10.1109/iciiet65921.2025.11378681 · Cited by 1

OLG-FL: A Federated Learning Framework for Optimizing Local Training and Global Aggregation

Qiu, H., Liu, Z., Zhao, X., Xue, J., Hu, W., & Li, M. (2025) · International Conference on Computer Science and Management Technology (ICCSMT) · doi:10.1145/3795154.3795290 · Cited by 1

Generative AI-Enabled Cybersecurity Using Federated Learning for Malware Detection in Industrial IoT Environment

Alnajjar, I. A., Abu-AlSondos, I. A., & Amer, A. E. (2025) · International Conference on Business Intelligence for Technology Innovation (ICBITI) · doi:10.1109/icbiti65527.2025.11501038 · Cited by 1

A Hybrid Clustered Approach for Enhanced Communication and Model Performance in Blockchain-Based Collaborative Learning

Wang, Z., Hisada, M., & Abdallah, A. B. (2024) · IEEE Access · doi:10.1109/access.2024.3359272 · Cited by 1

Malware Detection in Industrial IoT using Federated Cloud

Lamech Vedaraj, J., & Maheswaran, C. P. (2023) · International Conference on Recent Advances in Science and Engineering Technology (ICRASET) · doi:10.1109/icraset59632.2023.10419977 · Cited by 1

DRL-based Federated Uncertainty-guided Semi-Supervised Learning for Network Traffic Selection and Threshold Determination in ZSM

Albaseer, A. M., & Abdallah, M. M. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437173 · Cited by 1

FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensitive Information Leakage in Federated Learning-based Android Malware Classifier

Jiang, C., Xia, C., Chen, C., Li, H., Wang, T., & Li, X. (2023) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom60117.2023.00051 · Cited by 1

Privacy Preserving Fingerprint Classification Using Federated Learning

Soni, A., Sandhya, M., & Rao, Y. S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-60935-0_7 · Cited by 1

Efficient Federated Split Learning on Android Smartphones via Adaptive Offloading Point Mechanism

Thanh, P. D., & Zettsu, K. (2025) · doi:10.1145/3733566.3734434 · Cited by 1

WeiDetect: Weibull distribution-based defense against poisoning attacks in federated learning for network intrusion detection systems

Sameera, K. M., Vinod, P., Rocha, A. D. R., A., R. R. K., & Conti, M. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104275 · Cited by 1

Federated Learning Over the Industrial Internet of Things: A Joint Optimization of Edge Association and Resource AllocationPreprint

Zhang, L., Wu, S., Xu, H., Liu, Q., Hong, C. S., & Han, Z. (2022) · doi:10.36227/techrxiv.20784001 · Cited by 1

Federated Learning Over the Industrial Internet of Things: A Joint Optimization of Edge Association and Resource AllocationPreprint

Zhang, L., Wu, S., Xu, H., Liu, Q., Hong, C. S., & Han, Z. (2022) · doi:10.36227/techrxiv.20784001.v1 · Cited by 1

Secure and Trust-based Model for E-Commerce applicationPreprint

Gao, W., Ding, Z., Crespo, R. A. G., & Martínez, Ó. S. (2022) · Research Square · doi:10.21203/rs.3.rs-720097/v1 · Cited by 1

A Dynamic Weighted Federated Learning for Android Malware ClassificationPreprint

Chaudhuri, A., Nandi, A., & Pradhan, B. (2023) · doi:10.36227/techrxiv.22209670

AI Enabled Human and Machine Activity Monitoring in Industrial IoT Systems

Saha, A., Saha, J., Mallik, M., & Chowdhury, C. (2023) · Engineering cyber-physical systems and critical infrastructures · doi:10.1007/978-3-031-31952-5_2 · Cited by 1

Federated Learning for IoT Applications, Attacks and Defense Methods

Qu, Z., Duan, R., Liu, Y., & Lu, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-42637-7_9

A Lightweight Malicious Traffic Identification Method Based on a Simplified Hybrid Vision Transformer ModelPreprint

Zhang, W., Feng, Y., Zhu, H., & Wen, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4791447

Enhancing Robustness within the Collaborative Federated Learning Framework: A Novel Grouping Algorithm for Edge Clients

Su, Z., Liu, I., Li, C.-F., Liu, C.-K., & Chiang, C.-H. (2024) · Applied Sciences · doi:10.3390/app14083255

Enhancing Edge Computing Security: Advanced Machine Learning Strategies for Malicious Task DetectionPreprint

Aljumaie, M., Dinh, H., & Nguyen, D. N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4868314

The intrinsic convenience of federated learning in malware IoT detection

Camerota, C., Pecorella, T., & Bagdanov, A. D. (2024) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm62983.2024.10814605

A Comprehensive Analysis of Various Federated Learning Techniques

Saini, N., Sharma, D., Mehrotra, D., & Bourazeri, A. (2024) · International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) · doi:10.1109/icaiccit64383.2024.10912324

Adaptive GAN-Based Defense Against Data Poisoning in Federated Learning

Pathak, P., & Sreekumari, P. (2025) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-89063-5_1

Integration of IoT in Dairy Facilities

Hamad, A. M. A., & Awad, D. A. B. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9949-1.ch005

A Review of Hybrid Defences for IoT: Rule-Based Systems and GANs

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