The DOI matches its Crossref record; 38 of the 40 references it cites were found in a registry or an index.
Fed-IIoT: A Robust Federated Malware Detection Architecture in Industrial IoT
Abstract
The sheer volume of industrial Internet of Things (IIoT) malware is one of the most serious security threats in today's interconnected world, with new types of advanced persistent threats and advanced forms of obfuscations. This article presents a robust federated learning based architecture called Fed-IIoT for detecting Android malware applications in IIoT. Fed-IIoT consists of two parts: first, participant side, where the data are triggered by two dynamic poisoning attacks based on a generative adversarial network (GAN) and federated GAN; and second, server side, which aims to monitor the global model and shape a robust collaboration training model, by avoiding anomaly in aggregation by a GAN network (A3GAN) and adjust two GAN-based countermeasure algorithms. One of the main advantages of Fed-IIoT is that devices can safely participate in the IIoT and efficiently communicate with each other, with no privacy issues. We evaluate our solutions through experiments on various features using three IoT datasets. The results confirm the high accuracy rates of our attack and defense algorithms and show that the A3GAN defensive approach preserves the robustness of data privacy for Android mobile users and is about 8% higher accuracy with existing state-of-the-art solutions.
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“the Fed-IIoT system performs significantly better than other local adversarial training mechanisms.”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [87]
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DEFEAT: Android device behavior-based datasets for multi-stage APT
Jabar, T., Al-Kadhimi, A. A., & Singh, M. M. (2026) · Data in Brief · doi:10.1016/j.dib.2026.112539
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El-Hajj, M. I., & Zeineddine, M. A. J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1811692
Ciaramella, G., Martinelli, F., Santone, A., & Mercaldo, F. (2026) · International Journal of Information Security · doi:10.1007/s10207-026-01268-4
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RDO-DCNet: Red Deer Optimized Deep Learning Framework for Malware Detection in IIoT
Alwarsamy, V., Rethnaraj, J., Nagappan, M. M., & Patibandla, L. P. K. R. (2026) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-026-01392-y
FedHDFF: Personalized federated learning based on hyper-dimensional feature fusion
Zhu, S., Yao, W., Zhang, L., Sun, G., Liu, Y., He, X., Zhu, X., & Wang, R. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116043
Prototype region calibration guided federated domain generalization
Yao, W., Zhu, S., Zhang, L., Xu, Q., Yang, Y., Sun, G., Zhu, X., & Wang, R. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.105061
Federated multimodal malware classification under non-IID data
Liu, S., Zhang, W., Yang, T., Li, G., & Zhang, L. (2026) · Cybersecurity · doi:10.1186/s42400-026-00630-2
Enhancing privacy and security in android devices through federated learning for detecting malware
Joshi, A., & Kumar, S. (2026) · AIP conference proceedings · doi:10.1063/5.0328829
Saeed, R., Qureshi, H. K., Qadir, J., & Haider, S. A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7207445
Khacha, A., Aliouat, Z., Harbi, Y., Gherbi, C., Saadouni, R., & Lakhlef, H. (2026) · Cluster Computing · doi:10.1007/s10586-026-06418-w
Alsaidlani, A., Rashid, M., & Aljabri, M. (2026) · Sensors · doi:10.3390/s26165098
Federated and Collaborative Deep Learning Optimization in Cloud–Fog Computing
Shamila, M., Shashank, S., Vishwakarma, B., Gupta, Z., Khare, N., & Bansal, S. (2026) · International Conference on Emerging Trends in Information, Communication & Systems (ICETICS) · doi:10.1109/icetics69505.2026.11651234
Feng, L., Zhou, J., Fang, F., Yu, Y., & Wang, J. (2026) · Computer Networks · doi:10.1016/j.comnet.2026.112773
AFedAIoT: Adaptive Federated AI model for secure and continuous anomaly detection in industrial IoT
Islam, U., Ali, I., Abdulwahid, A. A., Alamro, S., Alwageed, H. S., & Ullah, H. (2026) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2026.102292
Anas, H., Toumi, H., & Talea, M. (2026) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1826274
Metadata
- Authors
- Taheri, R., Shojafar, M., Alazab, M., & Tafazolli, R.
- Affiliations
- Shiraz University of Technology (IR)
- University of Surrey (GB)
- Charles Darwin University (AU)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-12
- Volume
- 17
- Issue
- 12
- Pages
- 8442-8452
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3113308842
- Cited by
- 218
- References
- 40
- Citations by year
- 2026: 22 · 2025: 33 · 2024: 40 · 2023: 52 · 2022: 47 · 2021: 24
- FWCI
- 16.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Software › Advanced Malware Detection Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- federated learning, malware detection, Industrial Internet of Things, Android malware, data poisoning, generative adversarial networks, data privacy preservation, advanced persistent threats, malware obfuscation
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