Blockchain-Inspired Collaborative Cyber-Attacks Detection for Securing Metaverse

Measuring the Effects of Non-Identical Data Distribution for Federated Visual ClassificationPreprint

Hsu, T., Qi, & Brown, M. (2019) · arXiv.org · doi:10.48550/arxiv.1909.06335 · Cited by 1,757 · see citing works

A Survey on Metaverse: Fundamentals, Security, and Privacy

Wang, Y., Su, Z., Zhang, N., Xing, R., Liu, D., Luan, T. H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3202047 · Cited by 1,655 · see citing works

Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory

Kang, J., Xiong, Z., Niyato, D. T., Xie, S., & Zhang, J. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2940820 · Cited by 961 · see citing works

Adaptive Federated Optimization

Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., & McMahan, H. B. (2020) · arXiv · doi:10.48550/arxiv.2003.00295 · Cited by 840 · see citing works

Artificial intelligence for the metaverse: A survey

Huynh‐The, T., Pham, Q., Pham, X., Nguyen, T. T., Han, Z., & Kim, D. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105581 · Cited by 788 · see citing works

Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism

Khan, L. U., Pandey, S. R., Tran, N. H., Saad, W., Han, Z., Nguyen, M. N. H., & Hong, C. S. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900649 · Cited by 506 · see citing works

Model aggregation techniques in federated learning: A comprehensive survey

Qi, P., Chiaro, D., Guzzo, A., Ianni, M., Fortino, G., & Piccialli, F. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.09.008 · Cited by 473 · see citing works

Decentralized Privacy Using Blockchain-Enabled Federated Learning in Fog Computing

Qu, Y., Gao, L., Luan, T. H., Xiang, Y., Yu, S., Li, B., & Zheng, G. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2977383 · Cited by 451 · see citing works

Federated learning for the Internet of Things: Applications, challenges, and opportunities

Zhang, T., Gao, L., He, C., Zhang, M., Krishnamachari, B., & Avestimehr, A. S. (2022) · IEEE Internet of Things Magazine · doi:10.1109/iotm.004.2100182 · Cited by 412 · see citing works

FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning

AbdulRahman, S., Tout, H., Mourad, A., & Talhi, C. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3028742 · Cited by 323 · see citing works

Byzantine-Resilient Secure Federated Learning

So, J., Guler, B., & Avestimehr, A. S. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3041404 · Cited by 313 · see citing works

Client Selection in Federated Learning: Principles, Challenges, and Opportunities

Fu, L., Zhang, H., Gao, G., Zhang, M., & Liu, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3299573 · Cited by 310 · see citing works

PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems

Zhang, J., Chen, B., Cheng, X., Binh, H. T. T., & Yu, S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3023126 · Cited by 291 · see citing works

Blockchain Meets Metaverse and Digital Asset Management: A Comprehensive Survey

Truong, V. T., Le, L. B., & Niyato, D. T. (2023) · IEEE Access · doi:10.1109/access.2023.3257029 · Cited by 275 · see citing works

Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data

Wu, H., & Wang, P. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3146399 · Cited by 197 · see citing works

Federated learning in smart cities: Privacy and security survey

Al-Huthaifi, R., Li, T., Huang, W. X., Gu, J., & Li, C. (2023) · Information Sciences · doi:10.1016/j.ins.2023.03.033 · Cited by 160 · see citing works

An Efficient Hybrid-DNN for DDoS Detection and Classification in Software-Defined IIoT Networks

Zainudin, A., Ahakonye, L. A. C., Akter, R., Kim, D., & Lee, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3196942 · Cited by 134 · see citing works

Trustworthy Federated Learning via Blockchain

Yang, Z., Shi, Y., Zhou, Y., Wang, Z., & Yang, K. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3201117 · Cited by 130 · see citing works

MiTFed: A Privacy Preserving Collaborative Network Attack Mitigation Framework Based on Federated Learning Using SDN and Blockchain

Houda, Z. A. E., Hafid, A. S., & Khoukhi, L. (2023) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2023.3237367 · Cited by 129 · see citing works

5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless NetworkPreprint

Samarakoon, S., Siriwardhana, Y., Porambage, P., Liyanage, M., Chang, S., Kim, J., Kim, J., & Ylianttila, M. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.01298 · Cited by 107 · see citing works

Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review

Witt, L., Heyer, M., Toyoda, K., Samek, W., & Li, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3231363 · Cited by 101 · see citing works

Federated Transfer Learning With Client Selection for Intrusion Detection in Mobile Edge Computing

Cheng, Y., Lu, J., Niyato, D. T., Lyu, B., Kang, J., & Zhu, S. (2022) · IEEE Communications Letters · doi:10.1109/lcomm.2022.3140273 · Cited by 88 · see citing works

Federated Learning Inspired Low-Complexity Intrusion Detection and Classification Technique for SDN-Based Industrial CPS

Zainudin, A., Akter, R., Kim, D., & Lee, J. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3299606 · Cited by 88 · see citing works

BESIFL: Blockchain-Empowered Secure and Incentive Federated Learning Paradigm in IoT

Xu, Y., Lu, Z., Gai, K., Duan, Q., Lin, J., Wu, J., & Choo, K. R. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3138693 · Cited by 86 · see citing works

Blockchain-Based Personalized Federated Learning for Internet of Medical Things

Lian, Z., Wang, W., Han, Z., & Su, C. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3279111 · Cited by 75 · see citing works

A Survey on Consensus Algorithms in Blockchain-Based Applications: Architecture, Taxonomy, and Operational Issues

Islam, S., Islam, M. J., Hossain, M., Noor, S., Kwak, K. S., & Islam, S. (2023) · IEEE Access · doi:10.1109/access.2023.3267047 · Cited by 71 · see citing works

Toward Zero-Trust Security for the Metaverse

Cheng, R., Chen, S., & Han, B. (2023) · IEEE Communications Magazine · doi:10.1109/mcom.018.2300095 · Cited by 66 · see citing works

Eiffel : Efficient and Fair Scheduling in Adaptive Federated Learning

Sultana, A., Haque, M. M., Chen, L., Xu, F., & Yuan, X. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3187365 · Cited by 56 · see citing works

A GAN-Based Intrusion Detection Model for 5G Enabled Future Metaverse

Ding, S., Kou, L., & Wu, T. (2022) · Mobile Networks and Applications · doi:10.1007/s11036-022-02075-6 · Cited by 56 · see citing works

ACS: Accuracy-based client selection mechanism for federated industrial IoT

Putra, M. A. P., Putri, A. R., Zainudin, A., Kim, D., & Lee, J. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100657 · Cited by 55 · see citing works

Security for the Metaverse: Blockchain and Machine Learning Techniques for Intrusion Detection

Truong, V. T., & Le, L. B. (2024) · IEEE Network · doi:10.1109/mnet.2024.3351882 · Cited by 55 · see citing works

Privacy-Preserving Decentralized Aggregation for Federated Learning

Jeon, B., Ferdous, S. M., Rahman, M. R., & Walid, A. (2021) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps51825.2021.9484437 · Cited by 53 · see citing works

Decentralized Aggregation for Energy-Efficient Federated Learning via D2D Communications

Al-Abiad, M. S., Obeed, M., Hossain, M., & Chaaban, A. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3253718 · Cited by 52 · see citing works

A Lightweight Residual Networks Framework for DDoS Attack Classification Based on Federated Learning

Qin, T., Guang, C., Chen, W., & Si, W. (2021) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps51825.2021.9484622 · Cited by 50 · see citing works

A Game Theory-Based Incentive Mechanism for Collaborative Security of Federated Learning in Energy Blockchain Environment

He, Y., Luo, M., Wu, B. L., Sun, L., Wu, Y., Liu, Z., & Xiao, K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3282732 · Cited by 49 · see citing works

Zeno: Byzantine-suspicious stochastic gradient descentPreprint

Xie, C., Koyejo, O., & Gupta, I. (2018) · arXiv.org · doi:10.48550/arxiv.1805.10032 · Cited by 48 · see citing works

FLASH: Heterogeneity-Aware Federated Learning at Scale

Yang, C., Xu, M., Wang, Q., Chen, Z., Huang, K., Ma, Y., Bian, K., Huang, G., Liu, Y., Jin, X., et al. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3214234 · Cited by 48 · see citing works

MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning

Truong, V. T., & Le, L. B. (2023) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2023.3312299 · Cited by 48 · see citing works

Metaverse intrusion detection of wormhole attacks based on a novel statistical mechanism

Kuo, S., Tseng, F., & Chou, Y. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.01.017 · Cited by 48 · see citing works

Blockchain-Based Decentralized Model Aggregation for Cross-Silo Federated Learning in Industry 4.0

Ranathunga, T., McGibney, A., Rea, S., & Bharti, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3218704 · Cited by 47 · see citing works

Reputation-Aware Federated Learning Client Selection Based on Stochastic Integer Programming

Tan, X., Ng, W. C., Lim, W. Y. B., Xiong, Z., Niyato, D. T., & Yu, H. (2022) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2022.3191332 · Cited by 37 · see citing works

FL-Incentivizer: FL-NFT and FL-Tokens for Federated Learning Model Trading and Training

Majeed, U., Khan, L. U., Hassan, S. S., Han, Z., & Hong, C. S. (2023) · IEEE Access · doi:10.1109/access.2023.3235484 · Cited by 31 · see citing works

6G-Enabled Anomaly Detection for Metaverse Healthcare Analytics in Internet of Things

Wu, X., Yang, Y., Bilal, M., Qi, L., & Xu, X. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3298092 · Cited by 30 · see citing works

Federated Learning for Metaverse: A Survey

Chen, Y., Huang, S., Gan, W., Huang, G.-S., & Wu, Y.-D. (2023) · The Web Conference · doi:10.1145/3543873.3587584 · Cited by 29 · see citing works

RL-Based Federated Learning Framework Over Blockchain (RL-FL-BC)

Riahi, A. A., Mohamed, A. M. S., & Erbad, A. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3241437 · Cited by 28 · see citing works

FedDDoS: An Efficient Federated Learning-based DDoS Attacks Classification in SDN-Enabled IIoT Networks

Zainudin, A., Akter, R., Kim, D.-S., & Lee, J. (2022) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc55196.2022.9952610 · Cited by 27 · see citing works

An Efficient Blockchain Assisted Reputation Aware Decentralized Federated Learning Framework

Kasyap, H., Manna, A., & Tripathy, S. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3231283 · Cited by 25 · see citing works

TORR: A Lightweight Blockchain for Decentralized Federated Learning

Ma, X., & Xu, D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3288078 · Cited by 24 · see citing works

Blockchain-Based Federated Learning: A Systematic Survey

Huang, J., Kong, L., Chen, G., Xiang, Q., Chen, X., & Liu, X. (2022) · IEEE Network · doi:10.1109/mnet.129.2200346 · Cited by 21 · see citing works

Blockchain-Empowered Federated Learning Through Model and Feature Calibration

Wang, Q., Liao, W., Guo, Y., McGuire, M. P., & Yu, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3311967 · Cited by 19 · see citing works

A Novel Blockchain-Assisted Aggregation Scheme for Federated Learning in IoT Networks

Liu, Z., Zheng, K., Hou, L., Yang, H., & Yang, K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3277463 · Cited by 15 · see citing works

Fast Detection of Cyberattacks on the Metaverse through User-plane Inference

Bütün, B., Akem, A. T., Gucciardo, M., & Fiore, M. (2023) · IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom) · doi:10.1109/metacom57706.2023.00067 · Cited by 13 · see citing works

Decentralized Aggregation Design and Study of Federated Learning

Malladi, V. S., Li, Y. J., Siddula, M., Seoand, D., & Huang, Y. (2021) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/swc50871.2021.00052 · Cited by 11 · see citing works

Local Loss-Assisted Dynamic Client Selection for Image Classification-Oriented Federated Learning

Ong, S. C., & Gau, R.-H. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838873 · Cited by 10 · see citing works

Blockchain-Based Decentralized Trust Aggregation for Federated Cyber-Attacks Classification in SDN-Enabled Maritime Transportation Systems

Zainudin, A., Alief, R. N., Putra, M. A. P., Akter, R., Kim, D.-S., & Lee, J. (2023) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops57953.2023.10283507 · Cited by 10 · see citing works

BFLMeta: Blockchain-Empowered Metaverse with Byzantine-Robust Federated Learning

Truong, V. T., Hoang, D. N. M., & Le, L. B. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437675 · Cited by 8 · see citing works

Securing the Metaverse: A Blockchain-Enabled Zero-Trust Architecture for Virtual Environments

Din, I. U., Khan, K. H., Almogren, A., Zareei, M., & Pérez-Díaz, J. A. (2024) · IEEE Access · doi:10.1109/access.2024.3423400 · Cited by 37 · see citing works

Resilience and Failure Analysis in Next-Generation Communication Networks: A Contemporary Survey

Bi, S., Yuan, X., Hu, S., Li, K., Ni, W., Hossain, E., & Wang, X. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3620950 · Cited by 31 · see citing works

Double Robust Federated Digital Twin Modeling in Smart Grid

Zhou, Y., Ge, Y., & Jia, L. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3450433 · Cited by 23 · see citing works

A Lightweight Method for Botnet Detection in Internet of Things Environment

Ma, W., Wang, X., Dong, J., Hu, M., & Zhou, Q. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3548411 · Cited by 21 · see citing works

Integrating machine learning with proof-of-authority-and-association for dynamic signer selection in blockchain networks

Kim, D., & Rizal, S. (2024) · ICT Express · doi:10.1016/j.icte.2024.10.008 · Cited by 20 · see citing works

MetaWatch: Trends, Challenges, and Future of Network Intrusion Detection in the Metaverse

Nkoro, E. C., Njoku, J. N., Nwakanma, C. I., Lee, J., & Kim, D. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3568477 · Cited by 16 · see citing works

Blockchain-as-a-Service: A Pure Chain approach

Kim, D., Tuli, E. A., Igboanusi, I. S., Somrat, M. M. H., & Pham, X. (2025) · Blockchain Research and Applications · doi:10.1016/j.bcra.2025.100397 · Cited by 11 · see citing works

UMetaBE-DPPML: Urban metaverse & blockchain-enabled decentralised privacy-preserving machine learning verification and authentication with metaverse immersive devices

Kuru, K., & Kuru, K. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2025.02.001 · Cited by 11 · see citing works

Blockchain and 6G: Pioneering New Dimensions in Metaverse Marketing

Din, I. U., Almogren, A., & Kim, B.-S. (2024) · IEEE Access · doi:10.1109/access.2024.3438842 · Cited by 10 · see citing works

Blockchain for the metaverse: Recent advances, taxonomy, and future challenges

Yousafzai, A., Sheeraz, M. M., Pogrebna, G., Crowcroft, J., & Yaqoob, I. (2025) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2025.104355 · Cited by 8 · see citing works

A comprehensive survey on the convergence of Blockchain, Digital Twins, and Metaverse: Shaping the future of cybersecurity frameworks

Ahmad, A., Shah, A., Alnumay, W. S., Adnan, M., Anwer, S., & Zaman, Q. U. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110486 · Cited by 7 · see citing works

FL-IDS++: A dynamic federated learning framework for intrusion detection with personalized non-IID data, adversarial resilience and energy-efficient lightweight models

Arimanda, N., C, V. R., & Padmavathi, U. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.108234 · Cited by 6 · see citing works

A systematic literature review on federated cyber-attack detection for edge intelligence: Challenges, approaches, and future directions

Sharmin, Z., Uddin, M. P., Xiang, Y., & Chen, F. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.100965 · Cited by 6 · see citing works

An intelligent federated learning boosted cyberattack detection system for Denial-Of-Wallet attack using advanced heuristic search with multimodal approaches

Akhmetshin, E., Hudayberganov, D., SHICHIYAKH, R. A., Yelisetti, S., Pappala, L. K., Shukla, R. D., & Chandra, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-96986-5 · Cited by 6 · see citing works

BPSAC: A Blockchain and PUF-Enabled Security Architecture With C-IDS for Maritime Intelligent Transportation Systems

Liu, J., Hou, J., Lin, M., & Xu, Z. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3605407 · Cited by 5 · see citing works

Integration of digital twin, blockchain, artificial intelligence in an IoT Metaverse environment for mitigation and assessment of cascading failure events in smart grids: Recent advancement and future research challengesPreprint

Fida, K., Abbasi, U., Adnan, M., Iqbal, S., Ishaq, I., & Gasim, S. E. (2025) · doi:10.31224/4397 · Cited by 5 · see citing works

Cybersecurity issues in the metaverse libraries

Oladokun, B. D., Ajani, Y. A., Oloniruha, E. A., Ilori, O. O., Nsirim, O., & Egbe, I. M. (2025) · Business Information Review · doi:10.1177/02663821251328826 · Cited by 4 · see citing works

Collaborative Decentralized Learning for Detecting Bearing Faults in Industrial Internet of Things

Putra, M. A. P., Zainudin, A., Sampedro, G. A., Utami, N. W., Kim, D., & Lee, J. (2024) · Asia-Pacific Conference on Communications (APCC) · doi:10.1109/apcc62576.2024.10768063 · Cited by 4 · see citing works

PureNFT: A Blockchain-Based Ticketing System with Lightweight AI for Scalping Prevention

Haryadi, G. A., Zainudin, A., Lee, J., & Kim, D. (2025) · International Conference on Consumer Electronics · doi:10.1109/icce63647.2025.10930216 · Cited by 4 · see citing works

Gated Recurrent Unit-Based Detection Framework for Digital Wallet Attacks in Serverless Computing Environments

Yalla, S. T., Batra, I., & Malik, A. (2026) · The International journal of networked and distributed computing · doi:10.1007/s44227-025-00084-x · Cited by 3 · see citing works

Impact of Adaptive Client Selection on Federated Learning for IoMT Ecosystem

Ihekoronye, V. U., Lee, J. M., & Kim, D. (2024) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc62082.2024.10827188 · Cited by 3 · see citing works

Deep learning with leagues championship algorithm based intrusion detection on cybersecurity driven industrial IoT systems

Alotaibi, S. S., & Alghamdi, T. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-15464-0 · Cited by 2 · see citing works

Blockchain enabled architecture for data privacy preservation in the metaverse environment

Patwe, S., & Mane, S. B. (2025) · Discover Computing · doi:10.1007/s10791-025-09606-1 · Cited by 2 · see citing works

Leveraging blockchain for cybersecurity detection using hybridization of prairie dog optimization with differential evolution on internet of things environment

Alruwaili, F. F. (2025) · Scientific Reports · doi:10.1038/s41598-025-10410-6 · Cited by 2 · see citing works

Hybrid Blockchain-Assisted Federated Learning Intrusion Detection System for DoS Attacks in UAV Sensor Network

Nwankwo, O. U., Ajakwe, S. O., Kim, D., & Lee, J. M. (2026) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic68212.2026.11454237 · Cited by 2 · see citing works

Exploring Blockchain in the Metaverse: Review of State-of-the-Art Frameworks and Future Research Challenges

Pandey, A., Obaidat, M. S., Kushwaha, A., Dwivedi, S. K., & Amin, R. (2025) · International Conference on Computer, Information and Telecommunication Systems · doi:10.1109/cits65975.2025.11099077 · Cited by 1 · see citing works

Implementation of Digital Signatures and QR Codes for the Verification of Certificates of Authenticity for Diplomas

Hidayat, A. N., Zainudin, A., & Yuliana, M. (2025) · International Electronics Symposium (IES) · doi:10.1109/ies67184.2025.11161333 · Cited by 1 · see citing works

Resilient IoT Authentication Against Physical Tampering and Side Channel Attacks

Alzubaidi, W. K., Ataalla, A. F., Alsayednoor, H. M., Al-Shareeda‬‏, ‪. A., Almaiah, M. A., & Albahar, M. A. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.13464 · Cited by 1 · see citing works

Collaborative edge learning-assisted interactive avatar generation framework for IoT-driven metaverse

Zainudin, A., Putra, M. A. P., Kim, D., & Lee, J. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101639 · Cited by 1 · see citing works

Explainable Deep Reinforcement Learning for Anomaly Detection in IoT-Enabled Metaverse Healthcare: Toward Trustworthy Cyber Threat Intelligence

Yang, J., Xu, X., Khan, M. A., Brahim, G. B., Baili, J., Por, L. Y., & Li, C. (2026) · Research · doi:10.34133/research.1245 · Cited by 1 · see citing works

PureFL: Trust-Weighted Federated Learning With Noise-Resilient Homomorphic Encryption for Blockchain-Based IoV Networks

Nakayiza, H. L., Ahakonye, L. A. C., Kim, D., & Lee, J. M. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3691349 · Cited by 1 · see citing works

Metaverse Security Monitoring Based on Virtual Environment Analysis Using Machine Learning Techniques

Oliver, S., Chinnasamy, A., Varun, P., Manikandan, N., Magesh, S., & Manikandan, R. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0340-6.ch017

Lightweight data security system for resource-constrained IIoT devices

Amadi, C. S., Ajakwe, S. O., Nnadiekwe, C. A., & Jun, T. (2026) · Franklin Open · doi:10.1016/j.fraope.2026.100585

BERBLOM: Blockchain-enabled reliable land ownership management system using ERC-4907 smart contract

Nwankwo, O. U., Ansori, M. R. R., Tuli, E. A., Kim, D.-S., & Lee, J.-M. (2026) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-026-02227-5

FED-FAN: Federated learning-based intrusion detection system for Flying Ad-hoc Networks

Nwankwo, O. U., Ajakwe, S. O., Haryadi, G. A., Ansori, M. R. R., Kim, D., & Lee, J. M. (2026) · Ad Hoc Networks · doi:10.1016/j.adhoc.2026.104348

Balancing Innovation and Privacy in the Decentralized Metaverse: Case studies of Exploring Blockchain and Web 3.0 for Sustainable Development

Mohammed, F. H., Izham, I. A., Akbar, M. J., Ansar, N. S. M., Roslan, N. M., Zainuddin, A. A., & Ahmad, H. K. (2026) · International Journal on Perceptive and Cognitive Computing · doi:10.31436/ijpcc.v12i1.534

Blockchain-Based Secure Land Certificate Storage with Encryption and Digital Signatures

Irvani, M. A., Sudarsono, A., Zainudin, A., Kim, D.-S., & Lee, J.-M. (2026) · International Conference on Mobile, Military, Maritime IT Convergence (ICMIC) · doi:10.1109/icmic70587.2026.11708652

MetaCIDS: Metaverse Collaborative Intrusion Detection Based on Blockchain and Federated Learning

Truong, V. T., Nguyen, V. T., & Le, L. B. (2023) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps58843.2023.10464435 · Cited by 4 · see citing works

MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning

Truong, V. T., & Le, L. B. (2023) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2023.3312299 · Cited by 48 · see citing works

MetaCIDS: A Metaverse Collaborative Intrusion Detection System based on Blockchain and Federated LearningPreprint

Truong, V., & Le, L. B. (2023) · doi:10.36227/techrxiv.22816568.v2 · Cited by 1 · see citing works

BFLMeta: Blockchain-Empowered Metaverse with Byzantine-Robust Federated Learning

Truong, V. T., Hoang, D. N. M., & Le, L. B. (2023) · Globecom · doi:10.1109/globecom54140.2023.10437675 · Cited by 8 · see citing works

BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse

Liu, W., Chen, H., & Ngai, E. C. ‐. (2024) · IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom) · doi:10.1109/metacom62920.2024.00037 · Cited by 4 · see citing works