Publications [Google Scholar]
Journal Publications
PUFZIN: Secure and Scalable Blockchain-IoT with PUFs and Zero-Knowledge Proofs
Daniel Commey, S. G. Hounsinou, G. V. Crosby
Journal of Information Security and Applications, vol. 100, article 104510, 2026
TL;DR Combines physical-unclonable-function device fingerprints with zero-knowledge proofs for scalable, privacy-preserving authentication in blockchain-IoT.
A Comparative Study of Explainability Methods for Time-Series Forecasting of Blood Glucose Levels
G. B. Akrong, B. Appiah, Daniel Commey, A. Dwumfour, P. Boakye-Sekyerehene, E. Owusu
Discover Artificial Intelligence, 2026
TL;DR Benchmarks explainability methods (attention, saliency maps, integrated gradients, SHAP, LIME) on a BiLSTM for blood-glucose forecasting, finding attention and gradient-based methods most faithful for clinical time series.
EdgeFence: Federated Temporal Graph Neural Networks for Lightweight, Adversarial Malware Detection in Distributed Edge Networks
O. Isaac, B. Appiah, Daniel Commey, K. Owusu-Agyemang, M. Asante, B. H. Acquah
International Journal of Information Security, vol. 25, article 88, 2026
TL;DR Federated temporal graph neural networks for lightweight, adversarially robust malware detection across distributed edge networks.
PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework
Daniel Commey, G. V. Crosby
Expert Systems with Applications, 2026
TL;DR A post-quantum-secure, blockchain-verified federated learning framework that keeps model updates authenticated against quantum adversaries.
Game-Theoretic Analysis of MEV Attacks and Mitigation Strategies in Decentralized Finance
B. Appiah, Daniel Commey, W. Bagyl-Bac, L. Adjei, E. Owusu
Analytics, 2025
TL;DR Models the MEV supply chain (searchers, builders, validators) as a three-stage game of incomplete information, analyzes commit-reveal and threshold-encryption mitigations, and validates the theory against Ethereum on-chain data.
Blockchain-Enabled Dynamic Honeypot Conversion for Resource-Efficient IoT Security
Daniel Commey, M. Nkoom, S. G. Hounsinou, G. V. Crosby
Journal of Information Security and Applications, 2025
TL;DR Dynamically converts idle IoT devices into honeypots using ML threat scoring and game theory, improving security with minimal resource cost.
Post-Quantum Secure Blockchain-Based Federated Learning Framework for Healthcare Analytics
Daniel Commey, S. G. Hounsinou, G. V. Crosby
IEEE Networking Letters, 2025
TL;DR A post-quantum, blockchain-verified federated learning framework tailored to privacy-sensitive healthcare analytics.
Quantifying the Impact of TLE Ageing on LEO IoT Link Reliability
Daniel Commey, K. Abbad, M. Nkoom, G. S. Klogo, L. Khoukhi, G. V. Crosby
IEEE Networking Letters, 2025
TL;DR Quantifies how ageing of Two-Line Element orbital data causes duty-cycled LEO IoT terminals to miss satellite passes, and finds that keeping TLEs fresher than about six hours is needed for over 99% pass reliability.
Secure IoT Firmware Updates Against Supply Chain Attacks
B. Appiah, Daniel Commey, I. Osei, B. K. Frimpong, G. Assamah, E. N. A. Hammond
The Journal of Supercomputing, 2025
TL;DR A scheme for securing IoT firmware updates against supply-chain attacks on the update pipeline.
Enhanced federated learning for secure medical data collaboration
B. Appiah, I. Osei, B. K. Frimpong, Daniel Commey, K. Owusu-Agyemang, G. Assamah
Journal of Analytical Science and Technology, 2025
TL;DR An enhanced federated learning approach for secure, privacy-preserving collaboration on medical data across institutions.
Securing Blockchain-Based IoT Systems: A Review
Daniel Commey, B. Mai, S. G. Hounsinou, G. V. Crosby
IEEE Access, vol. 12, pp. 98856--98881, 2024
TL;DR A comprehensive review of security mechanisms, threat models, and open challenges for blockchain-based IoT systems.
Performance Comparison of 3DES, AES, Blowfish and RSA for Dataset Classification and Encryption in Cloud Data Storage
Daniel Commey, G. S. Klogo, J. D. Gadze
International Journal of Computer Applications, 177(40), 17--22, 2020
TL;DR Compares 3DES, AES, Blowfish, and RSA for encrypting and classifying datasets in cloud storage, weighing security against performance.
Conference Publications
Fusing Vessel Behavior and Weather Context for Real-time Attribution of AIS Dropouts
K. Abbad, Daniel Commey, S. G. Hounsinou, L. Khoukhi, L. Mesnil, G. V. Crosby
IEEE International Conference on Communications, Communication and Information Systems Security Symposium, 2026
TL;DR Fuses vessel behavior with weather context to attribute, in real time, whether maritime AIS signal dropouts are benign or suspicious.
Scalability and Resilience in Practice: A 24-Month Measurement Study of Congestion Dynamics and Reliability in Production Layer-2 Networks
Daniel Commey, K. Abbad, L. Khoukhi, G. V. Crosby
IEEE International Conference on Communications Workshops, 5th Workshop on Sustainable and Resilient Industrial Networks, 2026
TL;DR A 24-month measurement study of congestion dynamics and reliability in production blockchain Layer-2 networks.
Federated DDoS Detection with Clustered Quantization-Aware Training Models for IoRT
M. Nkoom, Daniel Commey, Y. Alsenani, S. G. Hounsinou, G. V. Crosby
2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA
TL;DR Federated, clustered, quantization-aware models for lightweight DDoS detection in the Internet of Robotic Things.
FedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated Learning
Daniel Commey, K. Abbad, G. V. Crosby, L. Khoukhi
2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA
TL;DR Uses lightweight digital twins to predict low-value client updates and skip them, cutting federated-learning communication without destabilizing convergence.
A Unified Lightweight Benchmark for Privacy-Preserving Federated Learning in Cyber-Physical Systems (Fashion-MNIST Case Study)
B. Ockman, Daniel Commey, G. V. Crosby
2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA
TL;DR A unified, lightweight, reproducible benchmark for privacy-preserving federated learning in cyber-physical systems, demonstrated on Fashion-MNIST.
Resource-Aware Clustered Federated Learning for Industrial Digital Twins: A Reproducible Benchmark on Fashion-MNIST
U. Hamid, D. Sung, Daniel Commey, G. V. Crosby
2026 IEEE 23rd Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA
TL;DR Resource-aware clustered federated learning for industrial digital twins, with a reproducible Fashion-MNIST benchmark.
Robotic Algorithm Service Contracts to Manage and Incentivize Adaptive Behavior
S. Mallikarachchi, P. Thammi, Daniel Commey, S. S. Vitharana, M. Chintalapati, I. S. Godage
2025 7th International Conference on Blockchain Computing and Applications (BCCA), Dubrovnik, Croatia
TL;DR Uses blockchain service contracts to manage and incentivize adaptive behavior in robotic systems.
Securing Blockchain-based IoT Systems with Physical Unclonable Functions and Zero-Knowledge Proofs
Daniel Commey, S. G. Hounsinou, G. V. Crosby
2024 IEEE 49th Conference on Local Computer Networks (LCN), Normandy, France
TL;DR The conference foundation for PUFZIN: device authentication for blockchain-IoT built on physical unclonable functions and zero-knowledge proofs.
Securing the Internet of Robotic Things: A Federated Learning Approach
M. Nkoom, Daniel Commey, S. G. Hounsinou, G. V. Crosby
2024 IEEE 49th Conference on Local Computer Networks (LCN), Normandy, France
TL;DR Applies federated learning to secure the Internet of Robotic Things without centralizing sensitive device data.
Strategic Deployment of Honeypots in Blockchain-based IoT Systems
Daniel Commey, S. G. Hounsinou, G. V. Crosby
2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS), Abu Dhabi, UAE
TL;DR Studies where to place honeypots in blockchain-based IoT systems for the most efficient deception-based defense.
EGAN: Evolutional GAN for Ransomware Evasion
Daniel Commey, B. Appiah, B. K. Frimpong, I. Osei, E. N. A. Hammond, G. V. Crosby
2023 IEEE 48th Conference on Local Computer Networks (LCN), Daytona Beach, FL, USA
TL;DR Combines an evolution strategy with a GAN to generate ransomware variants that stay functional while evading a majority of real-world VirusTotal detection engines.
Preprints and Working Papers
FedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Laundering
Daniel Commey, M. Nkoom, Y. Alsenani, S. G. Hounsinou, G. V. Crosby
arXiv:2601.17935 [cs.LG], 2026
TL;DR Detects cross-institution money laundering by sharing only boundary node embeddings (never raw transaction graphs) through federated graph learning, secured with post-quantum cryptography.
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs
Daniel Commey, B. Appiah, G. S. Klogo, G. V. Crosby
arXiv:2507.11649 [cs.LG], 2025
TL;DR Replaces raw metric reporting in federated learning with a zero-knowledge proof of a predicate (for example, loss below a threshold), so the server can gate updates without learning the actual metric.
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
Daniel Commey, R. A. Sarpong, G. S. Klogo, W. Bagyl-Bac, G. V. Crosby
arXiv:2507.12439 [cs.LG], 2025
TL;DR Designs a Bayesian incentive mechanism that makes data poisoning economically irrational for rational clients, while staying plug-in compatible with standard federated-learning pipelines.
Performance Analysis and Deployment Considerations of Post-Quantum Cryptography for Consumer Electronics
Daniel Commey, B. Appiah, G. S. Klogo, W. Bagyl-Bac, J. D. Gadze
arXiv:2505.02239 [cs.CR], 2025
TL;DR Benchmarks post-quantum cryptography (for example, ML-KEM) on consumer and edge hardware such as the Raspberry Pi, showing it is practical and offering concrete deployment guidance.
Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework
Daniel Commey, S. G. Hounsinou, G. V. Crosby
arXiv:2405.11580 [cs.CR], 2024
TL;DR Combines differential privacy with blockchain-verified federated learning to protect health data during collaborative training.