7 citations · 23 across the 12 of their papers we have counts for
14 papers
Names Are Not Objects: Object-Bound Confinement for Compromised AI Coding Agents
Daniel Commey
Coding agents read repositories and web pages that anyone can write, and then act with their user's authority. Prompt-injection defenses make it less likely that the model is steer…
Post-Quantum Session Establishment for Matter over Thread: Message Size, Transport, and Protocol Options
Daniel Commey
Matter's first post-quantum change protects device attestation with ML-DSA, but its operational handshake, CASE, still derives session keys from P-256 ECDH, so recorded traffic can…
Who Does Withholding Delay? A Welfare Model of Open-Weight AI Release Under Asymmetric Proliferation
Daniel Commey
Withholding a dual-use AI model delays only the actors that lack other routes to a comparable capability. If sophisticated adversaries obtain substitutes faster than distributed de…
Security-Induced Braess Paradoxes in Service Function Chain Orchestration
Daniel Commey, Bin Mai
NFV/SDN orchestration lets operators instantiate and steer traffic through virtual firewalls, IDS/IPS replicas, WAF clusters, zero-trust gateways, backup inspection paths, and migr…
FedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Laundering
Daniel Commey, Matilda Nkoom, Yousef Alsenani +2
Virtual Asset Service Providers (VASPs) face a fundamental tension between regulatory compliance and user privacy when detecting cross-institutional money laundering. Current appro…
FedSkipTwin: Digital-Twin-Guided Client Skipping for Communication-Efficient Federated Learning
Daniel Commey, Kamel Abbad, Garth V. Crosby +1
Communication overhead remains a primary bottleneck in federated learning (FL), particularly for applications involving mobile and IoT devices with constrained bandwidth. This work…
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs
Daniel Commey, Benjamin Appiah, Griffith S. Klogo +1
Federated Learning (FL) enables collaborative model training on decentralized data without exposing raw data. However, the evaluation phase in FL may leak sensitive information thr…
A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning
Daniel Commey, Rebecca A. Sarpong, Griffith S. Klogo +2
Federated learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, its open-participation nature exposes it to data-p…
Performance Analysis and Deployment Considerations of Post-Quantum Cryptography for Consumer Electronics
Daniel Commey, Benjamin Appiah, Griffith S. Klogo +4
Quantum computing threatens the security foundations of consumer electronics (CE). Preparing the diverse CE ecosystem, particularly resource-constrained devices, for the post-quant…
PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework
Daniel Commey, Garth V. Crosby
Federated Learning (FL) enables collaborative model training while preserving data privacy, but its classical cryptographic underpinnings are vulnerable to quantum attacks. This vu…
Strategic Deployment of Honeypots in Blockchain-based IoT Systems
Daniel Commey, Sena Hounsinou, Garth V. Crosby
This paper addresses the challenge of enhancing cybersecurity in Blockchain-based Internet of Things (BIoTs) systems, which are increasingly vulnerable to sophisticated cyberattack…
Securing Blockchain-based IoT Systems with Physical Unclonable Functions and Zero-Knowledge Proofs
Daniel Commey, Sena Hounsinou, Garth V. Crosby
This paper presents a framework for securing blockchain-based IoT systems by integrating Physical Unclonable Functions (PUFs) and Zero-Knowledge Proofs (ZKPs) within a Hyperledger…
EGAN: Evolutional GAN for Ransomware Evasion
Daniel Commey, Benjamin Appiah, Bill K. Frimpong +3
Adversarial Training is a proven defense strategy against adversarial malware. However, generating adversarial malware samples for this type of training presents a challenge becaus…
Securing Health Data on the Blockchain: A Differential Privacy and Federated Learning Framework
Daniel Commey, Sena Hounsinou, Garth V. Crosby
This study proposes a framework to enhance privacy in Blockchain-based Internet of Things (BIoT) systems used in the healthcare sector. The framework addresses the challenge of lev…