@inproceedings{dangEMAuthGeneralizableEyeMovement2026,

title = {EM-Auth: A Generalizable Eye-Movement Authentication Framework using Event Cameras},

author = {L. B. Dang and V. Tuan Truong and L. B. Le and T. H. Falk},

url = {https://www.scopus.com/pages/publications/105042114237?origin=resultslist},

doi = {10.1109/CAI68641.2026.11536355},

isbn = {979-833156039-3 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {IEEE Conf. Artif. Intell., CAI},

pages = {2064–2069},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {With the rapid emergence of virtual and augmented reality (VR/AR) head-mounted devices that closely interact with users' eyes, eye-tracking-based applications are becoming increasingly promising. Among them, eye movement patterns have emerged as a reliable biometric modality for user authentication. However, existing solutions based on conventional frame-based cameras face significant limitations, including low temporal resolution, high latency, high power consumption, and potential privacy concerns due to the detailed appearance captured around the eyes. To address these challenges, we propose EM-Auth, a novel framework for eye-tracking-based user authentication that leverages event cameras, a type of neuromorphic vision sensor providing asynchronous, high-temporal-resolution data, while preserving user privacy. Our method introduces a two-stage spatiotemporal feature extraction pipeline: first, a lightweight convolutional neural network (CNN) extracts spatial features from event streams; second, a transformer-based module models the temporal dependencies to generate robust eye representations. Extensive experiments show that our framework significantly outperforms existing state-of-the-art (SOTA) methods, achieving 0.008 and 0.015 equal error rates (EERs) on two representative datasets. Notably, EM-Auth demonstrates a strong generalization capability. In a zero-shot setting where the model is trained on a specific dataset and evaluated on previously unseen users, our approach achieves authentication accuracies of 98.4% and 95.9% on the EV-Eye and EBV-Eye datasets, respectively. Under cross-dataset evaluation, our framework achieves an authentication accuracy of nearly 85% without additional fine-tuning, and consistently improves performance through transfer learning. © 2026 IEEE.},

note = {Journal Abbreviation: IEEE Conf. Artif. Intell., CAI},

keywords = {augmented reality, Authentication, Cameras, Convolutional neural networks, Data privacy, Eye movement patterns, Eye movements, eye tracking, Eye-tracking, Frame-based, High power consumption, Neuromorphic vision sensors, Power potential, Privacy concerns, Temporal resolution, Transfer learning, User authentication, Virtual and augmented reality, virtual reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mehrbanBlockchainEnabledMultiLayeredZeroTrust2025,

title = {A Blockchain-Enabled Multi-Layered Zero-Trust Security Framework for O-RAN},

author = {A. Mehrban and Z. A. El Houda and H. Moudoud and B. Brik and L. Khoukhi},

url = {https://www.scopus.com/pages/publications/105011345211?origin=resultslist},

doi = {10.1109/IWCMC65282.2025.11059720},

isbn = {979-833150887-6 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int. Wirel. Commun. Mob. Comput. Conf., IWCMC},

pages = {1564–1569},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {O-RAN (Open Radio Access Network) is a set of open and interoperable radio access technologies, guided by the O-RAN Alliance, that, despite an open ecosystem, introduces significant security risks, expanding the threat surface in 6G networks. Traditional perimeter-based security approaches are inadequate for O-RAN's highly distributed, multi-vendor environments, where Zero Trust Architecture (ZTA) becomes essential for robust security. To address these challenges, we propose a novel blockchain-based, decentralized Zero-Trust Framework specifically designed for O-RAN security. Our proposed framework comprises two key layers: the first layer utilizes Federated Learning (FL) and Transfer Learning (TL) for advanced attack detection, enabling distributed, privacy-preserving threat analysis across O-RAN nodes. The second layer enforces Zero Trust access control through a blockchain-based identity management system, ensuring tamper-resistant, real-time policy updates. This multi-layered framework provides adaptive threat detection and resilient access control, validated through simulations demonstrating high detection accuracy and robust access management with minimal impact on network performance, offering a scalable security solution for next-generation O-RAN deployments. © 2025 IEEE.},

note = {Journal Abbreviation: Int. Wirel. Commun. Mob. Comput. Conf., IWCMC},

keywords = {Access control, Block-chain, Blockchain, Data privacy, Federated learning, federated learning (FL), Internet of thing, Internet of things, Internet of Things (IoT), Interoperability, Learning systems, Mobile telecommunication systems, Multi-layered, Network architecture, Network security, Open access, Open radio access network, open radio access network (O-RAN), Radio, Radio access networks, Radio access technologies, Radio communication, Secure communication, Security, Security frameworks, Security risks, Transfer learning, Trusted computing},

pubstate = {published},

tppubtype = {inproceedings}

}