Building secure, privacy-preserving, and intelligent systems at the intersection of decentralized cybersecurity and artificial intelligence to protect critical infrastructures.
Explore ResearchOur work spans critical areas of cybersecurity and Artificial Intelligence, focusing on building robust, privacy-preserving systems for healthcare, industrial IoT, and smart environments.
Designing closed-loop autonomous cybersecurity systems that continuously perceive threats, reason under uncertainty, and take adaptive defensive actions with minimal human intervention.
Studying the security, reliability, and controllability of agentic and autonomous AI systems, including adversarial manipulation, unsafe emergent behavior, verification, and deployment-time guarantees.
Developing federated and decentralized learning systems that enable cross-organizational collaboration while addressing trust, robustness, auditability, and adversarial behavior.
Designing privacy-preserving learning frameworks with formal guarantees for sensitive domains, integrating differential privacy, secure computation, and cryptographic safeguards into real-world AI systems.
Protecting cyber-physical and industrial IoT systems through AI-driven detection, adaptive defense, and resilient control mechanisms for critical infrastructure.
Integrating AI-driven threat intelligence with governance frameworks that support explainability, accountability, and coordinated decision-making in security operations.
Studying the security and trustworthiness of blockchain-enabled autonomous agents in Web3 ecosystems, focusing on adversarial behavior, economic manipulation, and governance.
The Decentralized Cybersecurity and Artificial Intelligence Lab (DCAILab) is dedicated to advancing the frontiers of secure and intelligent systems. Our research combines cutting-edge AI techniques with robust cybersecurity frameworks to address the most pressing challenges in protecting critical infrastructures.
We focus on developing privacy-preserving machine learning algorithms, secure federated learning systems, and autonomous cybersecurity solutions that can defend against sophisticated threats while maintaining user privacy and system integrity.
Our interdisciplinary approach brings together expertise in artificial intelligence, cryptography, distributed systems, and cybersecurity to create innovative solutions for healthcare, industrial IoT, smart cities, and emerging Web3 technologies.
Our diverse team of researchers, engineers, and students working together to advance cybersecurity and AI.
Explore our latest research contributions to the fields of cybersecurity and artificial intelligence.
Loading publications...
Invited talks, panels, and public engagement activities by DCAILab.
We are recruiting highly motivated students to work on cutting-edge research in Agentic AI, Federated Learning, and Cybersecurity for Critical Infrastructure.
Research topics include Agentic AI security, privacy-preserving machine learning, federated unlearning, and AI governance for critical infrastructure systems.
Work on applied AI-driven cybersecurity projects and contribute to real-world systems involving IoT, smart grids, and decentralized AI.
Recent highlights including publications, invited talks, workshops, grants, and student achievements.
We are currently preparing recent announcements and activities from DCAILab. Please check back soon for updates.
Interested in collaboration, joining our team, or learning more about our research?
University Campus
Research Building, Room 301
We welcome graduate students, postdocs, and visiting researchers.