Explore our latest research contributions to the cybersecurity community. Our publications span top-tier conferences, journals, and workshops worldwide.
This paper presents a novel deep learning framework for real-time malware detection in IoT networks, achieving 99.7% accuracy with minimal computational overhead.
We propose a new class of quantum-resistant cryptographic protocols that maintain security against both classical and quantum adversaries.
This work introduces a decentralized identity management system built on blockchain technology, providing enhanced privacy and security.
We present a graph neural network approach for detecting advanced persistent threats in enterprise networks.
This paper explores privacy-preserving techniques for machine learning in cybersecurity, enabling secure collaborative threat intelligence.
We propose an automated framework for comprehensive vulnerability assessment of smart contracts using symbolic execution.