Explore peer-reviewed papers, technical reports, and whitepapers published by the CyberVault Research Division. Our work advances AI-driven threat detection, zero-trust architectures, and next-generation cryptographic protocols.
We propose a dynamic graph neural network architecture that models lateral movement patterns in enterprise environments. Our framework achieves 99.4% accuracy in detecting Advanced Persistent Threats with sub-second latency, outperforming static baseline models by 18.2%.
This paper introduces ZK-CloudVerify, a protocol enabling continuous integrity checks across hybrid cloud deployments without exposing raw telemetry. We demonstrate 60% lower computational overhead compared to traditional hash-chain methods while maintaining cryptographic assurance.
Traditional MFA creates friction and blind spots between authentications. We present a continuous verification model leveraging keyboard dynamics, cursor trajectories, and session entropy to maintain trust scores without interrupting user workflows.
We train a multi-agent RL system to simulate security operations center workflows. The agents learn optimal triage, isolation, and remediation sequences from historical breach data, reducing mean time to containment (MTTC) by 63% in controlled enterprise simulations.
We design a lattice-based key encapsulation mechanism optimized for constrained IoT devices. Our protocol achieves quantum-resistant security with 40% smaller ciphertexts and 2.3x faster handshakes compared to CRYSTALS-Kyber baseline implementations.
We investigate data poisoning vectors targeting ML-based IDS systems and propose a robust training pipeline incorporating differential privacy and anomaly-aware weighting. The method maintains 96% F1-score under heavy attack scenarios while standard models degrade to 68%.