Abraham Itzhak Weinberg

The paper introduces ARCANE, a Bayesian network framework for cross-campaign cyber attribution, finding that while aggregating telemetry improves identification, structural feature limitations prevent complete resolution of ambiguity between distinct sophisticated adversaries.

PHANTOM is a novel framework that generates highly convincing, context-aware honeytokens by incorporating deep organizational knowledge, significantly improving their believability and detection resistance compared to static templates.

ORCHID introduces a novel, bio-inspired consensus protocol that uses quantum-noisy phase oscillators and a binding threshold derived from neuroscience to achieve scalable, high-fidelity consensus in distributed ledgers.

CLOUDBURST introduces a novel framework and taxonomy for passive cloud-native beacons, demonstrating that IAM Canary Roles are the most effective vector for real-time threat attribution in modern cloud environments.