Elisa Bertino
3 indexed papers
Publications per year
Top categories
Frequent co-authors
Research Timeline
The paper proposes a universal robustification framework to enhance drift-adaptive malware detectors against combined concept drift and adversarial attacks, significantly reducing attack success rates across various threat models.
This paper provides a comprehensive, system-level taxonomy for designing quantum-resistant network architectures, moving beyond simple protocol substitutions to address key distribution and management challenges across diverse deployment environments.
The paper proposes WARDEN, a distributionally robust adversarial training framework that significantly reduces LLM vulnerability to adversarial attacks by dynamically reweighting hard adversarial examples within a divergence ball.
Papers
Information Theoretic Adversarial Training of Large Language Models
Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi, Rouzbeh Behnia +2 more
The paper proposes WARDEN, a distributionally robust adversarial training framework that significantly reduces LLM vulnerability to adversarial attacks by dynamically reweighting hard adversarial exam…