CyLab’s Future Enterprise Security Initiative has announced its second round of funded proposals.
The 12 selected proposals will empower researchers and industry leaders to rethink security across enterprise ecosystems through innovations in artificial intelligence, computer science, engineering, and human-factors research.
Each project falls under one of the four FutureEnterprise@CyLab key research thrusts:
- AI-driven workflows to automate security management and data-driven decision-making to minimize the need for large human teams
- Collaborative capabilities for real-time global visibility for security decision making
- Foundations for understanding cyber risk and dependencies in complex ecosystems and supply chains
- Least-privilege-by-design infrastructure, including trustworthy outsourcing, remote work/management, and deployable software-defined architectures
This year, Generative AI and Large Language Models (LLMs) were added as a technology of interest in all four research thrusts.
Funding for the projects is made possible by sponsorships from Amazon Web Services, Cisco, Microsoft, Nokia Bell Labs, PNC, and the VMware University Research Fund. Sponsors actively worked with FutureEnterprise@CyLab Co-Directors Lujo Bauer and Vyas Sekar on proposal requests and reviews.
During the execution of these projects, faculty will collaborate with FutureEnterprise@CyLab sponsors to develop a suite of novel foundations and technologies, re-imagining ways to achieve security in small- and medium-sized enterprise systems.
“The Future Enterprise Security Initiative brings lots of value to CyLab because we get to benefit from sponsors’ expertise — both their technical expertise and their understanding of which problems they're struggling with most — so that we can direct our research energies towards solving the problems that really matter right now,” said Bauer.
Adversarial Robustness and Unhardening Dynamics in Federated Learning
Evaluating Large Language Models’ Privacy Risks with Privacy Attacks
Combining Program Synthesis and LLMs to Identify Code-Injection Vulnerabilities in Node.js packages
ODO: Open Dependency Observatory for Software Dependencies
Harnessing LLMs for enabling fuzzing of high-level API properties
- PI: Rohan Padhye - Assistant Professor, S3D
Conversational AI to Simplify Wireless Enterprise Security
- PI: Swarun Kumar - Associate Professor, ECE
LLM Self-Defense Against Adversarial Attacks for Coding Tasks
Least Privilege By Design
Beyond Zero Trust Architectures for Enterprise Security
- PI: Virgil Gligor - Professor, ECE
Verus: Enabling Engineers to Develop Provably Secure and Performant Software
- PI: Bryan Parno - Associate Professor, CSD and ECE
Adaptive Deployment of SDN/NFV Network Security Infrastructure with SyNAPSE
Provable and Practical Defenses against Spatial Algorithmic Complexity Attacks
- PI: Justine Sherry - Associate Professor, CSD