C3.ai Digital Transformation Institute (C3.ai DTI) announced the third round of C3.ai DTI funded advanced research awards, focused on using artificial intelligence (AI) to harden information security and secure critical infrastructure.
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“Scalable, Secure Machine Learning in the Presence of Adversaries”
The Institute awarded a total of $6.5 million in cash awards to leading research scientists at University of California, Berkeley, University of Illinois at Urbana-Champaign, Carnegie Mellon, Princeton, University of Chicago, KTH Royal Institute of Technology, and MIT.
“Cybersecurity is an immediate existential issue,” said Thomas M. Siebel, chairman and CEO of C3 AI, a leading enterprise AI software provider. “We are equipping top scientists with the means to advance technology to help secure critical infrastructure.”
Twenty-four projects were awarded $100,000 to $700,000 each, for an initial period of one year:
AI Resilience: Techniques and methods to enable the development of AI algorithms that are resilient to adversarial attacks
- “High Performance Provably Robust AI Methods for Cybersecurity Tasks on Critical Infrastructure,” (Zico Kolter, Carnegie Mellon University)
- “Scalable, Secure Machine Learning in the Presence of Adversaries,” (John Kubiatowicz, University of California, Berkeley)
- “REFL: Resilient Distributed Cybersecurity Learning System,” (Bo Li, University of Illinois at Urbana-Champaign)
- “Fundamental Limits on the Robustness of Supervised Machine Learning Algorithms,” (Ben Zhao, University of Chicago)
Anomaly Detection: AI techniques, including supervised and unsupervised learning, to provide early detection of system and/or network anomalies that might be indicative of unauthorized access, denial of service, or data exfiltration
- “Continuously and Automatically Discovering and Remediating Internet-Facing Security Vulnerabilities,” (Nick Feamster, University of Chicago)
- “AI Techniques for Power Systems Under Cyberattacks,” (Javad Lavaei, University of California, Berkeley)
- “Physics-aware AI-based Approach for Cyber Intrusion Detection in Substation Automation Systems,” (Alberto Sangiovanni-Vincentelli, University of California, Berkeley)
Advanced Persistent Threats: AI techniques to detect the presence of advanced persistent threats
- “Deep-Learning Detection Algorithms for Advanced Persistent Attacks in Mixed-Autonomy Traffic: Design and Experimental Validation,” (Alex Bayen, University of California, Berkeley)
- “AI Support for Cybersecurity,” (David Wagner, University of California, Berkeley)
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Securing Critical Cyber-Physical Infrastructure: AI techniques to secure critical infrastructure against cyber threats
- “Cyber Safety Cage for Networks,” (Cyrille Valentin Artho, KTH Royal Institute of Technology)
- “Security for Large-Scale Infrastructure using Probabilistic Programming,” (Nikita Borisov, University of Illinois at Urbana-Champaign)
- “A Compositional Neural Certificate Framework for Securing Critical Networked Infrastructure,” (Chuchu Fan, Massachusetts Institute of Technology)
- “Democratizing AI-Driven Security Workflows for Critical Energy Infrastructure,” (Vyas Sekar, Carnegie Mellon University)
- “Semantic Adversarial Analysis for Secure Critical Infrastructure,” (Sanjit Seshia, University of California, Berkeley)
Forensics: AI forensics and attribution techniques to identify sources of attacks
- “Causal Reasoning for Real-Time Attack Identification in Cyber-Physical Systems,” (György Dán, KTH Royal Institute of Technology)
- “Statistical Learning Theory and Graph Neural Networks for Identifying Attack Sources,” (H. Vincent Poor, Princeton University)
- “Robust and Scalable Forensics for Deep Neural Networks,” (Ben Zhao, University of Chicago)
Securing Emerging Financial Infrastructure: AI techniques to identify attacks on emerging decentralized financial and business infrastructure
- “An Intelligence Platform for Better Security in Decentralized Finance,” (Dawn Song, University of California, Berkeley)
- “Blockchain Forensics,” (Pramod Viswanath, University of Illinois at Urbana-Champaign)
Vulnerability Identification: AI techniques to identify previously unknown malware, ransomware, and zero-day vulnerabilities, enabling isolation and neutralization
- “GAN-Aided Automatic Test Case Generation,” (Giulia Fanti, Carnegie Mellon University)
- “Machine Learning for JavaScript Vulnerability Detection,” (Corina Pasareanu, Carnegie Mellon University)
Insider Threats: Change management techniques to prevent the weaponization of innocent and malicious insiders
- “Protecting Critical Infrastructures Against Evolving Insider Threats,” (Carl Gunter, University of Illinois at Urbana-Champaign)
- “Multi-Facet Rare Event Modeling of Adaptive Insider Threats,” (Jingrui He, University of Illinois at Urbana-Champaign)
- “AI-Supported Nudging for Cyber-Hygiene,” (Cedric Langbort, University of Illinois at Urbana-Champaign)
