SalesTech Star Digital Transformation Institute Announces Research Awards for AI to Transform Cybersecurity and Secure Critical Infrastructure Digital Transformation Institute ( DTI) announced the third round of DTI funded advanced research awards, focused on using artificial intelligence (AI) to harden information security and secure critical infrastructure.

Read More: INTELITY Introduces 1-Click Mobile Check-In For A Seamless Guest Experience

“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)

Read More: C3.Ai Digital Transformation Institute Welcomes First International Consortium Member KTH Royal…

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)

Read More: SalesTechStar Interview with Zubin Vandrevala, VP and Head of Business and Partner Development at Gr4vy

Write in to to learn more about our exclusive editorial packages and programs.