AI-infused security: Robust defense by bridging theory and practice

While Artificial Intelligence (AI) has tremendous potential as a defense against real-world cybersecurity threats, understanding the capabilities and robustness of AI remains a fundamental challenge. This dissertation tackles problems essential to successful deployment of AI in security settings and is comprised of the following three interrelated research thrusts. (1) Adversarial Attack and Defense of Deep Neural Networks: We discover vulnerabilities of deep neural networks in real-world settings and the countermeasures to mitigate the threat. We develop ShapeShifter, the first targeted physical adversarial attack that fools state-of-the-art object detectors. For defenses, we develop SHIELD, an efficient defense leveraging stochastic image compression, and UnMask, a knowledge-based adversarial detection and defense framework. (2) Theoretically Principled Defense via Game Theory and ML: We develop new theories that guide defense resources allocation to guard against unexpected attacks and catastrophic events, using a novel online decision-making framework that compels players to employ ``diversified'' mixed strategies. Furthermore, by leveraging the deep connection between game theory and boosting, we develop a communication-efficient distributed boosting algorithm with strong theoretical guarantees in the agnostic learning setting. (3) Using AI to Protect Enterprise and Society: We show how AI can be used in real enterprise environment with a novel framework called Virtual Product that predicts potential enterprise cyber threats. Beyond cybersecurity, we also develop the Firebird framework to help municipal fire departments prioritize fire inspections. Our work has made multiple important contributions to both theory and practice: our distributed boosting algorithm solved an open problem of distributed learning; ShaperShifter motivated a new DARPA program (GARD); Virtual Product led to two patents; and Firebird was highlighted by National Fire Protection Association as a best practice for using data to inform fire inspections.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Computational Science and Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Chen, Shang-Tse

Advisors dc:contributor.advisor

• Chau, Duen Horng
• Balcan, Maria-Florina

Committee members dc:contributor.committeemember

• Lee, Wenke
• Song, Le
• Roundy, Kevin A.
• Cornelius, Cory

• Security
• Cybersecurity
• Machine learning
• Artificial Intelligence
• Adversarial machine learning
• Game theory
• Boosting
• Fire risk

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1853/62296

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/62296