The growing cost of cyberattacks is a global concern and is predicted to reach $10.5 trillion in 2025. Emerging technologies and the advancements of AI further increase the complexity of interconnected and intelligent computing systems and allow for more sophisticated attacks, which in turn hinder accurate and timely threat detection and investigation. In our research, we aim to develop scalable, efficient, and robust provenance-based security mechanisms against evasive attacks in interconnected and intelligent systems.
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Projects
Provenance-based Threat Detection
Provenance-based Threat Detection
Provenance-based intrusion detection is challenged by the complexity of attacks and underlying systems. We aim to build adaptive frameworks that reduce computation while improving accuracy.
Scalable Provenance Investigation
Scalable Provenance Investigation
Provenance analysis is both storage and labor intensive, and current solutions struggle to scale with interconnected systems. To this end, we aim to build more efficient data collection and analysis frameworks.
Robust and Reliable Investigation
Robust and Reliable Investigation
AI-equipped attackers can undermine the robustness of intrusion detection by evading existing defenses. We aim to address this by studying such attackers and detecting their evasion.
5G and O-RAN Security
5G and O-RAN Security
O-RAN advances 5G networks with agile, programmable architectures, but this flexibility also increases security risks. Our research aims to improve attack investigation in such complex and dynamic environments.
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