CSIS Doctor of Philosophy Dissertation Defense by Gaspard Baye
Date: Wednesday, May 7, 2025
Time: 10am
Location: DION 311
Microsoft Teams Link: Join Remotely via Microsoft Teams
Abstract:
Network intrusion detection is a foundational pillar of cybersecurity, essential for safeguarding digital infrastructures against both internal and external threats. While conventional Intrusion Detection Systems (IDS) perform well against known attack signatures, they fall short when confronted with previously unseen or zero-day threats-- exposing a critical vulnerability in real-world deployment. To address this, we propose OpenLLM, a generative multi-agent framework designed specifically for Open-World Intrusion Detection. OpenLLM integrates modified generative transformers into a multi-agent architecture, enabling the system to detect both known and novel intrusions in dynamic environments. This approach represents a shift from static signature-based detection to a more adaptive, open-set recognition paradigm capable of proactively identifying emerging threats.
This dissertation advances the state of the art through five key contributions: (i) a comprehensive evaluation of current deep learning-based Open Set Recognition (OSR) models in the context of network intrusion, revealing critical performance gaps; (ii) the development of varMax, a robust open-set classifier tailored to high-dimensional network data; (iii) the introduction of UPacketLabel, an enhanced transformer-based IDS equipped with LLM-driven explainability for interpreting unknown threats; (iv) implementation of PacketGuard, adversarial defense mechanisms and test-time resilience techniques; and (v) the integration of an Adversarial Risk Assessment (ARA) framework for quantifying OSR model risk under adversarial attack scenarios. Experimental results show that OpenLLM is a forward-looking solution capable of securing networks in adversarial and evolving threat landscapes.
Advisor: Dr. Gokhan Kul, Assistant Professor, Dept. of Computer & Information Science, UMASS Dartmouth
Committee members:
Dr. Lance Fiondella, Associate Professor, Dept. of Electrical & Computer Engineering, UMASS Dartmouth
Dr. Jiawei Yuan, Assistant Professor, College of Computer and Information Science, UMASS Dartmouth
Dr. Ming Shao Associate Professor, Miner School of Computer and Information Science, UMASS Lowell
Dr. Long Jiao, Assistant Professor, College of Computer and Information Science, UMASS Dartmouth
Note: All EAS Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
*For further information, please contact Dr. Gokhan Kul at 508-910-6484 or via email
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