The team is analyzing how deep learning and sequence modeling can be applied to network telemetry to identify polymorphic malware and zero-day exploits. Our lab seeks to design predictive defense architectures capable of orchestrating dynamic, automated incident response.
- Zero-Day Anomaly Detection: Researching unsupervised deep learning models to identify statistically anomalous behaviors indicative of unseen network intrusions.
- Automated Malware Analysis: Investigating the use of graph neural networks to dynamically classify and deconstruct obfuscated malicious binaries.
- Predictive Threat Intelligence: Exploring how specialized Large Language Models (LLMs) can synthesize and forecast attack vectors from global security telemetry.
- Autonomous Incident Response: Conceptualizing reinforcement learning agents intended to execute real-time countermeasures to isolate compromised network segments.
