Temporal and Multimodal Deep Learning for Cyberattack Detection in LEO Satellite Systems
Combining hardware, orbital, and radio-frequency telemetry to detect satellite cyberattacks across time.
I develop data-efficient, robust artificial intelligence for cybersecurity, with an emphasis on systems that generalize to unseen threats and continue adapting as attack behavior changes. My work spans transformer-based packet modeling, few-shot and continual learning, vision-language models, and backdoor defense.
I am open to collaborations with researchers at other universities on computer vision, the detection and mitigation of adversarial and backdoor attacks, continual and few-shot learning, and related AI × cybersecurity applications.
Combining hardware, orbital, and radio-frequency telemetry to detect satellite cyberattacks across time.
My research centers on robust learning under distribution shift, limited labels, emerging attack behavior, and changing operational environments.
Transformer-based modeling of network traffic and cyber behavior for malware detection, threat recognition, and adaptive defense.
Parameter-efficient adaptation that learns new threats from limited examples while preserving previously acquired knowledge.
Compositional reasoning, prompt-based adaptation, and open-world defenses for detecting previously unseen backdoor behavior.
Kyle Stein, Guillermo Francia III, Eman El-Sheikh, Andrew Arash Mahyari
arXiv preprint · arXiv:2608.23536
Kyle Stein, Andrew Arash Mahyari, Guillermo Francia III, Eman El-Sheikh
IEEE/CVF International Conference on Computer Vision (ICCV) Workshops
Kyle Stein, Guillermo Francia III, Eman El-Sheikh, Andrew Arash Mahyari
IEEE Access
Kyle Stein, Andrew A. Mahyari, Guillermo Francia III, Eman El-Sheikh
IEEE International Conference on Image Processing (ICIP)