Compromising Industrial Processes using Web-Based Programmable Logic Controller Malware
We developed a new class of PLC malware that exclusively infects the client-side web application hosted by the embedded PLC webserver.
Researcher: Ryan Pickren
Machine Learning in Security
We propose ways to test the quality of Machine Learning applications in Cybersecurity, proposing mitigations when possible and using multiple techniques such as generative AI for software security.
Researcher: FabrÃcio Ceschin
Collaborators: Burak Sahin, Dymytriy Zyunkin
Deep Learning Based Defect Detection in Manufacturing
Our research focuses on object recognition and defect detection using deep learning in additive manufacturing. We will be using UltiMaker 3, BioAssemblyBot 400, and Mazak VC-500 AM.
Researcher: Yihan Jiang
Empirical Analysis of the Vulnerabilities on IoT Firmware
The development of IoT firmware heavily depends on third-party components (TPCs). Nevertheless, TPCs are not secure, and the vulnerabilities in TPCs will turn back influence the security of IoT firmware. We design and implement FirmSec, which leverages syntactical and control-flow graph features to detect the TPCs in firmware, and then recognizes the corresponding vulnerabilities.
Researcher: Binbin Zhao
Simple circuits in IC supply chain
We are building tools using classical learning-theory results to identify Boolean circuits that are “simple” and cannot be hidden from adversarial foundries in the logic-locking/design-hiding setting.
Researcher: Animesh Chhotaray
Security of Space Satellites
This project recognizes the unique and harsh conditions of outer space to identify new cyber-attacks specific to this domain. Initially, the primary focus will be on 5G systems.
Researcher: Daniel Khoshkhoo