SHIELD LAB
Secure and Trustworthy Intelligent Systems Lab
Welcome to Dr. Shahid's SHIELD Lab @ SIUC
How can we harness AI-driven solutions for enhanced security while ensuring they remain secure, trustworthy, and privacy-preserving?
Welcome to the cybersecurity research group at the School of Computing of Southern Illinois University Carbondale. We are a dedicated lab led by Dr. Abdur Rahman Bin Shahid, focused on pushing the boundaries of innovation in security and trustworthy AI-driven solutions for privacy-enhanced Cyber-Physical Systems (CPS).
Our research spans a diverse array of innovative topics including AI, the metaverse, robotics, federated learning, the Internet of Things (IoT), mobile technologies, digital health, interdependent networks, and smart homes.
We leverage AI and cutting-edge technologies not only to advance the functionality and efficiency of CPS but also to fortify their security frameworks. Our projects aim to develop robust, privacy-enhanced environments that allow for safe and confident interaction with intelligent systems.
Through interdisciplinary collaborations with renowned experts, industry leaders, and academic partners, we cultivate a dynamic ecosystem of innovation and scholarly exchange. Our ultimate goal is to drive meaningful advancements that redefine the future of CPS, ensuring they are both powerful and protective in our increasingly digital world.
News
October 2024
Our paper has been accepted at the NeurIPS 'GenAI4Health' workshop! Congratulations to Malithi on this achievement!
September 2024
Two papers have been accepted at the IEEE ICMLA 2024 conference.
Two research papers have been accepted for poster presentations at IEEE BSN 2024.
The first paper explores the use of Large Language Models (LLMs) in detecting data poisoning attacks, while the second focuses on sponge attacks designed to increase latency and energy consumption in wearable AI devices.
May 2024
Five research papers have been accepted at The IEEE COMPSAC 2024.
Our collaborative research work has been accepted at the IEEE AI-DCS of IEEE ICDCS 2024.
March 2024
Our research on Quantifying Carbon Footprint of Adversarial Machine Learning has been accepted at GreenNet of IEEE ICC 2024.
Secure and Trustworthy Intelligent Systems (SHIELD) Lab
EGRA-409D
1230 Lincoln Dr, Carbondale, IL 62901