Defending next generation mobile and IoT devices
Research Issues
Mobile malware detection techniques; Intrusion detection and protection for embedded systems; Side-channels and covert-channels in mobile and IoT devices, detection and mitigation strategies; Threat analysis and protection strategies for Cyber-Physical Systems.
Research Areas
Mobile computing security; IoT security; Cyber-Physical System security; Trust-oriented embedded system design; Deep learning-based system diagnosis; Security and privacy of emerging devices.
Majors Preparation and Interests
Electrical Engineering – Signal Processing, Machine Learning, Control theory; Computer Engineering – Embedded Systems, Computer Architecture, Mobile Devices, Cybersecurity; Computer Science – Neural Networks, Algorithms, Complexity Analysis, Programming/Software; Mechanical Engineering – Cyber-Physical Systems, Robotics, Dynamics, Control systems; Mathematics – coding, algorithms, complexity analysis.
Key Elements
Programming of mobile, IoT, and embedded devices, Signal processing, Deep learning, Neural networks, Hardware Performance Counters (HPC), Differential power attacks, Energy harvesting devices.
Goals
Internet-of-Things (IoT) is a global information network that allows different smart devices (or “things”) to collect and exchange data seamlessly. What comes with this trend, however, is an Internet-of-Threats: sophisticated attacks can be initiated from any IoT device over the Internet. The fact that these devices are widely deployed in critical applications, such as smart manufacturing, transportation and healthcare, also makes them “ideal” targets for attackers to cause serious damage. This VIP team centers on the study of various cybersecurity threats coming with the global information network, as well as defense of mobile phones, IoT, and embedded devices against these cyberattacks. The team will be working actively on intrusion detection, malware detection and side-channel attacks in mobile phones and embedded devices. The team will collect both digital signals (e.g., hardware performance counters) and analog signals (e.g., dynamic power traces, electromagnetic signals) from the target devices, and adopt signal processing and deep learning techniques for filtering, enhancing, and classifying the obtained signals. The team will also study the security and privacy challenges in energy harvesting devices built with emerging technologies.
Faculty
Chengmo Yang, PhD, Professor, ECE, chengmo@udel.edu
