The Trusted Things project develops new software systems to enable secure IoT leveraging trusted execution environment techniques.
The Shear project creates a secure environment for the least-authority execution of over-privileged applications that may be exploited by adversaries to launch privileged attacks.
The RetroFirm project focuses on analyzing and modifying the off-the-shelf firmware binary images of various cyber-physical and IoT systems to enhance their security.
The PeriShield project analyzes the security of various types of peripheral devices and it develops cutting-edge tools to detect/prevent malicious peripherals.
The AI Vault project designs and develops a new trusted execution environment tailored to run artificial intelligence and machine learning programs on modern AI platforms (e.g., cloud and embedded devices) while providing strong data confidentiality and high efficiency.
The AutoInsight project applies advanced techniques in software security and control systems to build a new security analysis platform for self-driving car systems.
Robotic vehicles (as known as drones) are facing various threats of cyber-physical attacks that exploit their security vulnerabilities. RetroV develops automated analysis tools to find such vulnerabilities in existing robotic vehicle systems retrospectively and retrofits their design against advanced cyber-physical attacks.
The CLUE project develops an infrastructure to detect and diagnose system anomalies in enterprise systems. These anomalies include stealthy malware and other types of hidden system anomalies. CLUE provides a diverse set of tools to find and understand such anomalies with minimal disruption to the target system.
Cloud Application Function Enclave (CAFE) is an end-to-end framework for confidential distribution and execution of cloud applications. Attackers with a reverse-engineering capability may steal or manipulate sensitive application logic. CAFE prevents such attempts using hypervisor- and hardware-based techniques.