A comprehensive study of security vulnerabilities in information systems and the basic techniques for developing secure applications and practicing safe computing.
This graduate-level course covers security issues relating to cyber-physical systems and the Internet of Things.
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.
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