Systems

To reduce production cost while meeting time-to-market constraints, semiconductor companies usually design hardware systems with reusable hardware modules, popularly known as Intellectual Property (IP) blocks. Growing reliance on these hardware IPs, often gathered from untrusted third-party vendors, severely affects the security and trustworthiness of the final system. The hardware IPs acquired from external sources may come with deliberate malicious implants, undocumented interfaces working as hidden backdoor, or other integrity issues.

Secure Sockets Layer (SSL)/Transport Layer Security (TLS) protocols are critical to internet security. However, the software that implements SSL/TLS protocols is especially vulnerable to security flaws and the consequences can be disastrous. A large number of security flaws in SSL/TLS implementations (such as man-in-the-middle attacks, denial-of-service attacks, and buffer overflow attacks) result from incorrect error handling.

Embedded computing systems are found at the heart of medical devices, automotive systems, smartphone, etc. Securing these embedded systems is a significant challenge that requires new methods that address the power, time, and cost requirements under which these systems operate. Because embedded systems must meet precise time requirements, detecting changes in timing can indicate the presence of malware. This research investigates new models for capturing the expected behavior of embedded systems, in which time requirements play a pivotal role.

The United States is facing a cyber-security crisis. Recent studies predict a shortfall of 1.5M global information security jobs by 2020. The lack of qualified cyber-security workforce gives rise to high-profile security incidents. In addition, attacks against the nation's critical infrastructure can have devastating effect that go well beyond the financial losses that we are witnessing today.

Video-based traffic monitoring systems have been widely used for traffic management, incident detection, intersection control, and public safety operations. Current designs pose critical challenges. First, it relies heavily on human operators to monitor and analyze video images. Second, commercially available computer vision technologies cannot satisfactorily handle severe conditions, such as weather and glare, which significantly impair video image quality.