Division of Computer and Network Systems (CNS)

Transportation systems are being profoundly transformed with the emergence of a series of software-based smart transportation solutions such as intelligent traffic signal control and autonomous driving. In these systems, the key enabler of their functional intelligence is the sensing capability, which collects necessary road information to enable better control decisions. However, sensor data are collected from a public channel, i.e., the physical transportation environment, which thus inevitably creates opportunities for attackers to tamper with the sensing process.

Mobile users hold people's sensitive information such as passwords, locations, and health information. Users are permitted to control the use of some of this information by configuring their privacy settings in the apps they use. These settings, however, are often difficult to locate and understand, even in popular apps such as Facebook. Moreover, the settings are often set to share user data by default, exposing personal data without users' explicit consent.

Modern high-performance processors implement complex microarchitectural optimizations involving speculative execution which has recently been shown to be vulnerable to a type of malicious attack called Spectre. This project will investigate a microarchitectural solution framework to secure against such attacks. This framework, called context-sensitive fencing, will seek to automatically track and detect malicious execution patterns dynamically to trigger defense code without programmer intervention and with minimal impact on processor performance.

Cyber attackers are increasingly targeting emerging smart devices (e.g., Internet of Things devices) causing devastating damages to various enterprises and government agencies. To combat these attacks, rapid and effective investigation is critical to understand attack paths and measure the damages. Unfortunately, forensic logging infrastructures are not efficient and effective enough. Many devices completely lack forensic logging systems and others rely on ineffective logging schemes, delaying or often completely preventing forensic investigation.

Control-related attacks are a severe threat to cyber-physical systems (CPSs) such as smart grids, because they can introduce catastrophic physical damage by using malicious control commands crafted in a legitimate format. While current research efforts have focused on detecting malicious commands that lead to physical damage, the investigator proposes to preemptively prevent the damage by disrupting and misleading adversaries' preparation before they issue the malicious commands.