Research

My research aims to design and develop security and safety techniques for secure and reliable cyber-physical systems (CPS). To this end, I incorporate interdisciplinary knowledge from program analysis, control engineering, system modeling, software testing, and AI/ML. 

CPS Attack Defense (Attack Detection) -- Physical Attack Detection via Control Invariants

Robotic vehicles (RVs) are a type of cyber-physical systems that operate in the physical world under the control of computing components in the cyber world. Despite RVs’ robustness against natural disturbances, cyber or physical attacks against RVs may lead to physical malfunction and subsequently, disruption or failure of the vehicles’ missions.

To avoid or mitigate such consequences, our research presents a novel attack detection framework to identify external, physical on the fly by deriving and monitoring Control Invariants (CI). The control invariants are determined jointly by physical attributes, control algorithm, and the laws of physics. Thus, it reflects RV's normal behaviors according to its control inputs and current physical states. Any deviation from it will be considered anomalous.

CPS Attack Defense (Attack Resilience) -- Sensor Attack Recovery via Software Sensors

Attacks on autonomous vehicles are safety-critical as it could cause physical damages or even endanger human lives. Especially, many sensor attacks have been reported recently, which fundamentally compromise the source of control. Therefore, along with the wide deployment of the autunomous vehicles, defending against such adversarial attempts are increasingly important. Unlike the traditional physical control systems, the emergence of computationally powerful Cyber-Physical Systems (CPS) allows new opportunities to deploy more complex software-based control components. Advancing the previous work, we present a novel recovery technique against various sensor attacks with so-called Software Sensors. Specifically, our technique builds a predictive state-space model for the individual sensorrs based on the generic system identification technique. Sensor measurement prediction is tailored to the individual sensors and runs as a software backup of the corresponding physical sensor. When physical sensors are under attack, the corresponding software sensors can localize and recover the compromised sensors individually and provide attack-resilient and seamless control to prevent further damage.

Secure Control Program --  CP-inconsistency Identification

Attack Investigation -- Robotic Vehicle Forensics by Replay with What-if Reasoning

Robotic vehicle (RV) attack forensics identifies root cause of an accident. Reproduction of accident and reasoning about its causation are critical steps in the process. Ideally, such investigation would be performed in real-world field tests by faithfully regenerating the environmental conditions and varying the different factors to understand causality. However, such analysis is extremely expensive and in many cases infeasible due to the difficulties of enforcing physical conditions. Existing RV forensics techniques focus on faithful accident reproduction in simulation and hence lack the support of causality reasoning. They also entail substantial overhead. We propose RVPLAYER, a system for RV forensics. It supports replay with what-if reasoning inside simulator (e.g., checking if an accident can be avoided by changing some control parameter, code, or vehicle states). It is a low-cost replacement of the expensive field test based forensics. It features an efficient demand-driven adaptive logging method capturing non-deterministic physical conditions, and a novel replay technique supporting various replay policies that selectively enable/disable information during replay for root cause analysis.

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