Paul J Bonczek
I am currently a Ph.D. student in Electrical Engineering at the University of Virginia. I work in the Autonomous Mobile Robots (AMR) Lab and Link Lab under the advisement of Prof. Nicola Bezzo. Before joining AMR Lab, I obtained my B.S. degree dual majoring in Electrical & Computer Engineering and Applied Mathematics at the State University of New York (SUNY) Polytechnic Institute in 2016.
My research focuses on cyber-physical system cyber-security, resilient control and planning of single- and multi-vehicle autonomous systems, run-time attack detection algorithms, and adaptive control systems.
Monitoring for Non-Randomness Behavior in Residual-based Detection Schemes
Today's autonomous cyber-physical systems are fitted with multiple on-board sensors and computers, allowing for increased capabilities in various applications to perform complex tasks. Consequently, these enhancements increase the risk of cyber attackers to spoofing attacks that can compromise system integrity and safety. With a growing number of vulnerable entry points for attackers on progressively impactful systems within our society, it is imperative to develop more strict security measures to ensure proper performance.
Intelligent attackers are able to implement stealthy attack sequences that hide within system uncertainties in order to remain hidden from typical state-of-the-art detection techniques. However, to effectively hijack a system, an attack must create behavior anomalies (i.e., non-randomness) that contradict the known system model(s). We develop attack detectors that are able to discover non-random events due to sensor cyber-attacks in residual-based detection schemes. With our proposed attack detectors implemented on-board a system, we show that previously undetectable attacks can now be found. More details about our approaches with simulation and experiment results can be found in our papers. [ACC '20], [IFAC '20], [ACC '21 (to appear)].
Detection of Non-Random Behavior for Resilient Coordination of Robotic Swarms
Within robotics, multi-agent system coordination and swarming have long been studied, but have regained consideration attention to consider safety and security issues. Most multi-agent applications are designed without considering cyber-security issues, which perform operations with the assumption that all agents are cooperating. We propose a monitoring framework for multi-agent systems that detects and isolates compromised vehicles to enable the swarm to resiliently continue operations in the presence of stealthy communications and sensor attacks. Additional simulation and experiment results of our resilient multi-agent framework can be found in the following link.
Detection and Inference of Randomness-based Behavior for Resilient Multi-vehicle Coordinated Operations
Generally, applications that leverage multi-vehicle networks assume that all vehicles are cooperative while performing desired operations to maintain swarming formations and can exchange all necessary information to complete a task. However, these vehicles are susceptible to malicious external cyber-attacks that can affect system performance or intercept safety-critical information. We propose to utilize a side-channel information exchange that contains hidden data for vehicles in the network to detect that is unknown to potential attackers.
Virtual spring-damper meshes for proximity-based formation control are leveraged for both detection of misbehaving vehicles and also any hidden signatures that are being passed from nearby vehicles. In our work, we apply our framework to applications where mobile robotic teams relay safety-critical information concerning any discovered object of interest to the remaining vehicles by utilizing hidden signatures to maintain secrecy from potential attackers.
Outside of work, I enjoy cooking, hiking, playing/watching sports, fantasy football, and learning about WWII history.