Paul J Bonczek
I am currently a Ph.D. candidate 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].
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 webpage. This work has recently been accepted to the Transactions on Robotics (T-RO) and will be included in an upcoming Special Issue on Resilience in Networked Robotic Systems. To learn more about our framework, the details may be found in our paper [TRO '21].
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.
Cooperative Recovery for Safe Multi-robot Operations
Our current work focuses on maintaining resilience in multi-robot systems in the scenario of cyber-attacks and/or faults occur on individual robots within the formation. Just a single compromised robot can degrade the swarms control performance, and potentially cause devastating effects. We propose to leverage a hidden signal-based alerting mechanism to notify neighboring robots of the impending attack/fault. The uncompromised neighbors then behave in a cooperative manner to aid in re-localization of the robot to maintain desired control performance of the entire swarm.
We are currently preparing this work for submission to the Robotics and Automation Letters (RAL) in the Spring of '22.
Recovery of Autonomous Systems Operating During On-board Controller Attacks
Cyber attacks, failures, and implementation errors inside of an on-board controller of an autonomous system can affect its correct behavior, which can lead to unsafe states and degraded performance. For example, a scenario may occur when cyber attacks manipulate controller feedback gain parameters or block control inputs to trigger an undesired behavior of the system based on state and reference signal information provided to the controller. In this work, we seek to detect the compromised regions within the information provided to the controller and then design a recovery method to counteract the malicious effects to the controller to allow for resilient operations such that a robot can maintain desired control performance.
We have submitted this work to IROS '22 and it is currently under review. To see our simulation and experiment results, visit our webpage for this paper.
Outside of work, I enjoy cooking, hiking, playing/watching sports, fantasy football, and learning about WWII history.