I am a recent Ph.D. graduate of the Autonomous Mobile Robots (AMR) Lab in December 2022 while a part of the Electrical Engineering department. Before joining the AMR Lab, I obtained my B.S. degree while double majoring in Electrical & Computer Engineering and Applied Mathematics at the State University of New York (SUNY) Polytechnic Institute in 2016.
My research in the AMR Lab focused on cyber-physical system cyber security, resilient control and planning of autonomous single- and multi-robot systems, resilient state estimation, and randomness-based run-time attack detection algorithms.
Since graduating from the University of Virginia, I have joined the Networked Systems and Integrated Fires Group (A4B) within the Air and Missile Defense Sector at The Johns Hopkins University Applied Physics Laboratory.
Monitoring for Non-Randomness Behavior in Residual-based Detection Schemes for Discovery of Stealthy Sensor Attacks
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 been published in the IEEE Transactions on Robotics (T-RO) within the Special Issue on Resilience in Networked Robotic Systems. To learn more about our framework, the details may be found in our paper [TRO '22].
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 Multi-robot Operations under Loss of Localization
Our recent work focuses on maintaining resilience in multi-robot systems when cyber attacks or faults occur to on-board positioning sensors on individual robots. A single compromised robot can degrade the swarm's formation control performance and potentially cause devastating effects. Our framework allows neighboring robots to cooperatively recover compromised robots.
In our previous work (T-RO '22), compromised agents within the swarm were isolated and removed from the control network to ensure the system can resiliently maintain operations. However, isolated robots that lose localization capabilities are left behind by the remaining agents. In this work, we propose a framework for neighboring robots to cooperatively aid in re-localization of compromised agents. We propose to leverage a hidden randomness-based signature to notify neighboring robots of the impending attack/fault. The uncompromised neighbors then behave in a cooperative manner to aid in re-localization to save the compromised agent, thus allowing all robots to continue the desired operation.
This work will be submitted to the 2024 IEEE American Control Conference (ACC) for peer review. Videos can be found at this webpage!
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.
This work has recently been published in the proceedings of the 2022 IEEE International Conference on Intelligent Robots and Systems [IROS '22]. We provide extensive simulation and experiment results on our webpage for this paper.
RSSI-based Localization with Adaptive Noise Covariance Estimation for Resilient Multi-Agent Formations
Cooperative multi-agent systems exchange information to coordinate their motion, such as in maintaining specific formations while performing operations. When localization is compromised on one or more agents, due to scenarios like cyber attacks or faults to on-board positioning sensors, desired formation control cannot be preserved.
To deal with this, we propose to utilize received signal strength indication (RSSI) as an inexpensive method to aid in localization (computationally and weight-wise). In this work, we formalize: i) detection of compromised positioning sensor measurements, ii) sensor reconfiguration to switch to RSSI-based position measurements, and iii) a robust method to adaptively estimate noise covariance of the RSSI-based sensing when fitted to a Kalman filter.
This work was presented at the 2023 IEEE American Control Conference (ACC) in San Diego, CA, USA. To learn more about this paper, check out our paper and its corresponding webpage containing MATLAB simulations found here.
Cooperative Robot Teams for Defending Against Malicious Intruders
Present-day robot teams are being used to accomplish various civilian and military applications, such as surveillance and security. My most recent project involves the development of robotic teams to collaborate their motion in order to defend against potential disasters. These disasters could include maliciously behaving intruders attempting to interfere with safety-critical infrastructure or regions within an environment. When behaving cooperatively, the multi-robot team can more effectively detect and engage an intruding agent. In this work, we develop a framework for a robot team to defend a "protected" region within an environment from intruders (i.e., prevent the intruder from entering the region) then, if possible, shepherd the maliciously behaving intruder to a safe region.
As this work progresses, we will provide more details which include pictures and simulation/experiment videos!
Outside of work, I enjoy playing pickleball, cooking, hiking, playing/watching sports, fantasy football, and learning about WWII history.