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Mission
Mission
The objective of this research is to design secure and resilient control and estimation algorithms for unmanned aircraft systems (UAS) operating in Urban environments while maintaining safety, reliability, and performance.
[Technology Innovation Institute (TII) Funded Project]
Driven by the emerging technologies on communication, sensors, and autonomy, new modes of air mobility such as unmanned aerial systems (UAS), urban aerial mobility (UAM) (e.g., Uber Elevate), and UAS traffic management (UTM) are expected to bring positive effects to our society and economy in multiple domains such as logistics, medical services, law enforcement, etc. Interweaving a large number of logic components with mechanical parts, UAS/UAM is a large-scale networked cyber-physical system with vehicles connected via communication links. Challenges for safe and efficient operations of UAS/UAM come from the vulnerability to sophisticatedly designed cyberattacks in terms of communication, navigation, surveillance (CNS), and command and control (C2). For example, deceptive attacks through communication links, e.g., Micro Air Vehicle Link (MAVLink), can falsify the supervising control; the jammed vehicle-to-vehicle (V2V) Automatic Dependent Surveillance-Broadcast (ADS-B) signal can compromise the protocol for UAS coordination by sending falsified information about position, velocity, or identity, which could lead to midair collision. The complex urban environment makes the issues of cybersecurity even more critical due to the high population and critical infrastructures (e.g., electric grid, water distribution, and airports).
Meanwhile, the urban environment also poses additional risks in UAS operations due to its complex topography compounded by low-altitude operations of UAS/UTM. For example, the GPS signal in urban areas can be impacted by multi-path, which makes the GPS signal more vulnerable to spoofing since the attack has potentially more avenues to exploit; due to the buildings and signal blocking, the UAS/UAM network is less redundant and less resilient to the loss of connectivity under cyberattacks; Considering the detrimental effects of cyberattacks on a high density of aerial vehicles operating in metropolitan areas with dense population and critical infrastructures, it is necessary for UAS/UAM to detect and mitigate cyberattacks under stringent time and space constraints. Therefore, we are motivated to consider cybersecurity issues in UAS/UAM by employing systems approaches including the following tasks (note that we plan to use UAS as a motivating example in our proposed project, though our methods are general enough to apply to other applications like UAM, UTM, and smart cars)
SSRC Summit and GENZERO Workshop, Abu Dhabi, 2024
SSRC Summit and GENZERO Workshop, Abu Dhabi, 2024
MIXED-SENSE_IROS2024.mp4MAS-IntegrityMonitor.mp4Research Topics and Experimental Demostrations
Research Topics and Experimental Demostrations
1) Modeling and Analysis for UAS Cybersecurity
1) Modeling and Analysis for UAS Cybersecurity
Man-in-the-middle Attacks on Cyberphysical Systems
Man-in-the-middle Attacks on Cyberphysical Systems
To address the security of cyber-physical systems, stealthy attacks, a class of false data injection attacks that can impact a system without being detected, have been studied. The existing discussions on stealthy attack design have rarely considered systems with switching structures; therefore, we investigate the design of a stealthy man-in-the-middle attack strategy that can be applied to switched systems. Specifically, we investigate how to design a stealthy attack without using mode information and analyze whether the attacker can infer the mode information that can be used to design a stealthy attack. A stealthy man-in-the-middle attack strategy for switched systems is proposed by combining mode identification and stealthy attack design. The feasibility and effectiveness of the strategy are discussed by using an illustrative example.
Publications:
D. Sun, I. Hwang, and J. Goppert, “A stealthy man-in-the-middle attack strategy for switched systems,” International Journal of Systems Science, vol. 55, no. 6, pp. 1206–1223, Apr. 2024, doi: 10.1080/00207721.2024.2304127.
Attack Detection and Identification using Lifted System Approach
Attack Detection and Identification using Lifted System Approach
Motivated by the safety and security issues related to cyber-physical systems with potentially multi-rate, delayed, and nonuniformly sampled measurements, we investigate the attack detection and identification using the lifted system technique. Attack detectability and identifiability based on the lifted system model are formally defined and rigorously characterized. The method of checking detectability is discussed, and a residual design problem for attack detection is formulated in a general way. For attack identification, we define and characterize it by generalizing the concept of mode discernibility for switched systems, and a method for identifying the attack is discussed based on theoretical analysis. An illustrative example of an unmanned aircraft system with secure yet multi-rate/delayed/asynchronous off-board measurements is provided to validate our proposed vulnerability analysis methods.
TII_Nov_Demo_Video_Exo_wGazebo_vF.wmvPublications:
Sun, Dawei, Minhyun Cho, and Inseok Hwang. "On Attack Detection and Identification for the Cyber-Physical System using Lifted System Model." arXiv preprint arXiv:2212.04396 (2022).
2) High Assurance Security Algorithm Development
2) High Assurance Security Algorithm Development
Synthesis of Robust State Estimation Algorithms under Cyberattacks
Synthesis of Robust State Estimation Algorithms under Cyberattacks
In many practical applications, the measurements obtained from some of the sensors may be corrupted by various unknown inputs, which can occur due to sensor malfunction, miscalibration, modeling errors, reflection/fading of signals, and cyberattacks such as false-data injection (FDI). We use the adjective unknown to emphasize that these inputs cannot be observed directly, nor may dynamical or probabilistic models of them be available to us a priori. For instance, the reflection of GNSS2 signals by high-rise buildings can introduce a bias in the range measurements obtained by a receiver on the ground; this phenomenon is called multipath bias in the GNSS community. In this work, we focus on the problem of mitigating the effect of such unknown inputs on the state estimation performance.
Publications:
Khan, Shiraz, Inseok Hwang, and James Goppert. "Robust state estimation in the presence of stealthy cyberattacks." 2022 American Control Conference (ACC). IEEE, 2022.
Khan, Shiraz, Kartik A. Pant, and Inseok Hwang. "Synthesis of robust state estimation algorithms under unknown sensor inputs." IEEE Control Systems Letters 7 (2023): 2707-2712.
Multi-Agent Fault Detection, Isolation and Reconstruction
Multi-Agent Fault Detection, Isolation and Reconstruction
The conventional solutions for fault-detection, identification, and reconstruction (FDIR) require centralized decision-making mechanisms, which are typically combinatorial in nature, necessitating the design of an efficient distributed FDIR mechanism that is suitable for multi-agent applications. To this end, we develop a general framework for efficiently reconstructing a sparse vector being observed over a sensor network via nonlinear measurements. The proposed framework is used to design a distributed multi-agent FDIR algorithm based on a combination of the sequential convex programming (SCP)and the alternating direction method of multipliers (ADMM) optimization approaches. The proposed distributed FDIR algorithm can process a variety of inter-agent measurements (including distances, bearings, relative velocities, and subtended angles between agents) to identify the faulty agents and recover their true states. The effectiveness of the proposed distributed multi-agent FDIR approach is demonstrated by considering a numerical example in which the inter-agent distances are used to identify the faulty agents in a multi-agent configuration, as well as reconstruct their error vectors.
Publication:
Khan, Shiraz, and Inseok Hwang. "Collaborative Fault-Identification & Reconstruction in Multi-Agent Systems." arXiv preprint arXiv:2309.11784 (2023).
Khan, Shiraz, and Inseok Hwang. "Recovery of Localization Errors in Sensor Networks using Inter-Agent Measurements." arXiv preprint arXiv:2307.12078 (2023).
Exploiting sparsity for localization of large‐scale wireless networks
Exploiting sparsity for localization of large‐scale wireless networks
Wireless Sensor Network (WSN) localization refers to the problem of determining the position of each of the agents in a WSN using noisy measurement information. In many cases, such as in distance and bearing‐based localization, the measurement model is a non‐linear function of the agents' positions, leading to pairwise interconnections between the agents. As the optimal solution for the WSN localization problem is known to be computationally expensive in these cases, an efficient approximation is desired. The authors show that the inherent sparsity in this problem can be exploited to greatly reduce the computational effort of using an Extended Kalman Filter (EKF) for large‐scale WSN localization. In the proposed method, which the authors call the L‐Banded Extended Kalman Filter (LB‐EKF), the measurement information matrix is converted into a banded matrix by relabelling (permuting the order of) the vertices of the graph. Using a combination of theoretical analysis and numerical simulations, it is shown that typical WSN configurations (which can be modeled as random geometric graphs) can be localised in a scalable manner using the proposed LB‐EKF approach
Publication:
Khan, Shiraz, Inseok Hwang, and James Goppert. "Exploiting sparsity for localization of large‐scale wireless sensor networks." IET Wireless Sensor Systems 14.1-2 (2024): 20-32.
3) Simulation and Experiment Testbed for Verification and Validation
3) Simulation and Experiment Testbed for Verification and Validation
Mixed-Sense: A Mixed Reality Sensor Emulation Framework
Mixed-Sense: A Mixed Reality Sensor Emulation Framework
In this work, we propose a high-fidelity Mixed Reality sensor emulation framework for testing and evaluating the resilience of Unmanned Aerial Vehicles (UAVs) against false data injection (FDI) attacks. The proposed approach can be utilized to assess the impact of FDI attacks, benchmark attack detector performance, and validate the effectiveness of mitigation/reconfiguration strategies in single-UAV and UAV swarm operations. Our Mixed Reality framework leverages high-fidelity simulations of Gazebo and a Motion Capture system to emulate proprioceptive (e.g., GNSS) and exteroceptive (e.g., camera) sensor measurements in real-time. We propose an empirical approach to faithfully recreate signal characteristics such as latency and noise in these measurements. Finally, we illustrate the efficacy of our proposed framework through a Mixed Reality experiment consisting of an emulated GNSS attack on an actual UAV, which (i) demonstrates the impact of false data injection attacks on GNSS measurements and (ii) validates a mitigation strategy utilizing a distributed camera network developed in our previous work.
Publication:
Pant, Kartik A., et al. "MIXED-SENSE: A Mixed Reality Sensor Emulation Framework for Test and Evaluation of UAVs Against False Data Injection Attacks." arXiv preprint arXiv:2407.09342 (2024). [Accepted for IROS 2024, AbuDhabi]
Pant, Kartik A., Zhanpeng Yang, James M. Goppert, and Inseok Hwang. "An open-source gazebo plugin for GNSS multipath signal emulation in virtual urban canyons." In AIAA SCITECH 2023 Forum, p. 2586. 2023.
Overview of our proposed framework. Gazebo’s rendering engine spawns real vehicles tracked in the motion capture and virtual vehicles running as PX4 SiTL instances. Gazebo’s physics engine performs the real-time processing of scene rendering, sensor emulation, and collision detection.
Target Tracking System for Urban Counter-UAS
Target Tracking System for Urban Counter-UAS
As interest and investment in unmanned aerial systems (UASs) continue to grow in recent years, security and surveillance of these vehicles in urban environments become increasingly crucial for operators and residents. Traditional aerial surveillance methods, such as radar systems, are less efficient in urban canyons due to high-rise buildings blocking line of sight (LOS). In this paper, we design a stationary tracking node consisting of a GPS, a high-resolution fish-eye camera, an infrared beacon, and a GPU-accelerated edge computing unit. We develop algorithms for calibrating a network of these deployed nodes in a large-scale urban environment. We also develop a multiple-sensor data association and estimation algorithm to track multiple UASs simultaneously. The estimation algorithm leverages a dual measurement mode using blob tracking for distant UASs and convolutional neural network (CNN) based object detection when the vehicle is close to one of the camera nodes
Publication:
Yang, Zhanpeng, James M. Goppert, and Inseok Hwang. "Target Tracking System for Urban Counter-UAS Using a Camera Network." AIAA SCITECH 2022 Forum. 2022.
Publications
Vijay, Vishnu, Kartik A. Pant, Minhyun Cho, Yifan Guo, James M. Goppert, and Inseok Hwang. "Range-based Multi-Robot Integrity Monitoring Against Cyberattacks and Faults: An Anchor-Free Approach." arXiv preprint arXiv:2408.11155 (2024).
K. A. Pant, S. Khan and I. Hwang, "Adversarial Sensor Attacks Against Uncertain Cyber-Physical Systems: A Dynamic Output Feedback Approach," in IEEE Control Systems Letters, vol. 8, pp. 2997-3002, 2024, doi: 10.1109/LCSYS.2024.3519435.
K. A. Pant, L. -Y. Lin, J. Kim, W. Sribunma, J. M. Goppert and I. Hwang, "MIXED-SENSE: A Mixed Reality Sensor Emulation Framework for Test and Evaluation of UAVs Against False Data Injection Attacks," 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates, 2024, pp. 12414-12419, doi: 10.1109/IROS58592.2024.10802327.
Khan, Shiraz, Inseok Hwang, and James Goppert. "Exploiting sparsity for localization of large‐scale wireless sensor networks." IET Wireless Sensor Systems 14.1-2 (2024): 20-32.
D. Sun, I. Hwang, and J. Goppert, “A stealthy man-in-the-middle attack strategy for switched systems,” International Journal of Systems Science, vol. 55, no. 6, pp. 1206–1223, Apr. 2024, doi: 10.1080/00207721.2024.2304127.
Khan, Shiraz, and Inseok Hwang. "Collaborative Fault-Identification & Reconstruction in Multi-Agent Systems." arXiv preprint arXiv:2309.11784 (2023).
Khan, Shiraz, and Inseok Hwang. "Recovery of Localization Errors in Sensor Networks using Inter-Agent Measurements." arXiv preprint arXiv:2307.12078 (2023).
S. Khan and I. Hwang, “Information-weighted Consensus Filtering under Limited Communication,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 10546–10551, Jan. 2023, doi: 10.1016/j.ifacol.2023.10.1077.
K. A. Pant, L.-Y. Lin, J. Goppert, and I. Hwang, “Towards Robust State Estimation in Matrix Lie Groups,” in ICRA 2023 workshop.
Pant, Kartik A., Zhanpeng Yang, James M. Goppert, and Inseok Hwang. "An open-source gazebo plugin for GNSS multipath signal emulation in virtual urban canyons." In AIAA SCITECH 2023 Forum, p. 2586. 2023.
Khan, Shiraz, Kartik A. Pant, and Inseok Hwang. "Synthesis of robust state estimation algorithms under unknown sensor inputs." IEEE Control Systems Letters 7 (2023): 2707-2712.
Sun, Dawei, Minhyun Cho, and Inseok Hwang. "On Attack Detection and Identification for the Cyber-Physical Systems using Lifted System Model." arXiv preprint arXiv:2212.04396 (2022).
Khan, Shiraz, Inseok Hwang, and James Goppert. "Robust state estimation in the presence of stealthy cyberattacks." 2022 American Control Conference (ACC). IEEE, 2022.
Yang, Zhanpeng, James M. Goppert, and Inseok Hwang. "Target Tracking System for Urban Counter-UAS Using a Camera Network." AIAA SCITECH 2022 Forum. 2022.
People
Current
Kartik Anand Pant, Ph.D. student
Yifan Guo, Ph.D. student
Vishnu Vijay, Masters student
Past
Minhyun Cho, Ph.D. student
Jaehyok Kim, Masters [Graduated in 2024]
Shiraz Khan, Ph.D. [Graduated in 2023]
Mohamad Aal Abdulla, Masters [Graduated in 2023]
Dawei Sun, Ph.D. [Graduated in 2022]
Zhangpeng Yang, Masters [Graduated in 2022]
Sponsor
Sponsor
Technology Innovation Institute (TII), AbuDhabi UAE
From 2020 to 2024
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