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A receding-horizon MDP approach for performance evaluation of moving target defense in networks

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arXiv 2020年

作者： Qian, Zhentian Fu, Jie Zhu, Quanyan Robotics Engineering Program Dept. of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

In this paper, we study the problem of assessing the effectiveness of a proactive defense-by-detection policy with a network-based moving target defense. We model the network system using a probabilistic attack graph-a graphical security model. Given a network system with a proactive defense strategy, an intelligent attacker needs to repeatedly perform reconnaissance to learn about the locations of intrusion detection systems and re-plan optimally to reach the target while avoiding detection. To compute the attacker's strategy for security evaluation, we develop a receding-horizon planning algorithm in a risk-sensitive Markov decision process with a time-varying reward function. Finally, we implement both defense and attack strategies in a synthetic network and analyze how the frequency of network randomization and the number of detection systems can influence the success rate of the attacker. This study provides insights for designing proactive defense strategies against online and multi-stage attacks carried out by a resourceful attacker. Copyright © 2020, The Authors. All rights reserved.

关键词： Network security

Qualitative planning in imperfect information games with active sensing and reactive sensor attacks: Cost of unawareness

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arXiv 2021年

作者： Kulkarni, Abhishek N. Han, Shuo Leslie, Nandi O. Kamhoua, Charles A. Fu, Jie The Robotics Engineering Program Dept. of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Electrical and Computer Engineering University of Illinois ChicagoIL60607 United States Raytheon Technologies U.S. Army Research Laboratory

We consider the probabilistic planning problem where the agent (called Player 1, or P1) can jointly plan the control actions and sensor queries in a sensor network and an attacker (called player 2, or P2) can carry out attacks on the sensors. We model such an adversarial interaction using a formal model-a reachability game with partially controllable observation functions. The main contribution of this paper is to assess the cost of P1's unawareness: Suppose P1 misinterprets the sensor failures as probabilistic node failures due to unreliable network communication, and P2 is aware of P1's misinterpretation in addition to her partial observability. Then, from which states can P2 carry out sensor attacks to ensure, with probability one, that P1 will not be able to complete her reachability task even though, due to misinterpretation, P1 believes that she can almost-surely achieve her task. We develop an algorithm to solve the almost-sure winning sensor-attack strategy given P1's observation-based strategy. Our attack analysis could be used for attack detection in wireless communication networks and the design of provably secured attack-aware sensor allocation in decision-theoretic models for cyber-physical systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Formal methods

A Receding-Horizon MDP Approach for Performance Evaluation of Moving Target Defense in Networks

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A Receding-Horizon MDP Approach for Performance Evaluation o...

Control Technology and Applications (CCTA),

作者： Zhentian Qian Jie Fu Quanyan Zhu Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA New York University USA

ISBN: (数字)9781728171401

ISBN: (纸本)9781728171418

In this paper we study the problem of assessing the effectiveness of a proactive defense-by-detection policy with a network-based moving target defense. We model the network system using a probabilistic attack graph-a graphical security model. Given a network system with a proactive defense strategy an intelligent attacker needs to perform reconnaissance repeatedly to learn about the locations of intrusion detection systems and re- plan optimally to reach the target while avoiding detection. To compute the attacker's strategy for security evaluation we develop a receding-horizon planning algorithm using a risk-sensitive Markov decision process with a time-varying reward function. Finally we implement both defense and attack strategies in a synthetic network and analyze how the frequency of network randomization and the number of detection systems can influence the success rate of the attacker. This study provides insights for designing proactive defense strategies against online and multi-stage attacks by a resourceful attacker.

关键词： Security Probabilistic logic Planning Performance evaluation Computational modeling Uncertainty Games

Multimaterial 3D Nanoprinting of a 2 Fr Soft Robotic Microcatheter for Transarterial Chemoembolization of Liver Cancer

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Multimaterial 3D Nanoprinting of a 2 Fr Soft Robotic Microca...

IEEE International Conference on Soft robotics (RoboSoft)

作者： Bailey M. Felix Jordi T. Andreou Sarah McHugh Jack E. McCann Olivia M. Young Molly A. Carton Dheeraj Gandhi Miroslaw Janowski Mark Fuge Axel Kreiger Clifford R. Weiss Christopher R. Bailey Ryan D. Sochol Fischell Department of Bioengineering University of Maryland College Park MD USA Department of Mechanical Engineering University of Maryland College Park MD USA Department of Diagnostic Radiology and Nuclear Medicine Program in Image Guided Neurointerventions University of Maryland School of Medicine Baltimore MD USA Department of Mechanical Engineering ETH Zurich Zurich Switzerland Department of Mechanical Engineering Johns Hopkins University Baltimore MD USA The Russell H. Morgan Department of Radiology and Radiological Science Division of Vascular and Interventional Radiology Johns Hopkins University School of Medicine Baltimore MD USA Maryland Robotics Center University of Maryland College Park MD USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park MD USA

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

Transarterial chemoembolization (TACE)—i.e., the delivery of chemotherapeutics directly into the vasculature of liver tumors—necessitates the use of guidewire-microcatheter systems to navigate tortuous vascular networks. These guide-wires and microcatheters typically lack effective steering controls, making successful navigation through geometrically complex anatomy challenging. Recent advancements in the area of soft microrobotics could offer a pathway to remotely steerable microcatheters at size scales relevant to TACE procedures; however, the difficulties associated with fabricating such systems have represented a critical hurdle. To overcome this barrier, here we present a multimaterial additive nanomanufacturing— or "three-dimensional (3D) nanoprinting"—strategy to achieve steerable soft robotic microcatheters that not only facilitate on-demand regulation of catheter tip deflection, but also comprise 2 French (Fr) outer dimensions (i.e., ⌀ = 666 μm) that are suitable for clinical chemoembolization of liver cancers. Specifically, we use "Two-Photon Direct Laser Writing (DLW)" to 3D nanoprint 1.5 mm-tall microcatheters capable of unidirectional tip bending of approximately 140° as well as mediating delivery of a fluidic payload. Experimental results for navigating the soft robotic microcatheter through a vascular phantom to deliver fluid payloads to target sites suggest the presented strategy holds promise for TACE as well as additional endovascular interventions and minimally invasive surgeries.

关键词： Liver cancer Actuators Navigation Deformation Phantoms Liver Soft robotics Bending Three-dimensional printing Payloads

Balanced depth completion between dense depth inference and sparse range measurements via KISS-GP

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arXiv 2020年

作者： Yoon, Sungho Kim, Ayoung Robotics Program KAIST Daejeon Korea Republic of Department of Civil and Environmental Engineering KAIST Daejeon Korea Republic of

Estimating a dense and accurate depth map is the key requirement for autonomous driving and robotics. Recent advances in deep learning have allowed depth estimation in full resolution from a single image. Despite this impressive result, many deep-learning-based monocular depth estimation (MDE) algorithms have failed to keep their accuracy yielding a meter-level estimation error. In many robotics applications, accurate but sparse measurements are readily available from Light Detection and Ranging (LiDAR). Although they are highly accurate, the sparsity limits full resolution depth map reconstruction. Targeting the problem of dense and accurate depth map recovery, this paper introduces the fusion of these two modalities as a depth completion (DC) problem by dividing the role of depth inference and depth regression. Utilizing the state-of-the-art MDE and our Gaussian process (GP) based depth-regression method, we propose a general solution that can flexibly work with various MDE modules by enhancing its depth with sparse range measurements. To overcome the major limitation of GP, we adopt Kernel Interpolation for Scalable Structured (KISS)-GP and mitigate the computational complexity from O(N3) to O(N). Our experiments demonstrate that the accuracy and robustness of our method outperform state-of-the-art unsupervised methods for sparse and biased measurements. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical radar

Development of automatic rice plant pest detection system based on convolutional neural network

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AIP Conference Proceedings 2023年第1期2482卷

作者： Matthew Sjarah Winda Astuti Lie Zener Ari Fadli 1)Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia 11480 2)Electrical Departement Faculty of Engineering Jenderal Soedirman University Purwokerto Jawa Tengah Indonesia 53122

Rice is the most important food crop in Indonesia, one of the plants that is quite significant and as a daily staple food for the people of Indonesia. The Indonesia government through the ministry of Agriculture tries to maintain the rice production by maintaining of the rice field. One the challenging situation that can involve the rice production is pest. Pests became one of the reasons which can reduce rice production. The decrease in rice production due to pest attack is an important problem in rice plant care. In this work, a pest detection system in rice plants developed using an intelligent system technique. The system involved image processing and intelligent technique. The system recognizes the kind of pests of the rice plant based on the feature of the image of the pets. Rice plant pest detection systems based on the pest image proceed by image procession technique and Convolution Neural Network (CNN). The system is working properly, since it resulting the training and testing accuracy of 99% and 90%, respectively.

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Shared Autonomous Interface for Reducing Physical Effort in Robot Teleoperation via Human Motion Mapping

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Shared Autonomous Interface for Reducing Physical Effort in ...

IEEE International Conference on robotics and Automation (ICRA)

作者： Tsung-Chi Lin Achyuthan Unni Krishnan Zhi Li Robotics Engineering Program Worcester Polytechnic Institute Worcester MA USA Mechanical Engineering Department Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Motion mapping is an intuitive method of teleoperation with a low learning curve. Our previous study investigates the physical fatigue caused by teleoperating a robot to perform general-purpose assistive tasks and this fatigue affects the operator's performance. The results from that study indicate that physical fatigue happens more in the tasks which involve more precise manipulation and steady posture maintenance. In this paper, we investigate how teleoperation assistance in terms of shared autonomy can reduce the physical workload in robot teleoperation via motion mapping. Specifically, we conduct a user study to compare the muscle effort in teleoperating a mobile humanoid robot to (1) reach and grasp an individual object and (2) collect objects in a cluttered workspace with and without an autonomous grasping function that can be triggered manually by the teleoperator. We also compare the participants' task performance, subjective user experience, and change in attitude towards the usage of teleoperation assistance in the future based on their experience using the assistance function. Our results show that: (1) teleoperation assistance like autonomous grasping can effectively reduce the physical effort, task completion time and number of errors; (2) based on their experience performing the tasks with and without assistance, the teleoperators reported that they would prefer to use automated functions for future teleoperation interfaces.

关键词： Task analysis Fatigue Muscles Grasping Cameras Robot vision systems

Toward “S”-Shaped 3D-Printed Soft Robotic Guidewires for Pediatric Patent Ductus Arteriosus Endovascular Interventions

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Toward “S”-Shaped 3D-Printed Soft Robotic Guidewires for P...

IEEE International Conference on Soft robotics (RoboSoft)

作者： Adira Colton Declan Fitzgerald Sunandita Sarker Noah Barnes Dheeraj Gandhi Miroslaw Janowski Jeremy D. Brown Joshua Kanter Laura Olivieri Mark Fuge Axel Krieger Ryan D. Sochol Department of Mechanical Engineering University of Maryland College Park MD USA Department of Mechanical Engineering Johns Hopkins University Baltimore MD USA Program in Image Guided Neurointerventions Department of Diagnostic Radiology and Nuclear Medicine University of Maryland School of Medicine Baltimore MD USA Children's National Heart Institute Children's National Hospital Washington DC USA Department of Pediatrics University of Pittsburgh School of Medicine Pittsburgh PA USA Maryland Robotics Center University of Maryland College Park MD USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park MD USA

Patent Ductus Arteriosus (PDA) is a heart condition in which the ductus arteriosus-a blood vessel connecting the pulmonary artery to the aorta in a fetus-fails to undergo closure after birth. A PDA can be an important factor in neonates born with severe congenital heart disease (CHD) or born prematurely. With the advent of new intravascular stent technologies, treatments based on ductus arteriosus stenting can now be completed in many cases; however, difficulties remain in accessing the ductus arteriosus in small babies successfully using current guidewire-catheter systems. Recent developments for soft robotic endovascular instruments that leverage control schemes hold distinctive potential for addressing these access challenges, but such technologies are not yet at the sizes required for navigating neonatal vasculature safely and efficiently. In an effort to meet this clinical need, this work presents an approach for 3D printing 1.5 French (Fr) soft robotic guidewires that transition from straight to “S”-shaped configurations under the application of fluidic (e.g., pneumatic or hydraulic) loading. Two distinct dual-opposing segmented soft actuators, including a symmetric and asymmetric system design (both with heights of 2.5 mm), were 3D printed onto 1.1 Fr capillaries in 35–60 minutes via “Two-Photon Direct Laser Writing (DLW)”. Experimental results revealed that both designs not only withstood pressures of up to 550 kPa, but also exhibited increased opposing bending deformations-corresponding to decreased radii of curvature-with increasing applied pressure. In combination, this study serves as a critical foundation for next-generation fluidically actuated soft robotic guidewire-catheter systems for PDA interventions.

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PrimA6D: Rotational Primitive Reconstruction for Enhanced and Robust 6D Pose Estimation

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arXiv 2020年

作者： Jeon, Myung-Hwan Kim, Ayoung Department of the Robotics Program KAIST Daejeon Korea Republic of Department of Civil and Environmental Engineering KAIST Daejeon Korea Republic of

— In this paper, we introduce a rotational primitive prediction based 6D object pose estimation using a single image as an input. We solve for the 6D object pose of a known object relative to the camera using a single image with occlusion. Many recent state-of-the-art (SOTA) two-step approaches have exploited image keypoints extraction followed by PnP regression for pose estimation. Instead of relying on bounding box or keypoints on the object, we propose to learn orientation-induced primitive so as to achieve the pose estimation accuracy regardless of the object size. We leverage a Variational AutoEncoder (VAE) to learn this underlying primitive and its associated keypoints. The keypoints inferred from the reconstructed primitive image are then used to regress the rotation using PnP. Lastly, we compute the translation in a separate localization module to complete the entire 6D pose estimation. When evaluated over public datasets, the proposed method yields a notable improvement over the LINEMOD, the Occlusion LINEMOD, and the YCB-Video dataset. We further provide a synthetic-only trained case presenting comparable performance to the existing methods which require real images in the training phase. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning