RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots

Guo, Pinyao; Kim, Hunmin; Virani, Nurali; Xu, Jun; Zhu, Minghui; Liu, Peng

doi:10.1109/dsn.2018.00065

Cited by 40 publications

(23 citation statements)

References 21 publications

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“…Daniel et al [ 62 ], as another example, uses a radar, a lidar, and a stereo camera under foggy conditions. Other publications like Choi et al [ 46 ] and Guo et al [ 43 ] describe how to suppress security attacks by comparing between sensors of different types.…”

Section: Literature Survey On Fdiir Methods For Automotive Lidarmentioning

confidence: 99%

Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar

Goelles

Schlager

Muckenhuber

2020

Sensors

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Perception sensors such as camera, radar, and lidar have gained considerable popularity in the automotive industry in recent years. In order to reach the next step towards automated driving it is necessary to implement fault diagnosis systems together with suitable mitigation solutions in automotive perception sensors. This is a crucial prerequisite, since the quality of an automated driving function strongly depends on the reliability of the perception data, especially under adverse conditions. This publication presents a systematic review on faults and suitable detection and recovery methods for automotive perception sensors and suggests a corresponding classification schema. A systematic literature analysis has been performed with focus on lidar in order to review the state-of-the-art and identify promising research opportunities. Faults related to adverse weather conditions have been studied the most, but often without providing suitable recovery methods. Issues related to sensor attachment and mechanical damage of the sensor cover were studied very little and provide opportunities for future research. Algorithms, which use the data stream of a single sensor, proofed to be a viable solution for both fault detection and recovery.

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Section: Literature Survey On Fdiir Methods For Automotive Lidarmentioning

confidence: 99%

Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar

Goelles

Schlager

Muckenhuber

2020

Sensors

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“…To address the severe cyber-physical security issues, active work is on monitoring the robot behavior to detect attacks at the robot level. Guo et al investigate the robot misbehaviors originated from a variety of sources [7]. They propose to use a robot anomaly detection system to counteract various mobile robot attacks/failures, which exhibit either sensor or actuator misbehaviors.…”

Section: Related Workmentioning

confidence: 99%

“…We focus on the adversaries, whose goal is to hijack the robotic arm or impersonate the target user to control it for gaining higher control privileges, causing physical damages and achieving the repudiation of performing malicious tasks. These attacks could evade the existing anomaly detection methods [7], because an adversary could control the robotic arm to do valid tasks (e.g., grasping), which may not show any anomalies at the robotic arm. As most robot systems require login authentication, we assume the adversary has obtained the user's login credentials and gained access to the robotic arm to perform various control tasks.…”

Section: Threat Modelmentioning

confidence: 99%

Toward Verifying the User of Motion-Controlled Robotic Arm Systems via the Robot Behavior

Huang

Meng

Deng

et al. 2022

IEEE Internet Things J.

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Motion-controlled robotic arms allow a user to interact with a remote real world without physically reaching it. By connecting cyberspace to the physical world, such interactive teleoperations are promising to improve remote education, virtual social interactions and online participatory activities. In this work, we build up a motion-controlled robotic arm framework comprising a robotic arm end and a user end, which are connected via a network and responsible for manipulator control and motion capture respectively. To protect the system access, we propose to verify who is controlling the robotic arm by examining the robotic arm's behavior, which adds a second security layer in addition to the system login credentials. We show that a robotic arm's motion inherits its human controller's behavioral biometric in interactive control scenarios. By extracting the angle readings of the robotic arm's all joints, the proposed user authentication approach reconstructs the robotic arm's end-effector movement trajectory that follows the user's hand. Furthermore, we derive the unique robotic motion features to capture the user's behavioral biometric embedded in the robot motions and develop learning-based algorithms to verify the robotic arm user to be one of the enrolled users or a nonuser. Extensive experiments show that our system achieves 94% accuracy to distinguish users while preventing user identity spoofing attacks with 95% accuracy.

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“…For linear systems in (1) with Gaussian additive noises w k , v G k , and v I k , state estimations of the standard Kalman filter (KF) are Gaussian as well. Through this observation, the χ 2 statistical test is widely used in attack detection, Teixeira et al (2010); Mo et al (2014); Guo et al (2018), to distinguish whether the error is induced by statistical noises or attacks. In particular, the χ 2 test has two hypothesis:…”

Section: χ 2 Attack Detectormentioning

confidence: 99%

Towards Resilient UAV: Escape Time in GPS Denied Environment with Sensor Drift

et al. 2019

Self Cite

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This paper considers a resilient state estimation framework for unmanned aerial vehicles (UAVs) that integrates a Kalman filter-like state estimator and an attack detector. When an attack is detected, the state estimator uses only IMU signals as the GPS signals do not contain legitimate information. This limited sensor availability induces a sensor drift problem questioning the reliability of the sensor estimates. We propose a new resilience measure, escape time, as the safe time within which the estimation errors remain in a tolerable region with high probability. This paper analyzes the stability of the proposed resilient estimation framework and quantifies a lower bound for the escape time. Moreover, simulations of the UAV model demonstrate the performance of the proposed framework and provide analytical results.

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RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots

Cited by 40 publications

References 21 publications

Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar

Fault Detection, Isolation, Identification and Recovery (FDIIR) Methods for Automotive Perception Sensors Including a Detailed Literature Survey for Lidar

Toward Verifying the User of Motion-Controlled Robotic Arm Systems via the Robot Behavior

Towards Resilient UAV: Escape Time in GPS Denied Environment with Sensor Drift

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