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

Mobile robots such as unmanned vehicles integrate heterogeneous capabilities in sensing, computation, and control. They are representative cyber-physical systems where the cyberspace and the physical world are strongly coupled. However, the safety of mobile robots is significantly threatened by cyber/physical attacks and software/hardware failures. These threats can thwart normal robot operations and cause robot misbehaviors. In this paper, we propose a novel anomaly detection system, which leverages physical dynamics of mobile robots to detect misbehaviors in sensors and actuators. We explore issues raised in real-world implementations, e.g., distinctive robot dynamic models, sensor quantity and quality, decision parameters, etc., for practicality purposes. We implement the detection system on two types of mobile robots and evaluate the detection performance against various misbehavior scenarios, including signal interference, sensor spoofing, logic bomb and physical jamming. The experiments show detection effectiveness and small detection delays.

[1]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[2]  M. Morari,et al.  Internal model control: PID controller design , 1986 .

[3]  Lui Sha,et al.  A Software Architecture for Dependable and Evolvable Industrial Computing Systems. , 1995 .

[4]  Petar M. Djuric,et al.  Gaussian particle filtering , 2003, IEEE Trans. Signal Process..

[5]  T. Humphreys,et al.  Assessing the Spoofing Threat: Development of a Portable GPS Civilian Spoofer , 2008 .

[6]  Berk Sunar,et al.  An Emerging Threat: Eve Meets a Robot - (Work-in-Progress) , 2010, INTRUST.

[7]  Emanuele Garone,et al.  False data injection attacks against state estimation in wireless sensor networks , 2010, 49th IEEE Conference on Decision and Control (CDC).

[8]  Felix C. Freiling,et al.  A structured approach to anomaly detection for in-vehicle networks , 2010, 2010 Sixth International Conference on Information Assurance and Security.

[9]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[10]  Matti Valovirta,et al.  Experimental Security Analysis of a Modern Automobile , 2011 .

[11]  Hovav Shacham,et al.  Comprehensive Experimental Analyses of Automotive Attack Surfaces , 2011, USENIX Security Symposium.

[12]  Florian Dörfler,et al.  Attack Detection and Identification in Cyber-Physical Systems -- Part II: Centralized and Distributed Monitor Design , 2012, ArXiv.

[13]  Rodrigo Maximiano Antunes de Almeida,et al.  Microkernel Development for Embedded Systems , 2013 .

[14]  Insup Lee,et al.  Attack resilient state estimation for autonomous robotic systems , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Paulo Tabuada,et al.  Secure Estimation and Control for Cyber-Physical Systems Under Adversarial Attacks , 2012, IEEE Transactions on Automatic Control.

[16]  Yongdae Kim,et al.  Rocking Drones with Intentional Sound Noise on Gyroscopic Sensors , 2015, USENIX Security Symposium.

[17]  Paulo Tabuada,et al.  Attack-resilient state estimation in the presence of noise , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[18]  Emilio Frazzoli,et al.  Resilient state estimation against switching attacks on stochastic cyber-physical systems , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[19]  Mani Srivastava,et al.  PyCRA: Physical Challenge-Response Authentication For Active Sensors Under Spoofing Attacks , 2015, CCS.

[20]  Insup Lee,et al.  Sensor attack detection in the presence of transient faults , 2015, ICCPS.

[21]  Todd Litman,et al.  Autonomous Vehicle Implementation Predictions: Implications for Transport Planning , 2015 .

[22]  Jonathan Petit,et al.  Remote Attacks on Automated Vehicles Sensors : Experiments on Camera and LiDAR , 2015 .

[23]  Nathalie Japkowicz,et al.  Frequency-based anomaly detection for the automotive CAN bus , 2015, 2015 World Congress on Industrial Control Systems Security (WCICSS).

[24]  Kang G. Shin,et al.  Fingerprinting Electronic Control Units for Vehicle Intrusion Detection , 2016, USENIX Security Symposium.

[25]  Chen Yan.  Can You Trust Autonomous Vehicles : Contactless Attacks against Sensors of Self-driving Vehicle , 2016 .

[26]  Nathalie Japkowicz,et al.  Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks , 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[27]  Peng Liu,et al.  Attack-resilient estimation of switched nonlinear cyber-physical systems , 2016, 2017 American Control Conference (ACC).

[28]  Kang G. Shin,et al.  Exploiting Consistency among Heterogeneous Sensors for Vehicle Anomaly Detection , 2017 .

[29]  Kang G. Shin,et al.  Viden: Attacker Identification on In-Vehicle Networks , 2017, CCS.

[30]  Peng Liu,et al.  Nonlinear Unknown Input and State Estimation Algorithm in Mobile Robots , 2018, ArXiv.