Abstract
Inertial sensors are widely used in navigation, motion tracking, and gesture recognition systems. However, these sensors are vulnerable to spoofing attacks, where an attacker injects a carefully designed acoustic signal to trick the sensor readings. Traditional approaches to detecting and mitigating attacks rely on module redundancy, i.e., adding multiple sensor modules to increase robustness. However, this approach is not always feasible due to the limited space and increased complexity of current printed circuit boards.
This paper proposes a new method, ADC-Bank, to detect inertial sensor spoofing attacks via acoustic out-of-band signals. Unlike other multiple-sensor-based solutions, it is based on component redundancy within one sensor, using multiple analog-to-digital converters (ADCs) with different sampling rates to simultaneously sample the output of the sensors. The different sample rates result in different aliasing frequencies for out-of-band signals that can be used to detect attacks. The proposed method is evaluated on off-the-shelf inertial sensors with commercial ADCs, demonstrating its ability to detect the attacking signals with relatively low cost and computation overhead.
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References
Arduino: Arduino Uno Rev3. https://store-usa.arduino.cc/products/arduino-uno-rev3. Accessed 16 Aug 2022
Avisoft. Ultrasoundgate. http://www.avisoft.com/ultrasoundgate/. Accessed 16 Aug 2022
Bolton, C., Rampazzi, S., Li, C., Kwong, A., Xu, W., Fu, K.: Blue note: how intentional acoustic interference damages availability and integrity in hard disk drives and operating systems. In: 2018 IEEE Symposium on Security and Privacy (SP), pp. 1048ā1062. IEEE (2018)
Castro, S., Dean, R., Roth, G., Flowers, G.T., Grantham, B.: Influence of acoustic noise on the dynamic performance of mems gyroscopes. In: ASME International Mechanical Engineering Congress and Exposition, vol. 43033, pp. 1825ā1831 (2007)
Dean, R.N., et al.: On the degradation of mems gyroscope performance in the presence of high power acoustic noise. In: 2007 IEEE International Symposium on Industrial Electronics, pp. 1435ā1440. IEEE (2007)
Dean, R.N., et al.: A characterization of the performance of a mems gyroscope in acoustically harsh environments. IEEE Trans. Ind. Electron. 58(7), 2591ā2596 (2010)
Analog Devices. Adxl335. https://www.analog.com/cn/products/adxl335.html. Accessed 16 Aug 2022
Sami Fadali, M., Visioli, A.: Digital Control Engineering: Analysis and Design, Chapter 12 Practical Issues. Academic Press, Cambridge (2012)
Gallego-JuĆ”rez, J.A., Rodriguez-Corral, G., Gaete-Garreton, L.: An ultrasonic transducer for high power applications in gases. Ultrasonics 16(6), 267ā271 (1978)
Gao, M., et al.: KITE: exploring the practical threat from acoustic transduction attacks on inertial sensors. In: 20th ACM Conference on Embedded Networked Sensor Systems (2022)
Giechaskiel, I., Rasmussen, K.: Taxonomy and challenges of out-of-band signal injection attacks and defenses. IEEE Commun. Surv. Tutor. 22(1), 645ā670 (2019)
Giechaskiel, I., Zhang, Y., Rasmussen, K.B.: A framework for evaluating security in the presence of signal injection attacks. In: Sako, K., Schneider, S., Ryan, P.Y.A. (eds.) ESORICS 2019, Part I. LNCS, vol. 11735, pp. 512ā532. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29959-0_25
Hurley, R.: Design considerations for ESD/EMI filters: II low pass filters for audio filter applications. ON Semiconductor (2007)
National Instruments. NI USB-4431. https://www.ni.com/pdf/manuals/376767a.pdf. Accessed 16 Aug 2022
Kitchin, C.: Avoiding Op Amp instability problems in single-supply applications. Analog Devices, Tech. Rep (2001)
Kune, D.F., et al.: Ghost talk: mitigating EMI signal injection attacks against analog sensors. In: 2013 IEEE Symposium on Security and Privacy, pp. 145ā159. IEEE (2013)
Laermer, F.: Mechanical Microsensors. MEMS: A Practical Guide to Design, Analysis, and Applications, pp. 523ā566 (2006)
Mishali, M., Eldar, Y.C.: From theory to practice: sub-nyquist sampling of sparse wideband analog signals. IEEE J. Sel. Top. Signal Process. 4(2), 375ā391 (2010)
Nashimoto, S., Suzuki, D., Sugawara, T., Sakiyama, K.: Sensor confusion: defeating Kalman filter in signal injection attack. In: Proceedings of the 2018 on Asia Conference on Computer and Communications Security, pp. 511ā524 (2018)
Park, Y., Son, Y., Shin, H., Kim, D., Kim, Y.: This aināt your dose: sensor spoofing attack on medical infusion pump. In: 10th USENIX Workshop on Offensive Technologies. USENIX (2016)
Pei, D., Salomaa, A., Ding, C.: Chinese Remainder Theorem: Applications in Computing, Coding, Cryptography. World Scientific (1996)
Petit, J., Stottelaar, B., Feiri, M., Kargl, F.: Remote attacks on automated vehicles sensors: experiments on camera and lidar. Black Hat Europe 11(2015), 995 (2015)
Rigol. DG5352 function/arbitrary waveform generator. https://rigol.com/products/DGdetail/DG5000. Accessed 16 Aug 2022
SainSmart. UDB1002S DDS signal generator. https://www.amazon.com/SainSmart-UDB1002S-Signal-Generator-Function/dp/B00JTR66CG/. Accessed 10 July 2022
Sawigun, C., Thanapitak, S.: A 0.9-nW, 101-Hz, and 46.3-uv IRN low-pass filter for ECG acquisition using FVF biquads. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 26(11), 2290ā2298 (2018)
Shaeffer, D.K.: Mems inertial sensors: a tutorial overview. IEEE Commun. Mag. 51(4), 100ā109 (2013)
Shaw, M.L.: Accelerometer overload considerations for automotive airbag applications. SAE Trans., 344ā350 (2002)
Shin, H., Kim, D., Kwon, Y., Kim, Y.: Illusion and dazzle: adversarial optical channel exploits against lidars for automotive applications. In: Fischer, W., Homma, N. (eds.) CHES 2017. LNCS, vol. 10529, pp. 445ā467. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66787-4_22
Sƶderkvist, J.: Micromachined gyroscopes. Sens. Actuators, A 43(1ā3), 65ā71 (1994)
Son, Y., et al.: Rocking drones with intentional sound noise on gyroscopic sensors. In: 24th USENIX Security Symposium (USENIX Security 2015), pp. 881ā896 (2015)
SparkFun. MiniGen pro mini signal generator shield. https://www.sparkfun.com/products/11420. Accessed 10 July 2022
Sreenivasulu, P., Hanumantha Rao, G., Rekha, S., Bhat, M.S.: A 0.3 V, 56 db DR, 100 Hz fourth order low-pass filter for ECG acquisition system. Microelectron. J. 94, 104652 (2019)
Stilson, T.: Problems with the anti-aliasing filter. https://ccrma.stanford.edu/CCRMA/Courses/252/sensors/node35.html. Accessed 11 Dec 2022
STMICROELECTRONICS. Lpy550al. https://pdf1.alldatasheetcn.com/datasheet-pdf/view/346169/STMICROELECTRONICS/LPY550AL.html. Accessed 16 Aug 2022
Tharayil, K.S., et al.: Sensor defense in-software (SDI): practical software based detection of spoofing attacks on position sensors. Eng. Appl. Artif. Intell. 95, 103904 (2020)
Tian, J., et al.: Mobile device fingerprint identification using gyroscope resonance. IEEE Access 9, 160855ā160867 (2021)
Trippel, T., Weisse, O., Xu, W., Honeyman, P., Fu, K.: Walnut: Waging doubt on the integrity of mems accelerometers with acoustic injection attacks. In: 2017 IEEE European Symposium on Security and Privacy (EuroS &P), pp. 3ā18. IEEE (2017)
Tu, Y., Lin, Z., Lee, I., Hei, X.: Injected and delivered: fabricating implicit control over actuation systems by spoofing inertial sensors. In: USENIX Security Symposium, pp. 1545ā1562 (2018)
Tu, Y., Rampazzi, S., Hao, B., Rodriguez, A., Fu, K., Hei, X.: Trick or heat? Manipulating critical temperature-based control systems using rectification attacks. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 2301ā2315 (2019)
Tu, Y., Rampazzi, S., Hei, X.: Towards adversarial control loops in sensor attacks: a case study to control the kinematics and actuation of embedded systems. ar**v preprint ar**v:2203.07670 (2022)
Wang, Z., Wang, K., Yang, B., Li, S., Pan, A.: Sonic gun to smart devices. Black Hat USA (2017)
Wenyuan, X., Yan, C., Jia, W., Ji, X., Liu, J.: Analyzing and enhancing the security of ultrasonic sensors for autonomous vehicles. IEEE Internet Things J. 5(6), 5015ā5029 (2018)
Yan, C., Wenyuan, X., Liu, J.: Can you trust autonomous vehicles: contactless attacks against sensors of self-driving vehicle. Def Con 24(8), 109 (2016)
Yazdi, N., Ayazi, F., Najafi, K.: Micromachined inertial sensors. Proc. IEEE 86(8), 1640ā1659 (1998)
Zhang, G., Yan, C., Ji, X., Zhang, T., Zhang, T., Xu, W.: DolphinAttack: inaudible voice commands. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 103ā117 (2017)
Acknowledgment
The authors thank the anonymous reviewers for their valuable comments that improved this paper. This work is supported in part by the US NSF under grants CNS-1812553, CNS-2117785, OIA-2229752, CNS-2231682, and two gifts from Meta.
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Zhang, J. et al. (2024). ADC-Bank: Detecting Acoustic Out-of-Band Signal Injection onĀ Inertial Sensors. In: Chen, Y., Lin, CW., Chen, B., Zhu, Q. (eds) Security and Privacy in Cyber-Physical Systems and Smart Vehicles. SmartSP 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-031-51630-6_4
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