Research on Multi-Technology Integrated Anti-Mechanical Attack Scheme Based on Vibration Sensors

doi:10.19678/j.issn.1000-3428.0069514

Abstract

Abstract:

In predictive maintenance systems, the vibration sensors used during the data collection phase may be subjected to human or environmental interference, leading to data anomalies. A secure and reliable integrated pre-detection scheme is proposed to ensure the reliability of the collected data. This scheme combines random open strategies, similarity detection methods, and sound source localization technologies to enhance the accuracy and reliability of the system in terms of both spatial and temporal dimensions. First, a random open strategy is used to ensure that the sensors are not subjected to directional interference, thereby enhancing the safety redundancy of the system. Second, a similarity detection method utilizes multi-dimensional distances to calculate the similarity of consecutive acceleration data collected by vibration sensors and compares it with a threshold to increase the sensitivity of the system to equipment status. Finally, a sound source localization technology analyzes the audio corresponding to abnormal similarities to determine the source location, further enhancing the precision of pre-detection. Experimental results in targeted testing environments indicate that in non-adversarial scenarios, the non-integrated scheme improves the accuracy and precision by 4 and 4.13 percentage points, respectively, compared to the integrated scheme and the recall remains the same. Conversely, in adversarial scenarios, the integrated scheme improves the accuracy and recall by 9.5 and 9.14 percentage points, respectively, compared to the non-integrated scheme, with the precision remaining constant.

Key words: hardware security, predictive maintenance, industrial Internet of things, adversarial scenarios, random interference

摘要：

在预测性维护系统中，振动传感器在数据采集阶段可能会受到人为或自然环境的干扰，导致数据异常。为了确保采集数据的可靠性，提出一种安全可靠的集成式预检测方案。该方案结合了随机开启策略、相似性检测和声源定位这三种技术，从空间和时间两个维度提升系统的准确性与可靠性。首先，通过随机开启策略确保传感器不会受到定向干扰，增强系统的安全冗余；其次，相似性检测方法采用多维度距离来计算振动传感器连续采集加速度数据的相似度，并与阈值比较以提高系统对设备状态的敏感度；最后，通过声源定位技术分析异常相似度对应的音频来判断声源位置，进一步提高了预检测的精确度。在对抗和非对抗场景下的实验结果表明，在非对抗场景下，未集成方案相对于集成方案的准确率和精确度分别提升了4和4.13百分点，但召回率保持不变，在对抗场景下，集成方案相对于未集成方案的准确率和召回率分别提升了9.5和9.14百分点，但精确度保持不变。

关键词: 硬件安全, 预测性维护, 工业物联网, 对抗场景, 随机干扰

HUANG Daoqi, ZHANG Huajun, SUN Ning, ZHUANG Lihua, XU Shoukun. Research on Multi-Technology Integrated Anti-Mechanical Attack Scheme Based on Vibration Sensors[J]. Computer Engineering, 2025, 51(10): 213-224.

黄道旗, 张华君, 孙宁, 庄丽华, 徐守坤. 基于振动传感器的多技术集成抗机械攻击方案研究[J]. 计算机工程, 2025, 51(10): 213-224.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0069514

https://www.ecice06.com/EN/Y2025/V51/I10/213

Figures/Tables 14

Fig.1 Architecture of system model

Fig.2 Pre-detection procedure of integrated scheme

Fig.3 Experimental platform diagram

Fig.4 Three-dimensional space diagram drawn to scale(8 ∶1 ∶2)

Fig.5 Procedure of experimental scheme

Fig.6 Similarity in non-adversarial scenarios

Fig.7 Similarity in adversarial scenarios

References 30

1	JARDINE A K S , LIN D M , BANJEVIC D . A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 2006, 20 (7): 1483- 1510. doi: 10.1016/j.ymssp.2005.09.012
2	禹鑫燚, 施甜峰, 唐权瑞, 等. 面向预测性维护的工业设备管理系统. 计算机科学, 2020, 47 (S2): 667-672, 677.
	YU X H , SHI T F , TANG Q R , et al. Industrial equipment management system for predictive maintenance. Computer Science, 2020, 47 (S2): 667-672, 677.
3	周东华, 魏慕恒, 司小胜. 工业过程异常检测、寿命预测与维修决策的研究进展. 自动化学报, 2013, 39 (6): 711- 722.
	ZHOU D H , WEI M H , SI X S . A survey on anomaly detection, life prediction and maintenance decision for industrial processes. Journal of Automation Sinica, 2013, 39 (6): 711- 722.
4	ALSUWIAN T , AMIN A A , MAQSOOD M T , et al. Advanced fault-tolerant anti-surge control system of centrifugal compressors for sensor and actuator faults. Sensors, 2022, 22 (10): 3864. doi: 10.3390/s22103864
5	HUYNH K T , GRALL A , BÉRENGUER C . A parametric predictive maintenance decision-making framework considering improved system health prognosis precision. IEEE Transactions on Reliability, 2019, 68 (1): 375- 396. doi: 10.1109/TR.2018.2829771
6	HU J W , CHEN P . Predictive maintenance of systems subject to hard failure based on proportional hazards model. Reliability Engineering & System Safety, 2020, 196, 106707.
7	王汉禹, 陈震, 周笛, 等. 基于核函数-Wiener过程的轧辊非线性退化建模与剩余寿命预测. 上海交通大学学报, 2023, 57 (8): 1037- 1045.
	WANG H Y , CHEN Z , ZHOU D , et al. Nonlinear degradation modeling and residual life prediction for rollers based on kernel-based wiener process. Journal of Shanghai Jiao Tong University, 2023, 57 (8): 1037- 1045.
8	ZHAO R, WANG J J, YAN R Q, et al. Machine health monitoring with LSTM networks[C]//Proceedings of the 10th International Conference on Sensing Technology (ICST). Washington D.C., USA: IEEE Press, 2016: 1-6.
9	LI Y , SHI Y , ZHANG Z , et al. Condition-based maintenance for performance degradation under nonperiodic unreliable inspections. IEEE Transactions on Artificial Intelligence, 2023, 4 (4): 709- 721. doi: 10.1109/TAI.2022.3197680
10	ZHANG W T , YANG D , WANG H C . Data-driven methods for predictive maintenance of industrial equipment: a survey. IEEE Systems Journal, 2019, 13 (3): 2213- 2227. doi: 10.1109/JSYST.2019.2905565
11	王宇, 祁琦, 王纯, 等. 储能变流器信号高精度故障诊断方法. 计算机工程, 2024, 50 (8): 389- 396. doi: 10.19678/j.issn.1000-3428.0068520
	WANG Y , QI Q , WANG C , et al. High-precision fault diagnosis method for energy storage inverter signals. Computer Engineering, 2024, 50 (8): 389- 396. doi: 10.19678/j.issn.1000-3428.0068520
12	SHI Q , ZHANG H . Fault diagnosis of an autonomous vehicle with an improved SVM algorithm subject to unbalanced datasets. IEEE Transactions on Industrial Electronics, 2020, 68 (7): 6248- 6256.
13	ATTOUI I . Novel fast and automatic condition monitoring strategy based on small amount of labeled data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52 (2): 1266- 1275. doi: 10.1109/TSMC.2020.3018102
14	ABURAKHIA S, TAYEH T, MYERS R, et al. A transfer learning framework for anomaly detection using model of normality[C]//Proceedings of the 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). Washington D.C., USA: IEEE Press, 2020: 55-61.
15	ABURAKHIA S, TAYEH T, MYERS R, et al. Similarity-based predictive maintenance framework for rotating machinery[C]//Proceedings of the 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA). Washington D.C., USA: IEEE Press, 2022: 1-6.
16	TRIPPEL T, WEISSE O, XU W Y, et al. WALNUT: waging doubt on the integrity of MEMS accelerometers with acoustic injection attacks[C]//Proceedings of IEEE European Symposium on Security and Privacy (EuroS & P). Washington D.C., USA: IEEE Press, 2017: 3-18.
17	TU Y Z, LIN Z Q, LEE I, et al. Injected and delivered: fabricating implicit control over actuation systems by spoofing inertial sensors[C]//Proceedings of the 27th USENIX Security Symposium (USENIX Security 18). New York, USA: ACM Press, 2018: 1545-1562.
18	PETIT J , STOTTELAAR B , FEIRI M , et al. Remote attacks on automated vehicles sensors: experiments on camera and lidar. Black Hat Europe, 2015, 11, 995.
19	YAN C, XU W Y, LIU J H. Can you trust autonomous vehicles: contactless attacks against sensors of self-driving vehicle[EB/OL]. [2024-02-01]. https://cyansec.com/files/articles/16DEFCON-Sensor.pdf.
20	ABURAKHIA S , SHAMI A . SB-PdM: a tool for predictive maintenance of rolling bearings based on limited labeled data. Software Impacts, 2023, 16, 100503. doi: 10.1016/j.simpa.2023.100503
21	ATTOUI I , BOUTASSETA N , FERGANI N . Novel machinery monitoring strategy based on time-frequency domain similarity measurement with limited labeled data. IEEE Transactions on Instrumentation and Measurement, 2021, 70, 1- 8.
22	PADMAVATHI G, SHANMUGAPRIYA D, KALAIVANI M. A hybrid approach for acoustic signal segmentation by computing similarity matrix, novelty score and peak detection for vehicular classification in wireless sensor networks[C]//Proceedings of International Conference on Emerging Trends in Robotics and Communication Technologies. Washington D.C., USA: IEEE Press, 2010: 334-338.
23	唐兴潮, 伍星, 柳小勤, 等. 广义旁瓣抵消器算法的轴承噪声信号增强研究. 机械科学与技术, 2023, 42 (7): 1098- 1102.
	TANG X C , WU X , LIU X Q , et al. Research on bearing fault signal enhancement using generalized sidelobe canceller agorithm. Mechanical Science and Technology for Aerospace Engineering, 2023, 42 (7): 1098- 1102.
24	陈圆. 圆形麦克风阵列语音增强算法研究与实现[D]. 南京: 南京邮电大学, 2023.
	CHEN Y. Research and implementation of speech enhancement algorithm for circular microphone arrays[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023. (in Chinese)
25	KIRMAZ A , ŞAHIN T , MICHALOPOULOS D S , et al. ToA and TDoA estimation using artificial neural networks for high-accuracy ranging. IEEE Journal on Selected Areas in Communications, 2023, 41 (12): 3816- 3830. doi: 10.1109/JSAC.2023.3322803
26	LIU H T , CHEN Y H , HUANG Y , et al. Study on the localization method of multi-aperture acoustic array based on TDOA. IEEE Sensors Journal, 2021, 21 (12): 13805- 13814. doi: 10.1109/JSEN.2021.3070811
27	LI D , HU Y H . Energy-based collaborative source localization using acoustic microsensor array. EURASIP Journal on Advances in Signal Processing, 2003, 2003 (4): 321- 337.
28	JIA M S , JIA Y T , GAO S , et al. Multi-source DOA estimation in reverberant environments using potential single-source points enhancement. Applied Acoustics, 2021, 174, 107782. doi: 10.1016/j.apacoust.2020.107782
29	贺谦, 韩凯, 冯建民, 等. 任意构型传声器阵列声源定位方法. 西安交通大学学报, 2020, 54 (4): 186- 192.
	HE Q , HAN K , FENG J M , et al. A sound source localization method for microphone array with arbitrary configuration. Journal of Xi'an Jiao Tong University, 2020, 54 (4): 186- 192.
30	ZHANG Y, RASMUSSEN K. Electromagnetic signal injection attacks on differential signaling[C]//Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security. New York, USA: ACM Press, 2023: 314-325.

[1]	LI Siqi, TIAN Youliang, PENG Changgen. Industrial Internet of Things Threshold Signcryption Scheme Based on SM Algorithm [J]. Computer Engineering, 2025, 51(7): 210-222.
[2]	Jin XU, Xiangdong JIA, Xianghua HAN, Xingyuan ZHANG. Analysis of Non-Linear Age of Information in Industrial Internet of Things for Non-Linear Energy Harvesting [J]. Computer Engineering, 2024, 50(8): 198-206.
[3]	Jicheng CHEN, Zijian BAO, Min LUO, Debiao HE. A Security Remote Command Control Scheme for Industrial Internet of Things [J]. Computer Engineering, 2024, 50(3): 28-35.
[4]	LIU Jing, ZHU Bingxu, LIANG Jiahang, REN Nüer, JI Haipeng. Blockchain Access Control Strategy Based on Mainchain and Sidechain Cooperation [J]. Computer Engineering, 2022, 48(3): 10-16,22.
[5]	MIN Minghui,YANG Zhijia,LI Zhongsheng,LIU Zhifeng. Research on Multi-slot Frame Scheduling Algorithm in Industrial IoT Applications [J]. Computer Engineering, 2016, 42(11): 15-21,26.
[6]	ZHENG Zhao-xia, LI Yi-fan, YU Liang, TIAN Yuan, LIU Zheng-lin. Hardware Trojan Detection Technology Based on Probabilistic Signature [J]. Computer Engineering, 2014, 40(3): 18-22.
[7]	CAO Shi-Tong, CHEN Xian-Fu. A Novel Quantum Evolutionary Algorithm and Its Application Research [J]. Computer Engineering, 2012, 38(24): 188-190.
[8]	JI Jie, BAI Rui-Lin, CHEN Da-Feng, TANG Chao. Design and Implementation of Topology Discovery Protocol for Industrial Internet of Things [J]. Computer Engineering, 2012, 38(11): 221-224.
[9]	YUAN Gong-Lin, ZHANG Shi-Bing, YANG Yong-Jie, LI Wei, JIANG Hua. Transform Method of Separability Enhanced Frame Synchronization Code Radio Frequency Fingerprints [J]. Computer Engineering, 2011, 37(20): 7-9.

Please choose a citation manager

Content to export