CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic Arms
Article No.: 18, Pages 1 - 36
Abstract
Industrial cyber-physical systems (ICPS) are widely employed in supervising and controlling critical infrastructures, with manufacturing systems that incorporate industrial robotic arms being a prominent example. The increasing adoption of ubiquitous computing technologies in these systems has led to benefits such as real-time monitoring, reduced maintenance costs, and high interconnectivity. This adoption has also brought cybersecurity vulnerabilities exploited by adversaries disrupting manufacturing processes via manipulating actuator behaviors. Previous incidents in the industrial cyber domain prove that adversaries launch sophisticated attacks rendering network-based anomaly detection mechanisms insufficient as the “physics” involved in the process is overlooked. To address this issue, we propose an IoT-based cyber-physical anomaly detection system that can detect motion-based behavioral changes in an industrial robotic arm. We apply both statistical and state-of-the-art machine learning methods to real-time Inertial Measurement Unit data collected from an edge development board attached to an arm doing a pick-and-place operation. To generate anomalies, we modify the joint velocity of the arm. Our goal is to create an air-gapped secondary protection layer to detect “physical” anomalies without depending on the integrity of network data, thus augmenting overall anomaly detection capability. Our empirical results show that the proposed system, which utilizes 1D convolutional neural networks, can successfully detect motion-based anomalies on a real-world industrial robotic arm. The significance of our work lies in its contribution to developing a comprehensive solution for ICPS security, which goes beyond conventional network-based methods.
References
[1]
Adafruit. 2021. Adafruit Feather nRF52840 Sense. Adafruit Industries. Retrieved Nov 12, 2022 from DOI:https://learn.adafruit.com/adafruit-feather-sense
[2]
Mohiuddin Ahmed and Abdun Naser Mahmood. 2014. Network traffic analysis based on collective anomaly detection. In Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications. IEEE, 1141–1146.
[3]
Mohiuddin Ahmed and Abdun Naser Mahmood. 2015. Novel approach for network traffic pattern analysis using clustering-based collective anomaly detection. Ann. Data Sci. 2, 1 (2015), 111–130.
[4]
Safaa Allamy and Alessandro Lameiras Koerich. 2021. 1D CNN architectures for music genre classification. In Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI’21). IEEE, 01–07.
[5]
Matthew G. Angle, Stuart Madnick, James L. Kirtley, and Shaharyar Khan. 2019. Identifying and anticipating cyberattacks that could cause physical damage to industrial control systems. IEEE Power Energy Technol. Syst. J. 6, 4 (2019), 172–182.
[6]
R. Ani, S. Krishna, N. Anju, M. Sona Aslam, and O. S. Deepa. 2017. Iot based patient monitoring and diagnostic prediction tool using ensemble classifier. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI’17). IEEE, 1588–1593.
[7]
Apple. 2022. Track your sleep with Apple Watch. Apple Inc. Retrieved January 14, 2022 from DOI:https://support.apple.com/en-gb/guide/watch/apd830528336/watchos
[8]
Georgios Athanasakis, Gabriel Filios, Ioannis Katsidimas, Sotiris Nikoletseas, and Stefanos H. Panagiotou. 2022. TinyML-based approach for remaining useful life prediction of turbofan engines. In Proceedings of the IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA’22). IEEE, 1–8.
[9]
R. Ganesh Babu, P. Karthika, and V. Aravinda Rajan. 2019. Secure IoT systems using raspberry Pi machine learning artificial intelligence. In Proceedings of the International Conference on Computer Networks and Inventive Communication Technologies. Springer, Cham, Switzerland, 797–805.
[10]
Barış Bayram, Taha Berkay Duman, and Gökhan Ince. 2021. Real time detection of acoustic anomalies in industrial processes using sequential autoencoders. Expert Syst. 38, 1 (2021), e12564.
[11]
Abdelkareem Bedri, Richard Li, Malcolm Haynes, Raj Prateek Kosaraju, Ishaan Grover, Temiloluwa Prioleau, Min Yan Beh, Mayank Goel, Thad Starner, and Gregory Abowd. 2017. EarBit: Using wearable sensors to detect eating episodes in unconstrained environments. Proc. ACM Interact. Mobile Wear. Ubiq. Technol. 1, 3 (2017), 1–20.
[12]
Edgar A. Bernal, Xitong Yang, Qun Li, Jayant Kumar, Sriganesh Madhvanath, Palghat Ramesh, and Raja Bala. 2017. Deep temporal multimodal fusion for medical procedure monitoring using wearable sensors. IEEE Trans. Multimedia 20, 1 (2017), 107–118.
[13]
Anatolij Bezemskij, George Loukas, Richard J. Anthony, and Diane Gan. 2016. Behaviour-based anomaly detection of cyber-physical attacks on a robotic vehicle. In Proceedings of the 15th International Conference on Ubiquitous Computing and Communications and International Symposium on Cyberspace and Security (IUCC-CSS’16). IEEE, 61–68.
[14]
Monica Bianchini and Franco Scarselli. 2014. On the complexity of neural network classifiers: A comparison between shallow and deep architectures. IEEE Trans. Neural Netw. Learn. Syst. 25, 8 (2014), 1553–1565.
[15]
Ekaba Bisong. 2019. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. Apress, Ottawa, Canada.
[16]
BleepingComputer. 2021. Sierra Wireless Resumes Production After Ransomware Attack. BleepingComputer. Retrieved Nov 12, 2022 from DOI:https://www.bleepingcomputer.com/news/security/sierra-wireless-resumes-production-after-ransomware-attack/
[17]
Chris U. Carmona, François-Xavier Aubet, Valentin Flunkert, and Jan Gasthaus. 2021. Neural Contextual Anomaly Detection for Time Series. Retrieved from https://arxiv:2107.07702
[18]
Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. ACM Comput. Surv. 41, 3, Article 15 (July 2009), 58 pages. DOI:
[19]
Tingting Chen, Xueping Liu, Bizhong Xia, Wei Wang, and Yongzhi Lai. 2020. Unsupervised anomaly detection of industrial robots using sliding-window convolutional variational autoencoder. IEEE Access 8 (2020), 47072–47081.
[20]
Heeryon Cho and Sang Min Yoon. 2018. Divide and conquer-based 1D CNN human activity recognition using test data sharpening. Sensors 18, 4 (2018), 1055.
[21]
Dan Ciregan, Ueli Meier, and Jürgen Schmidhuber. 2012. Multi-column deep neural networks for image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 3642–3649.
[22]
Amazon Elastic Compute Cloud. 2011. Amazon web services. Retrieved November 9, 2011 https://aws.amazon.com/ec2/
[23]
Armando W. Colombo, Stamatis Karnouskos, Okyay Kaynak, Yang Shi, and Shen Yin. 2017. Industrial cyberphysical systems: A backbone of the fourth industrial revolution. IEEE Industr. Electr. Mag. 11, 1 (2017), 6–16.
[24]
Comunicaffè. 2021. Caffitaly, Gli Hacker all’Assalto Delle capsule di Gaggio. Caffitaly. Retrieved 2021-05-30 from DOI:https://www.comunicaffe.it/caffitaly-gli-haker-allassalto-delle-caspule-di-gaggio-montano/
[25]
Robert David, Jared Duke, Advait Jain, Vijay Janapa Reddi, Nat Jeffries, Jian Li, Nick Kreeger, Ian Nappier, Meghna Natraj, Shlomi Regev et al. 2020. Tensorflow lite micro: Embedded machine learning on tinyml systems. Retrieved from https://arXiv:2010.08678
[26]
Essam Debie, Raul Fernandez Rojas, Justin Fidock, Michael Barlow, Kathryn Kasmarik, Sreenatha Anavatti, Matt Garratt, and Hussein A. Abbass. 2019. Multimodal fusion for objective assessment of cognitive workload: A review. IEEE Trans. Cybernet. 51, 3 (2019), 1542–1555.
[27]
Ailin Deng and Bryan Hooi. 2021. Graph neural network-based anomaly detection in multivariate time series. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. AAAI Press, 4027–4035.
[28]
Taha Berkay Duman, Barış Bayram, and Gökhan İnce. 2019. Acoustic anomaly detection using convolutional autoencoders in industrial processes. In Proceedings of the International Workshop on Soft Computing Models in Industrial and Environmental Applications. Springer, Cham, Switzerland, 432–442.
[29]
Sinem Coleri Ergen. 2004. ZigBee/IEEE 802.15. 4 Summary. UC Berkeley, September 10, 17 (2004), 11. https://pages.cs.wisc.edu/˜suman/courses/707/papers/zigbee.pdf
[30]
Pavel Filonov, Andrey Lavrentyev, and Artem Vorontsov. 2016. Multivariate industrial time series with cyber-attack simulation: Fault detection using an lstm-based predictive data model. Retrieved from https://arxiv:1612.06676
[31]
Pedro J. Freire, Sasipim Srivallapanondh, Antonio Napoli, Jaroslaw E. Prilepsky, and Sergei K. Turitsyn. 2022. Computational complexity evaluation of neural network applications in signal processing. Retrieved from https://arXiv:2206.12191
[32]
Ryohei Fujimaki, Takehisa Yairi, and Kazuo Machida. 2005. An approach to spacecraft anomaly detection problem using kernel feature space. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD’05). ACM, New York, NY, 401–410. DOI:
[33]
Yang Gao, Borui Li, Wei Wang, Wenyao Xu, Chi Zhou, and Zhanpeng Jin. 2018. Watching and safeguarding your 3D printer: Online process monitoring against cyber-physical attacks. Proc. ACM Interact. Mobile Wear. Ubiq. Technol. 2, 3 (2018), 1–27.
[34]
Zhiwei Gao, Carlo Cecati, and Steven X. Ding. 2015. A survey of fault diagnosis and fault-tolerant techniques–Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Trans. Industr. Electr. 62, 6 (2015), 3757–3767.
[35]
Aurélien Géron. 2019. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Canada.
[36]
Mohammed Ghazal, Tasnim Basmaji, Maha Yaghi, Mohammad Alkhedher, Mohamed Mahmoud, and Ayman S. El-Baz. 2020. Cloud-based monitoring of thermal anomalies in industrial environments using AI and the internet of robotic things. Sensors 20, 21 (2020), 6348.
[37]
Jonathan Goh, Sridhar Adepu, Marcus Tan, and Zi Shan Lee. 2017. Anomaly detection in cyber physical systems using recurrent neural networks. In Proceedings of the IEEE 18th International Symposium on High Assurance Systems Engineering (HASE’17). IEEE, Singapore, 140–145.
[38]
Victor Gonzalez-Huitron, José A. León-Borges, AE Rodriguez-Mata, Leonel Ernesto Amabilis-Sosa, Blenda Ramírez-Pereda, and Hector Rodriguez. 2021. Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4. Comput. Electr. Agric. 181 (2021), 105951.
[39]
Haodong Guo, Ling Chen, Liangying Peng, and Gencai Chen. 2016. Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, New York, NY, 1112–1123.
[40]
Kevin Gurney. 2018. An Introduction to Neural Networks. CRC Press.
[41]
Juan Haladjian, Daniel Schlabbers, Sajjad Taheri, Max Tharr, and Bernd Bruegge. 2020. Sensor-based detection and classification of soccer goalkeeper training exercises. ACM Trans. Internet Things 1, 2, Article 12 (Apr. 2020), 20 pages. DOI:
[42]
Danfeng Hong, Naoto Yokoya, Gui-Song Xia, Jocelyn Chanussot, and Xiao Xiang Zhu. 2020. X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data. ISPRS J. Photogram. Remote Sens. 167 (2020), 12–23.
[43]
Yan Hu, An Yang, Hong Li, Yuyan Sun, and Limin Sun. 2018. A survey of intrusion detection on industrial control systems. Int. J. Distrib. Sensor Netw. 14, 8 (2018), 1550147718794615.
[44]
Turker Ince, Serkan Kiranyaz, Levent Eren, Murat Askar, and Moncef Gabbouj. 2016. Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans. Industr. Electr. 63, 11 (2016), 7067–7075.
[45]
Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, and Jun Sun. 2017. Anomaly detection for a water treatment system using unsupervised machine learning. In Proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW’17). IEEE, New Orleans, LA, USA, 1058–1065.
[46]
Rolf Isermann. 1997. Supervision, fault-detection and fault-diagnosis methods–an introduction. Control Eng. Pract. 5, 5 (1997), 639–652.
[47]
Rolf Isermann. 2005. Model-based fault-detection and diagnosis–status and applications. Annu. Rev. Control 29, 1 (2005), 71–85.
[48]
Tariqul Islam, Md Saiful Islam, Md Shajid-Ul-Mahmud, and Md Hossam-E-Haider. 2017. Comparison of complementary and Kalman filter based data fusion for attitude heading reference system. In Proceedings of the AIP Conference (Dhaka, Bangladesh), Vol. 1919. AIP Publishing LLC, New York, NY, 020002.
[49]
Gopal Chandra Jana, Ratna Sharma, and Anupam Agrawal. 2020. A 1D-CNN-spectrogram based approach for seizure detection from EEG signal. Procedia Comput. Sci. 167 (2020), 403–412.
[50]
Fazle Karim, Somshubra Majumdar, Houshang Darabi, and Samuel Harford. 2019. Multivariate LSTM-FCNs for time series classification. Neural Netw. 116 (2019), 237–245.
[51]
Hakan Kayan, Yasar Majib, Wael Alsafery, Mahmoud Barhamgi, and Charith Perera. 2021. AnoML-IoT: An end to end re-configurable multi-protocol anomaly detection pipeline for Internet of Things. Internet Things 16 (2021), 100437.
[52]
Hakan Kayan, Matthew Nunes, Omer Rana, Pete Burnap, and Charith Perera. 2022. Cybersecurity of industrial cyber-physical systems: A review. ACM Comput. Surv. 54, 11s, Article 229 (Sep. 2022), 35 pages.
[53]
Haider Adnan Khan, Nader Sehatbakhsh, Luong N. Nguyen, Robert L. Callan, Arie Yeredor, Milos Prvulovic, and Alenka Zajić. 2019. IDEA: Intrusion detection through electromagnetic-signal analysis for critical embedded and cyber-physical systems. IEEE Trans. Depend. Secure Comput. 18, 3 (2019), 1150–1163.
[54]
Haider Adnan Khan, Nader Sehatbakhsh, Luong N. Nguyen, Robert L. Callan, Arie Yeredor, Milos Prvulovic, and Alenka Zajić. 2019. IDEA: Intrusion detection through electromagnetic-signal analysis for critical embedded and cyber-physical systems. IEEE Trans. Depend. Secure Comput. 18, 3 (2019), 1150–1163.
[55]
Serkan Kiranyaz, Onur Avci, Osama Abdeljaber, Turker Ince, Moncef Gabbouj, and Daniel J. Inman. 2021. 1D convolutional neural networks and applications: A survey. Mech. Syst. Signal Process. 151 (2021), 107398.
[56]
Daniel Knight. 2021. DietPi OS. DietPi. Retrieved Nov 12, 2022 from DOI:https://dietpi.com/
[57]
Moshe Kravchik and Asaf Shabtai. 2018. Detecting cyber attacks in industrial control systems using convolutional neural networks. In Proceedings of the Workshop on Cyber-Physical Systems Security and PrivaCy. ACM, New York, NY, 72–83.
[58]
Andrew Kusiak. 2018. Smart manufacturing. Int. J. Prod. Res. 56, 1-2 (2018), 508–517.
[59]
Prasanth Lade, Rumi Ghosh, and Soundar Srinivasan. 2017. Manufacturing analytics and industrial internet of things. IEEE Intell. Syst. 32, 3 (2017), 74–79.
[60]
Ralph Langner. 2011. Stuxnet: Dissecting a cyberwarfare weapon. IEEE Secur. Privacy 9, 3 (2011), 49–51.
[61]
Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, and Pascal Lamblin. 2009. Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10, 1 (2009), 1–40.
[62]
Heiner Lasi, Peter Fettke, Hans-Georg Kemper, Thomas Feld, and Michael Hoffmann. 2014. Industry 4.0. Bus. Info. Syst. Eng. 6, 4 (2014), 239–242.
[63]
Yann LeCun, Yoshua Bengio et al. 1995. Convolutional networks for images, speech, and time series. Handbook Brain Theory Neural Netw. 3361, 10 (1995), 1995.
[64]
Dan Li, Dacheng Chen, Baihong Jin, Lei Shi, Jonathan Goh, and See-Kiong Ng. 2019. MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In Proceedings of the International Conference on Artificial Neural Networks. Springer, Cham, Switzerland, 703–716.
[65]
Guangxia Li, Yulong Shen, Peilin Zhao, Xiao Lu, Jia Liu, Yangyang Liu, and Steven C. H. Hoi. 2019. Detecting cyberattacks in industrial control systems using online learning algorithms. Neurocomputing 364 (2019), 338–348.
[66]
Zhe Li, Jingyue Li, Yi Wang, and Kesheng Wang. 2019. A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment. Int. J. Adv. Manufact. Technol. 103, 1 (2019), 499–510.
[67]
Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, and Jun Zhou. 2021. A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. (2021), 1–21.
[68]
Hung-Jen Liao, Chun-Hung Richard Lin, Ying-Chih Lin, and Kuang-Yuan Tung. 2013. Intrusion detection system: A comprehensive review. J. Netw. Comput. Appl. 36, 1 (2013), 16–24.
[69]
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation forest. In Proceedings of the 8th IEEE International Conference on Data Mining. IEEE, 413–422.
[70]
Qi Liu, Rudy Klucik, Chao Chen, Glenn Grant, David Gallaher, Qin Lv, and Li Shang. 2017. Unsupervised detection of contextual anomaly in remotely sensed data. Remote Sens. Environ. 202 (2017), 75–87.
[71]
Marc Moreno Lopez and Jugal Kalita. 2017. Deep Learning applied to NLP. Retrieved from https://arxiv:1703.03091
[72]
Huimin Lu, Yujie Li, Shenglin Mu, Dong Wang, Hyoungseop Kim, and Seiichi Serikawa. 2017. Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J. 5, 4 (2017), 2315–2322.
[73]
Simone A. Ludwig, Kaleb D. Burnham, Antonio R. Jiménez, and Pierre A. Touma. 2018. Comparison of attitude and heading reference systems using foot mounted MIMU sensor data: Basic, Madgwick, and Mahony. In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, Vol. 10598. SPIE, 644–650.
[74]
Chunjie Luo, Fan Zhang, Cheng Huang, Xingwang Xiong, Jianan Chen, Lei Wang, Wanling Gao, Hainan Ye, Tong Wu, Runsong Zhou et al. 2018. AIoT bench: Towards comprehensive benchmarking mobile and embedded device intelligence. In Proceedings of the International Symposium on Benchmarking, Measuring and Optimization. Springer, Cham, Switzerland, 31–35.
[75]
Zhiqing Luo, Mingxuan Yan, Wei Wang, and Qian Zhang. 2023. Non-intrusive anomaly detection of industrial robot operations by exploiting nonlinear effect. Proc. ACM Interact. Mobile Wear. Ubiq. Technol. 6, 4 (2023), 1–27.
[76]
Sebastian O. H. Madgwick, Andrew J. L. Harrison, and Ravi Vaidyanathan. 2011. Estimation of IMU and MARG orientation using a gradient descent algorithm. In Proceedings of the IEEE International Conference on Rehabilitation Robotics. IEEE, Zurich, Switzerland, 1–7.
[77]
Robert Mahony, Tarek Hamel, and Jean-Michel Pflimlin. 2008. Nonlinear complementary filters on the special orthogonal group. IEEE Trans. Autom. Control 53, 5 (2008), 1203–1218.
[78]
Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. 2015. Long short term memory networks for anomaly detection in time series. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Vol. 89. IEEE, 89–94.
[79]
S. Manimurugan. 2021. IoT-Fog-Cloud model for anomaly detection using improved Naïve Bayes and principal component analysis. J. Ambient Intell. Human. Comput. 12, 2 (2021), 1--10.
[80]
Aditya P. Mathur and Nils Ole Tippenhauer. 2016. SWaT: A water treatment testbed for research and training on ICS security. In Proceedings of the International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater’16). IEEE, 31–36.
[81]
Siamak Mehrkanoon. 2019. Deep shared representation learning for weather elements forecasting. Knowl.-Based Syst. 179 (2019), 120–128.
[82]
Microsoft. 2022. AZURE. Microsoft Corporation. Retrieved Nov 2, 2022 from DOI:https://azure.microsoft.com/en-gb/
[83]
Charlie Miller and Chris Valasek. 2014. A survey of remote automotive attack surfaces. black hat USA 2014 (2014), 94.
[84]
Jelena Mirkovic and Peter Reiher. 2004. A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Comput. Commun. Rev. 34, 2 (2004), 39–53.
[85]
Ali Moin, Andy Zhou, Abbas Rahimi, Alisha Menon, Simone Benatti, George Alexandrov, Senam Tamakloe, Jonathan Ting, Natasha Yamamoto, Yasser Khan et al. 2021. A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition. Nature Electr. 4, 1 (2021), 54–63.
[86]
Sebastian Münzner, Philip Schmidt, Attila Reiss, Michael Hanselmann, Rainer Stiefelhagen, and Robert Dürichen. 2017. CNN-based sensor fusion techniques for multimodal human activity recognition. In Proceedings of the ACM International Symposium on Wearable Computers. ACM, New York, NY, 158–165.
[87]
Andrew Murphy. 2022. Industrial: Robotics Outlook 2025. Loup Funds, LLC. Retrieved February 23, 2022 from DOI:https://loupfunds.com/industrial-robotics-outlook-2025/
[88]
Vedanth Narayanan and Rakesh B. Bobba. 2018. Learning based anomaly detection for industrial arm applications. In Proceedings of the Workshop on Cyber-Physical Systems Security and PrivaCy (CPS-SPC’18). ACM, New York, NY, 13–23. DOI:
[89]
Vedanth Narayanan and Rakesh B. Bobba. 2018. Learning based anomaly detection for industrial arm applications. In Proceedings of the Workshop on Cyber-Physical Systems Security and PrivaCy. ACM, 13–23. DOI:
[90]
Mao V. Ngo, Tie Luo, and Tony Q. S. Quek. 2021. Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A contextual-bandit approach. ACM Trans. Internet Things 3, 1, Article 4 (Oct. 2021), 23 pages. DOI:
[91]
Long D. Nguyen, Dongyun Lin, Zhiping Lin, and Jiuwen Cao. 2018. Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS’18). IEEE, New York, NY, 1–5.
[92]
Zhiyou Ouyang, Xiaokui Sun, Jingang Chen, Dong Yue, and Tengfei Zhang. 2018. Multi-view stacking ensemble for power consumption anomaly detection in the context of industrial internet of things. IEEE Access 6 (2018), 9623–9631.
[93]
Donghyun Park, Seulgi Kim, Yelin An, and Jae-Yoon Jung. 2018. LiReD: A light-weight real-time fault detection system for edge computing using LSTM recurrent neural networks. Sensors 18, 7 (2018), 2110.
[94]
Koeppe Patrick. 2020. HUBER+SUHNER: Gradually Resumes Production After Cyberattack | MarketScreener. Surperformance SAS. Retrieved 30 may, 2022 from DOI:https://www.marketscreener.com/quote/stock/HUBER-SUHNER-AG-278523/news/HUBER-SUHNER-nbsp-gradually-resumes-production-after-cyberattack-32074407/
[95]
D Pavithra and Ranjith Balakrishnan. 2015. IoT based monitoring and control system for home automation. In Proceedings of the Global Conference on Communication Technologies (GCCT’15). IEEE, 169–173.
[96]
Ángel Luis Perales Gómez, Lorenzo Fernández Maimó, Alberto Huertas Celdrán, and Félix J. García Clemente. 2020. Madics: A methodology for anomaly detection in industrial control systems. Symmetry 12, 10 (2020), 1583.
[97]
S. R. Prathibha, Anupama Hongal, and M. P. Jyothi. 2017. IoT based monitoring system in smart agriculture. In Proceedings of the International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT’17). IEEE, 81–84.
[98]
Associated Press. 2021. Hacker Tries to Poison Water Supply in Florida City. Telegraph Media Group. Retrieved May 3, 2021 from DOI:https://www.telegraph.co.uk/news/2021/02/09/hacker-tries-poison-water-supply-florida-city/
[99]
Australian Associated Press. 2019. Systems Shut Down in Victorian Hospitals After Suspected Cyber Attack. Guardian Media Group. Retrieved May 30, 2022 from DOI:http://www.theguardian.com/australia-news/2019/oct/01/systems-shut-down-in-victorian-hospitals-after-suspected-cyber-attack
[100]
Mohammad Riazi, Osmar Zaiane, Tomoharu Takeuchi, Anthony Maltais, Johannes Günther, and Micheal Lipsett. 2019. Detecting the onset of machine failure using anomaly detection methods. In Proceedings of the International Conference on Big Data Analytics and Knowledge Discovery. Springer, Cham, Switzerland, 3–12.
[101]
Mauro Ribeiro, Katarina Grolinger, and Miriam A. M. Capretz. 2015. MLaaS: Machine learning as a service. In Proceedings of the IEEE 14th International Conference on Machine Learning and Applications (ICMLA’15). IEEE, New York, NY, 896–902.
[102]
Haakon Ringberg, Matthew Roughan, and Jennifer Rexford. 2008. The need for simulation in evaluating anomaly detectors. ACM SIGCOMM Comput. Commun. Rev. 38, 1 (2008), 55–59.
[103]
Alina Roitberg, Nikhil Somani, Alexander Perzylo, Markus Rickert, and Alois Knoll. 2015. Multimodal human activity recognition for industrial manufacturing processes in robotic workcells. In Proceedings of the ACM International Conference on Multimodal Interaction. ACM, New York, NY, 259–266.
[104]
Beth Romanik. 2013. Prison Computer ’Glitch’ Blamed for Opening Cell Doors in Maximum-Security Wing. Techwell Insights. Retrieved February 28, 2021 from DOI:https://www.techwell.com/techwell-insights/2013/08/computer-glitch-blamed-opening-prison-cell-doors
[105]
Ellen Rushe and Brian Mac Namee. 2019. Anomaly detection in raw audio using deep autoregressive networks. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’19). IEEE, 3597–3601.
[106]
Ali M. Sadeghioon, Nicole Metje, David Chapman, and Carl Anthony. 2018. Water pipeline failure detection using distributed relative pressure and temperature measurements and anomaly detection algorithms. Urban Water J. 15, 4 (2018), 287–295.
[107]
Anam Sajid, Haider Abbas, and Kashif Saleem. 2016. Cloud-assisted IoT-based SCADA systems security: A review of the state of the art and future challenges. IEEE Access 4 (2016), 1375–1384.
[108]
Yasushi Sakurai, Yasuko Matsubara, and Christos Faloutsos. 2015. Mining and forecasting of big time-series data. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, New York, NY, 919–922.
[109]
Hojjat Salehinejad, Sharan Sankar, Joseph Barfett, Errol Colak, and Shahrokh Valaee. 2017. Recent advances in recurrent neural networks. Retrieved from https://arxiv:1801.01078.
[110]
Raed Abdel Sater and A. Ben Hamza. 2021. A federated learning approach to anomaly detection in smart buildings. ACM Trans. Internet Things 2, 4, Article 28 (Aug. 2021), 23 pages. DOI:
[111]
Debarshi Sen, Amirali Aghazadeh, Ali Mousavi, Satish Nagarajaiah, Richard Baraniuk, and Anand Dabak. 2019. Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes. Mech. Syst. Signal Process. 131 (2019), 524–537.
[112]
Gauri Shah and Aashis Tiwari. 2018. Anomaly detection in IIoT: A case study using machine learning. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. ACM, 295–300.
[113]
Syed Maaz Shahid, Sunghoon Ko, and Sungoh Kwon. 2022. Performance comparison of 1D and 2D convolutional neural networks for real-time classification of time series sensor data. In Proceedings of the International Conference on Information Networking (ICOIN’22). 507–511. DOI:
[114]
Matti Siekkinen, Markus Hiienkari, Jukka K. Nurminen, and Johanna Nieminen. 2012. How low energy is bluetooth low energy? comparative measurements with zigbee/802.15. 4. In Proceedings of the IEEE Wireless Communications and Networking Conference Workshops (WCNCW’12). IEEE, 232–237.
[115]
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel et al. 2017. Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. Retrieved from https://arxiv:1712.01815
[116]
Emiliano Sisinni, Abusayeed Saifullah, Song Han, Ulf Jennehag, and Mikael Gidlund. 2018. Industrial internet of things: Challenges, opportunities, and directions. IEEE Trans. Industr. Info. 14, 11 (2018), 4724–4734. DOI:
[117]
Daniel Sonntag, Sonja Zillner, Patrick van der Smagt, and András Lörincz. 2017. Overview of the CPS for smart factories project: Deep learning, knowledge acquisition, anomaly detection and intelligent user interfaces. In Industrial Internet of Things. Springer, Cham, Switzerland, 487–504.
[118]
Thomas Stibor, Jonathan Timmis, and Claudia Eckert. 2005. A comparative study of real-valued negative selection to statistical anomaly detection techniques. In Proceedings of the International Conference on Artificial Immune Systems. Springer, Berlin, 262–275.
[119]
Ljiljana Stojanovic, Marko Dinic, Nenad Stojanovic, and Aleksandar Stojadinovic. 2016. Big-data-driven anomaly detection in industry (4.0): An approach and a case study. In Proceedings of the IEEE International Conference on Big Data (Big Data). IEEE, Washington, DC, 1647–1652.
[120]
Abdulhamit Subasi, Dalia H. Dammas, Rahaf D. Alghamdi, Raghad A. Makawi, Eman A. Albiety, Tayeb Brahimi, and Akila Sarirete. 2018. Sensor-based human activity recognition using adaboost ensemble classifier. Procedia Comput. Sci. 140 (2018), 104–111.
[121]
Tomasz Szandała. 2021. Review and comparison of commonly used activation functions for deep neural networks. Bio-Inspired Neurocomput. 903 (2021), 203–224.
[122]
Rui Tan, Varun Badrinath Krishna, David K. Y. Yau, and Zbigniew Kalbarczyk. 2013. Impact of integrity attacks on real-time pricing in smart grids. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. ACM, New York, NY, 439–450.
[123]
Pavol Tanuska, Lukas Spendla, Michal Kebisek, Rastislav Duris, and Maximilian Stremy. 2021. Smart anomaly detection and prediction for assembly process maintenance in compliance with industry 4.0. Sensors 21, 7 (2021), 2376.
[124]
The Arduino Team. 2021. Nano 33 BLE Sense: Arduino Documentation. Arduino. Retrieved Nov 12, 2022 from DOI:https://docs.arduino.cc/hardware/nano-33-ble-sense
[125]
The Arduino Team. 2021. Nicla Sense ME. Arduino. Retrieved from DOI:http://store.arduino.cc/collections/sensors-environment/products/nicla-sense-me
[126]
Joe Tidy. 2021. Colonial hack: How did cyber-attackers shut off pipeline? BBC. Retrieved May 13, 2021 from DOI:https://www.bbc.com/news/technology-57063636
[127]
Chi-Ho Tsang and Sam Kwong. 2005. Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction. In Proceedings of the IEEE International Conference on Industrial Technology. IEEE, Hong Kong, China, 51–56.
[128]
David I. Urbina, David I. Urbina, Jairo Giraldo, Alvaro A. Cardenas, Junia Valente, Mustafa Faisal, Nils Ole Tippenhauer, Justin Ruths, Richard Candell, and Henrik Sandberg. 2016. Survey and New Directions for Physics-Based Attack Detection in Control Systems. U.S. Department of Commerce, NIST, College Park, MD.
[129]
Tuan Vuong, Avgoustinos Filippoupolitis, George Loukas, and Diane Gan. 2014. Physical indicators of cyber attacks against a rescue robot. In Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM’14). IEEE, New York, NY, 338–343.
[130]
Kun Wang, Yihui Wang, Yanfei Sun, Song Guo, and Jinsong Wu. 2016. Green industrial internet of things architecture: An energy-efficient perspective. IEEE Commun. Mag. 54, 12 (2016), 48–54. DOI:
[131]
Leyi Wei, Shixiang Wan, Jiasheng Guo, and Kelvin K. L. Wong. 2017. A novel hierarchical selective ensemble classifier with bioinformatics application. Artific. Intell. Med. 83 (2017), 82–90.
[132]
Actusnews Wire. 2021. MND. Actusnews. Retrieved Nov 12, 2022 from DOI:https://www.actusnews.com/en/mnd/pr/2021/03/24/mnd-statement-on-cyber-attack
[133]
Dazhong Wu, Shaopeng Liu, Li Zhang, Janis Terpenny, Robert X. Gao, Thomas Kurfess, and Judith A. Guzzo. 2017. A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. J. Manufact. Syst. 43 (2017), 25–34.
[134]
Mingtao Wu, Zhengyi Song, and Young B. Moon. 2019. Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods. J. Intell. Manufact. 30, 3 (2019), 1111–1123.
[135]
Weizhong Yan and Lijie Yu. 2019. Neural Contextual Anomaly Detection for Time Series. Retrieved from https://arxiv:1908.09238
[136]
Hasan Yetis and Mehmet Karakose. 2018. Image processing based anomaly detection approach for synchronous movements in cyber-physical systems. In Proceedings of the 23rd International Scientific-Professional Conference on Information Technology (IT’18). IEEE, Zabljak, Montenegro, 1–4.
[137]
Dong Yi, Zhen Lei, and Stan Z. Li. 2015. Shared representation learning for heterogenous face recognition. In Proceedings of the 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG’15), Vol. 1. IEEE, Ljubljana, Slovenia, 1–7.
[138]
Ashkan Yousefpour, Caleb Fung, Tam Nguyen, Krishna Kadiyala, Fatemeh Jalali, Amirreza Niakanlahiji, Jian Kong, and Jason P. Jue. 2019. All one needs to know about fog computing and related edge computing paradigms: A complete survey. J. Syst. Architect. 98 (2019), 289–330.
[139]
Huitaek Yun, Hanjun Kim, Young Hun Jeong, and Martin B. G. Jun. 2023. Autoencoder-based anomaly detection of industrial robot arm using stethoscope based internal sound sensor. J. Intell. Manufact. 34, 3 (2023), 1427–1444.
[140]
Dingwen Zhang, Guohai Huang, Qiang Zhang, Jungong Han, Junwei Han, and Yizhou Yu. 2021. Cross-modality deep feature learning for brain tumor segmentation. Pattern Recogn. 110 (2021), 107562.
[141]
Fukai Zhang, Ce Li, and Feng Yang. 2019. Vehicle detection in urban traffic surveillance images based on convolutional neural networks with feature concatenation. Sensors 19, 3 (2019), 594.
Index Terms
- CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic Arms
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Publication History
Published: 01 August 2024
Online AM: 01 June 2024
Accepted: 13 May 2024
Revised: 10 May 2024
Received: 20 April 2023
Published in TIOT Volume 5, Issue 3
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