Depend Ability Analysis on CPS Using Machine Learning Techniques
DOI:
https://doi.org/10.37385/jaets.v5i1.3411Keywords:
CPS, Dependability, Machine Learning, ComponentsAbstract
A Cyber-Physical System (CPS) is an entity that effortlessly monitors and controls physical operations by integrating computational and physical elements. Dependability on the CPS application program and also proposes a real-time analysis approach to CPS application dependability based upon Artificial Intelligence and Machine Learning (ML). For starters, pick complicated networks to determine tips within the system topology on the CPS application process. Unsupervised mastering category by a quick density clustering algorithm to classify the value of nodes could be successfully put on the crucial analysis of nodes in CPS application program as well as help support the setting up of CPS software phone Secondly, a real-time CPS dependability automated internet analysis technique is suggested. Unreliable methods are able to imply big losses, each monetarily in addition to within man's life. On a good mention, CPS has information like the main component of the operation of theirs. The prevalence and availability of information demonstrate a brand-new chance to change the methods within what dependability evaluation continues to be usually performed. This process utilizes printer mastering tips to create an analysis framework, design, and style an internet queuing algorithm, as well as put into action real-time internet evaluation and analysis of CPS dependability. Preventive steps make sure that the device works ordinarily as well as with no interruption, which significantly betters method dependability. Last but not least, simulation final results verify the usefulness of the analysis technique and the broad application prospects of its.
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Ahilan, A., & Jeyam, A. (2023). Breaking Barriers in Conventional Cryptography by Integrating with Quantum Key Distribution. Wireless Personal Communications, 129(1), 549-567. https://doi.org/10.1007/s11277-022-10110-8
Amanullakhan, M., Usha, M., & Ramesh, S. (2023). Intrusion Detection Architecture (IDA) In IOT Based Security System. International Journal of Computer and Engineering Optimization, 01 (01), 33-42. https://kitspress.com/journals/IJCEO/index.php?info=14&issue=25
Anusha, K., Lokesh, S., & Benila Christabel, T. (2023). Deep reinforcement learning based real time violence detection. International Journal of Data Science and Artificial Intelligence, 01(01), 33-40. https://kitspress.com/journals/IJDSAI/index.php?info=14&issue=5
Boursinos, D., & Koutsoukos, X. (2020). Assurance monitoring of cyber-physical systems with machine learning components. arXiv preprint arXiv:2001. 05014.https://doi.org/10.48550/arXiv.2001.05014
Castaño, F., Strzelczak, S., Villalonga, A., Haber, R. E., & Kossakowska, J. (2019). Sensor reliability in cyber-physical systems using internet-of-things data: A review and case study. Remote sensing, 11(19), 2252. https://doi.org/10.3390/rs11192252
Chiu, M. C., Tsai, C. D., & Li, T. L. (2020). An integrative machine learning method to improve fault detection and productivity performance in a cyber-physical system. Journal of Computing and Information Science in Engineering, 20(2), 021009. https://doi.org/10.1115/1.4045663
Fei, X., Shah, N., Verba, N., Chao, K. M., Sanchez-Anguix, V., Lewandowski, J., ... & Usman, Z. (2019). CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey. Future generation computer systems, 90, 435-450. https://doi.org/10.1016/j.future.2018.06.042
Gnana Sophia, S., Thanammal K. K., & Sujatha, S. S. (2023). Secure storage of lung brain multi-modal medical images using DNA homomorphic encryption, International Journal of Current Bio-Medical Engineering, 01, (01), 16-22. https://kitspress.com/journals/IJCBE/index.php?info=14&issue=15
Gutierrez-Rojas, D., Ullah, M., Christou, I. T., Almeida, G., Nardelli, P., Carrillo, D., ... & Kalalas, C. (2020, June). Three-layer approach to detect anomalies in industrial environments based on machine learning. In 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), 1, 250-256. IEEE. DOI: 10.1109/ICPS48405.2020.9274780
Hartsell, C., Mahadevan, N., Ramakrishna, S., Dubey, A., Bapty, T., Johnson, T., ... & Karsai, G. (2019a). Model-based design for CPS with learning-enabled components. In Proceedings of the Workshop on Design Automation for CPS and IoT (pp. 1-9). https://doi.org/10.1145/3313151.3313166
Hartsell, C., Mahadevan, N., Ramakrishna, S., Dubey, A., Bapty, T., Johnson, T., ... & Karsai, G. (2019b). CPS Design with Learning-Enabled Components: A Case Study. In Proceedings of the 30th International Workshop on Rapid System Prototyping (RSP'19), 57-63. https://doi.org/10.1145/3339985.3358491
Kanthavel, R., Dhaya, R., & Ahilan, A. (2022). AI-Based Efficient WUGS Network Channel Modeling and Clustered Cooperative Communication. ACM Transactions on Sensor Networks, 18(3). https://doi.org/10.1145%2F3469034
Khan, M. T., Serpanos, D., Shrobe, H., & Yousuf, M. M. (2020, September). Rigorous machine learning for secure and autonomous cyber physical systems. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 1, 1815-1819. DOI: 10.1109/ETFA46521.2020.9212074
Kumar, S., Cengiz, K., Vimal, S., & Suresh, A. (2021). Energy efficient resource migration-based load balance mechanism for high traffic applications IoT. Wireless personal communications, 1-19. https://doi.org/10.1007/s11277-021-08269-7
Kumar, S., Gautam, K., Singhal, V., & Sharma, N. (2023). Cloud and deep learning-based image analyzer. Journal of Electronic Imaging, 32(2), 021602-021602. https://doi.org/10.1117/1.JEI.32.2.021602
Kumar, S., Ranjan, P., Radhakrishnan, R., & Tripathy, M. R. (2017a). Energy efficient multichannel MAC protocol for high traffic applications in heterogeneous wireless sensor networks. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering), 10(3), 223-232. https://doi.org/10.2174/2352096510666170601090202
Kumar, S., Ranjan, P., Ramaswami, R., & Tripathy, M. R. (2017b). Resource efficient clustering and next hop knowledge-based routing in multiple heterogeneous wireless sensor networks. International Journal of Grid and High-Performance Computing (IJGHPC), 9(2), 1-20. DOI: 10.4018/IJGHPC.2017040101
Li, S., He, H., Li, J., Yin, P., & Wang, H. (2019, September). Machine learning algorithm-based battery modeling and management method: A Cyber-Physical System perspective. In 2019 3rd Conference on Vehicle Control and Intelligence (CVCI), IEEE, 1-4. DOI: 10.1109/CVCI47823.2019.8951635
Liu, G., Yang, H., Fu, Y., Mao, C., Xu, P., Hong, J., & Li, R. (2020). Cyber-physical system-based real-time monitoring and visualization of greenhouse gas emissions of prefabricated construction. Journal of Cleaner Production, 246, 119059. https://doi.org/10.1016/j.jclepro.2019.119059
Liu, Y., Liu, A., Liu, X., & Ma, M. (2019). A trust-based active detection for cyber-physical security in industrial environments. IEEE Transactions on Industrial Informatics, 15(12), 6593-6603. DOI: 10.1109/TII.2019.2931394
Lv, Z., Han, Y., Singh, A. K., Manogaran, G., & Lv, H. (2020). Trustworthiness in industrial IoT systems based on artificial intelligence. IEEE Transactions on Industrial Informatics, 17(2), 1496-1504. DOI: 10.1109/TII.2020.2994747
Martins, J. P. S., Rodrigues, F. M., & Henriques, N. (2020). Modeling system based on machine learning approaches for predictive maintenance applications. KnE Engineering, 857-871. DOI: 10.18502/keg. v5i6.7105
Mozaffari, F. S., Karimipour, H., & Parizi, R. M. (2020). Learning based anomaly detection in critical cyber-physical systems. Security of Cyber-Physical Systems: Vulnerability and Impact, 107-130. https://doi.org/10.1007/978-3-030-45541-5_6
Muthukumar, S., Hevin Rajesh, A., & Jenice Prabhu, D. (2023). Reduancy aware dynamic routing protocol using salp swarm optimization algorithm. International Journal of System Design and Computing, 01, (01), 35-42. https://kitspress.com/journals/IJSDC/index.php?info=14&issue=12
Pan, F., Pang, Z., Wen, H., Luvisotto, M., Xiao, M., Liao, R. F., & Chen, J. (2019). Threshold-free physical layer authentication based on machine learning for industrial wireless CPS. IEEE Transactions on Industrial Informatics, 15(12), 6481-6491. DOI: 10.1109/TII.2019.2925418
Pham, B. T., Jaafari, A., Avand, M., Al-Ansari, N., Dinh Du, T., Yen, H. P. H., ... & Tuyen, T. T. (2020). Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry, 12(6), 1022. https://doi.org/10.3390/sym12061022
Prabhu, M., Muthu Kumar, B., & Ahilan, A. (2023). Slime Mould Algorithm based Fuzzy Linear CFO Estimation in Wireless Sensor Networks. IETE Journal of Research, 1-11. https://doi.org/10.1080/03772063.2023.2194279
Sankareswaran, S. P., & Krishnan, M. (2022). Unsupervised end-to-end brain tumor magnetic resonance image registration using RBCNN: rigid transformation, B-spline transformation and convolutional neural network. Current Medical Imaging, 18(4), 387-397. https://doi.org/10.2174/1573405617666210806125526
Senthilselvi, A., Sellam, V., Alahmari, S. A., & Rajeyyagari, S. (2020). Accuracy enhancement in mobile phone recycling process using machine learning technique and MEPH process. Environmental Technology & Innovation, 20, 101137. https://doi.org/10.1016/j.eti.2020.101137
Shengdong, M., Zhengxian, X., & Yixiang, T. (2019). Intelligent traffic control system based on cloud computing and big data mining. IEEE Transactions on Industrial Informatics, 15(12), 6583-6592. DOI: 10.1109/TII.2019.2929060
Sigamani, R. M. S. (2020). Adoption of Machine Learning with Adaptive Approach for Securing CPS. In Handbook of Research on Machine and Deep Learning Applications for Cyber Security (pp. 388-415). IGI Global. DOI: 10.4018/978-1-5225-9611-0.ch018
Sreetha, G., & Chitra, T. V. Block Chain Assisted Cloud Based Medical Data Storage Via Quantum Diffie-Hellman Key Exchange. International Journal of Computer and Engineering Optimization, 01(01), 01-09, 2023. https://kitspress.com/journals/IJCEO/index.php?info=14&issue=21
Surya, V., & Senthilselvi, A. (2020). A qualitative analysis of the machine learning methods in food adultery: a focus on Milk adulteration detection. J Adv Res Dyn Control Syst, 12(7), 543-551.
Surya, V., & Senthilselvi, A. (2022). Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm. Neural Computing and Applications, 1-15. https://doi.org/10.1007/s00521-021-06829-3
Therase, J., Allwin, S., & Ahilan, A. (2023). Full Duplex Media Access Control Protocol for Multihop Network Computing. Computer Systems Science & Engineering, 44(1). https://doi.org/10.32604/csse.2023.023515
Usha, M., Mahalingam, T., Ahilan, A., & Sathiamoorthy, J. (2023). EOEEORFP: Eagle Optimized Energy Efficient Optimal Route-Finding Protocol for Secure Data Transmission in FANETs. IETE Journal of Research, DOI: 10.1080/03772063.2023.2253777
Wang, D., Wang, X., Zhang, Y., & Jin, L. (2019). Detection of power grid disturbances and cyber-attacks based on machine learning. Journal of information security and applications, 46, 42-52. https://doi.org/10.1016/j.jisa.2019.02.008
Yu, J. J., Kim, D. W., Lee, E. J., & Son, S. W. (2020). Determining the optimal number of ground control points for varying study sites through accuracy evaluation of unmanned aerial system-based 3D point clouds and digital surface models. Drones, 4(3), 49. https://doi.org/10.3390/drones4030049
Zhang, Y., Chu, L., Ou, Y., Guo, C., Liu, Y., & Tang, X. (2019). A cyber-physical system-based velocity-profile prediction method and case study of application in plug-in hybrid electric vehicle. IEEE transactions on cybernetics, 51(1), 40-51. DOI: 10.1109/TCYB.2019.2928945
Zhou, C., Luo, H., Fang, W., Wei, R., & Ding, L. (2019). Cyber-physical-system-based safety monitoring for blind hoisting with the internet of things: A case study. Automation in construction, 97, 138-150. https://doi.org/10.1016/j.autcon.2018.10.017
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