Research Article
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Year 2023, Volume: 11 Issue: 2, 193 - 206, 31.12.2023
https://doi.org/10.17093/alphanumeric.1404181

Abstract

References

  • Ahmed, C. M., Palleti, V. R., & Mathur, A. P. (2017, April 21). WADI: a water distribution testbed for research in the design of secure cyber physical systems. Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks. https://doi.org/10.1145/3055366.3055375
  • Alguliyev, R., Sukhostat, L., & Mammadov, A. (2022, October 12). Anomaly Detection in Cyber-Physical Systems based on BiGRU-VAE. 2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT). https://doi.org/10.1109/aict55583.2022.10013581
  • Alrowais, F., Mohamed, H. G., Al-Wesabi, F. N., Al Duhayyim, M., Hilal, A. M., & Motwakel, A. (2023, May). Cyber attack detection in healthcare data using cyber-physical system with optimized algorithm. Computers and Electrical Engineering, 108, 108636. https://doi.org/10.1016/j.compeleceng.2023.108636
  • Ashraf, I., Narra, M., Umer, M., Majeed, R., Sadiq, S., Javaid, F., & Rasool, N. (2022, February 21). A Deep Learning-Based Smart Framework for Cyber-Physical and Satellite System Security Threats Detection. Electronics, 11(4), 667. https://doi.org/10.3390/electronics11040667
  • Chen, T. M., & Abu-Nimeh, S. (2011, April). Lessons from Stuxnet. Computer, 44(4), 91–93. https://doi.org/10.1109/mc.2011.115
  • D., L., Nagpal, N., Chandrasekaran, S., & D., J. H. (2023, March). A quantum-based approach for offensive security against cyber attacks in electrical infrastructure. Applied Soft Computing, 136, 110071. https://doi.org/10.1016/j.asoc.2023.110071
  • Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J. J., Sandhu, S., Guppy, K. H., Lee, S., & Froelicher, V. (1989, August). International application of a new probability algorithm for the diagnosis of coronary artery disease. The American Journal of Cardiology, 64(5), 304–310. https://doi.org/10.1016/0002-9149(89)90524-9
  • EU monitor. (2008, December). Directive 2008/114 - Identification and designation of European critical infrastructures and the assessment of the need to improve their protection. Retrieved December 12, 2023, from https://www.eumonitor.eu/9353000/1/j9vvik7m1c3gyxp/vitgbgipfoqy
  • Faramondi, L., Flammini, F., Guarino, S., & Setola, R. (2021). A Hardware-in-the-Loop Water Distribution Testbed Dataset for Cyber-Physical Security Testing. IEEE Access, 9, 122385–122396. https://doi.org/10.1109/access.2021.3109465
  • Ferrag, M. A., Friha, O., Hamouda, D., Maglaras, L., & Janicke, H. (2022). Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning. IEEE Access, 10, 40281–40306. https://doi.org/10.1109/access.2022.3165809
  • Frazão, I., Abreu, P. H., Cruz, T., Araújo, H., & Simões, P. (2019). Denial of service attacks: Detecting the frailties of machine learning algorithms in the classification process. In Lecture Notes in Computer Science. Critical Information Infrastructures Security (pp. 230–235). https://doi.org/10.1007/978-3-030-05849-4_19
  • Funchal, G., Pedrosa, T., Vallim, M., & Leitao, P. (2020, July 20). Security for a Multi-Agent Cyber-Physical Conveyor System using Machine Learning. 2020 IEEE 18th International Conference on Industrial Informatics (INDIN). https://doi.org/10.1109/indin45582.2020.9478915
  • Geiger, M., Bauer, J., Masuch, M., & Franke, J. (2020, September). An Analysis of Black Energy 3, Crashoverride, and Trisis, Three Malware Approaches Targeting Operational Technology Systems. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). https://doi.org/10.1109/etfa46521.2020.9212128
  • Guarino, S., Faramondi, L., Setola, R. & Flammini, F. (2021). A hardware-in-the-loop water distribution testbed (WDT) dataset for cyber-physical security testing. IEEE Dataport. https://dx.doi.org/10.21227/rbvf-2h90
  • Habib, A. A., Hasan, M. K., Alkhayyat, A., Islam, S., Sharma, R., & Alkwai, L. M. (2023, April). False data injection attack in smart grid cyber physical system: Issues, challenges, and future direction. Computers and Electrical Engineering, 107, 108638. https://doi.org/10.1016/j.compeleceng.2023.108638
  • Han, M.L., Kwak, B.I., & Kim, H.K. (2018). Anomaly intrusion detection method for vehicular networks based on survival analysis. Vehicular Communications, Volume 14, 2018, Pages 52-63. https://doi.org/10.1016/j.vehcom.2018.09.004
  • Hou, H., Di, Z., Zhang, M., & Yuan, D. (2022, May). An Intrusion Detection Method for Cyber Monintoring Using Attention based Hierarchical LSTM. 2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). https://doi.org/10.1109/bigdatasecurityhpscids54978.2022.00032
  • Information Technologies and Communications Authori. (2013, January). National Cyber Security Strategy and 2013-2014 Action Plan. Retrieved December 12, 2023, from https://www.btk.gov.tr/uploads/pages/2-1-strateji-eylem-plani-2013-2014-5a3412cf8f45a.pdf
  • Kazanç, M. (2022). Resim formatindaki dijital dokümanlarin bilgisayar görüsü ve makine öğrenmesi yöntemleri kullanilarak LaTex formatina çevrilmesi [MSc Thesis, İstanbul University-Cerrahpaşa].
  • Li, K., Zhou, H., Tu, Z., Wang, W., Zhang, H. (2020). Distributed network intrusion detection system in satellite-terrestrial integrated networks using federated learning. IEEE Access, vol. 8, pp. 214852-214865. https://doi.org/10.1109/ACCESS.2020.3041641
  • Liu, Q., & Wu, Y. (2012). Supervised Learning. Encyclopedia of the Sciences of Learning, 3243–3245. https://doi.org/10.1007/978-1-4419-1428-6_451
  • Lu, K. D., & Wu, Z. G. (2022, July 9). An Ensemble Learning-Based Cyber-Attacks Detection Method of Cyber-Physical Power Systems. 2022 International Conference on Advanced Robotics and Mechatronics (ICARM). https://doi.org/10.1109/icarm54641.2022.9959185
  • Marino, D. L., Wickramasinghe, C. S., Singh, V. K., Gentle, J., Rieger, C., & Manic, M. (2021). The Virtualized Cyber-Physical Testbed for Machine Learning Anomaly Detection: A Wind Powered Grid Case Study. IEEE Access, 9, 159475–159494. https://doi.org/10.1109/access.2021.3127169
  • Mitarai, K., Negoro, M., Kitagawa, M., & Fujii, K. (2018, September 10). Quantum circuit learning. Physical Review A, 98(3). https://doi.org/10.1103/physreva.98.032309
  • Ozogur, G., Erturk, M. A., Gurkas Aydin, Z., & Aydin, M. A. (2023, January 22). Android Malware Detection in Bytecode Level Using TF-IDF and XGBoost. The Computer Journal, 66(9), 2317–2328. https://doi.org/10.1093/comjnl/bxac198
  • Perrone, P., Flammini, F., & Setola, R. (2021, July 26). Machine Learning for Threat Recognition in Critical Cyber-Physical Systems. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). https://doi.org/10.1109/csr51186.2021.9527979
  • Qu, X., Yang, L., Guo, K., Ma, L., Sun, M., Ke, M., & Li, M. (2019, October 2). A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection. Mobile Networks and Applications, 26(2), 808–829. https://doi.org/10.1007/s11036-019-01353-0
  • Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018). Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy. https://doi.org/10.5220/0006639801080116
  • Shi, L., Krishnan, S., Wen, S., & Xiang, Y. (2022). Supporting Cyber-Attacks and System Anomaly Detection Research with an Industry 4.0 Dataset. Network and System Security, 335–353. https://doi.org/10.1007/978-3-031-23020-2_19
  • Singapore University of Technology and Design (2022, June). Secure Water Treatment (SWaT). Retrieved December 20, 2023, from https://itrust.sutd.edu.sg/itrust-labs-home/itrust-labs_swat/
  • Suhail, S., Iqbal, M., Hussain, R., & Jurdak, R. (2023, October). ENIGMA: An explainable digital twin security solution for cyber–physical systems. Computers in Industry, 151, 103961. https://doi.org/10.1016/j.compind.2023.103961
  • Tavallaee, M., Bagheri, E., Lu, W. & Ghorbani, A. A., A detailed analysis of the KDD CUP 99 data set, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada, 2009, pp. 1-6, https://doi.org/10.1109/CISDA.2009.5356528
  • Teixeira, M., Salman, T., Zolanvari, M., Jain, R., Meskin, N., & Samaka, M. (2018, August 9). SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach. Future Internet, 10(8), 76. https://doi.org/10.3390/fi10080076
  • Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192. https://doi.org/10.1016/j.aci.2018.08.003
  • Turnipseed, I. (2015). A new scada dataset for intrusion detection research [Master of Science Thesis, Mississippi State University]. https://scholarsjunction.msstate.edu/td/209/
  • Verma, M.E., Iannacone, M.D., Bridges, R.A., Hollifield, S.C., Kay, B., & Combs, F.L. (2020). ROAD: The Real ORNL Automotive Dynamometer Controller Area Network Intrusion Detection Dataset (with a comprehensive CAN IDS dataset survey & guide). ArXiv, abs/2012.14600
  • Wang, Z., Li, Z., He, D., & Chan, S. (2022, November). A lightweight approach for network intrusion detection in industrial cyber-physical systems based on knowledge distillation and deep metric learning. Expert Systems with Applications, 206, 117671. https://doi.org/10.1016/j.eswa.2022.117671
  • Wazid, M., Das, A. K., Chamola, V., & Park, Y. (2022, September). Uniting cyber security and machine learning: Advantages, challenges and future research. ICT Express, 8(3), 313–321. https://doi.org/10.1016/j.icte.2022.04.007
  • Zhou, X., Pang, J., Yue, F., Liu, F., Guo, J., Liu, W., Song, Z., Shu, G., Xia, B., & Shan, Z. (2022, May 16). A new method of software vulnerability detection based on a quantum neural network. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-11227-3

Using Artificial Intelligence in the Security of Cyber Physical Systems

Year 2023, Volume: 11 Issue: 2, 193 - 206, 31.12.2023
https://doi.org/10.17093/alphanumeric.1404181

Abstract

The prominence of cyber security continues to increase on a daily basis. Following the cyber attacks in recent years, governments have implemented a range of regulations. The advancement of technology and digitalization has led to the creation of new vulnerabilities that cyber attackers can exploit. The digitalization of facilities such as energy distribution networks and water infrastructures has enhanced their efficiency, thereby benefiting states and society. The modern sensors, controllers, and networks of these new generation facilities have made them susceptible to cyber attackers. While all forms of cyber attacks are detrimental, targeting critical cyber-physical systems presents a heightened level of peril. These assaults have the potential to disrupt the social structure and pose a threat to human lives. Various techniques are employed to guarantee the security of these facilities, which is of utmost importance. This study examined the applications of machine learning and deep learning methods, which are sub-branches of artificial intelligence that have recently undergone a period of significant advancement. Intrusion detection systems are being created for the networks that facilitate communication among the hardware components of the cyber-physical system. Another potential application area involves the development of models capable of detecting anomalies and attacks in the data generated by sensors and controllers. Cyber physical systems exhibit a wide range of diversity. Due to the wide range of variations, it is necessary to utilize specific datasets for training the model. Generating a dataset through attacks on a functional cyber-physical system is unattainable. The study also analyzed the solutions to this problem. Based on the analyzed studies, it has been observed that the utilization of artificial intelligence enhances the security of cyber physical systems.

References

  • Ahmed, C. M., Palleti, V. R., & Mathur, A. P. (2017, April 21). WADI: a water distribution testbed for research in the design of secure cyber physical systems. Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks. https://doi.org/10.1145/3055366.3055375
  • Alguliyev, R., Sukhostat, L., & Mammadov, A. (2022, October 12). Anomaly Detection in Cyber-Physical Systems based on BiGRU-VAE. 2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT). https://doi.org/10.1109/aict55583.2022.10013581
  • Alrowais, F., Mohamed, H. G., Al-Wesabi, F. N., Al Duhayyim, M., Hilal, A. M., & Motwakel, A. (2023, May). Cyber attack detection in healthcare data using cyber-physical system with optimized algorithm. Computers and Electrical Engineering, 108, 108636. https://doi.org/10.1016/j.compeleceng.2023.108636
  • Ashraf, I., Narra, M., Umer, M., Majeed, R., Sadiq, S., Javaid, F., & Rasool, N. (2022, February 21). A Deep Learning-Based Smart Framework for Cyber-Physical and Satellite System Security Threats Detection. Electronics, 11(4), 667. https://doi.org/10.3390/electronics11040667
  • Chen, T. M., & Abu-Nimeh, S. (2011, April). Lessons from Stuxnet. Computer, 44(4), 91–93. https://doi.org/10.1109/mc.2011.115
  • D., L., Nagpal, N., Chandrasekaran, S., & D., J. H. (2023, March). A quantum-based approach for offensive security against cyber attacks in electrical infrastructure. Applied Soft Computing, 136, 110071. https://doi.org/10.1016/j.asoc.2023.110071
  • Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J. J., Sandhu, S., Guppy, K. H., Lee, S., & Froelicher, V. (1989, August). International application of a new probability algorithm for the diagnosis of coronary artery disease. The American Journal of Cardiology, 64(5), 304–310. https://doi.org/10.1016/0002-9149(89)90524-9
  • EU monitor. (2008, December). Directive 2008/114 - Identification and designation of European critical infrastructures and the assessment of the need to improve their protection. Retrieved December 12, 2023, from https://www.eumonitor.eu/9353000/1/j9vvik7m1c3gyxp/vitgbgipfoqy
  • Faramondi, L., Flammini, F., Guarino, S., & Setola, R. (2021). A Hardware-in-the-Loop Water Distribution Testbed Dataset for Cyber-Physical Security Testing. IEEE Access, 9, 122385–122396. https://doi.org/10.1109/access.2021.3109465
  • Ferrag, M. A., Friha, O., Hamouda, D., Maglaras, L., & Janicke, H. (2022). Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning. IEEE Access, 10, 40281–40306. https://doi.org/10.1109/access.2022.3165809
  • Frazão, I., Abreu, P. H., Cruz, T., Araújo, H., & Simões, P. (2019). Denial of service attacks: Detecting the frailties of machine learning algorithms in the classification process. In Lecture Notes in Computer Science. Critical Information Infrastructures Security (pp. 230–235). https://doi.org/10.1007/978-3-030-05849-4_19
  • Funchal, G., Pedrosa, T., Vallim, M., & Leitao, P. (2020, July 20). Security for a Multi-Agent Cyber-Physical Conveyor System using Machine Learning. 2020 IEEE 18th International Conference on Industrial Informatics (INDIN). https://doi.org/10.1109/indin45582.2020.9478915
  • Geiger, M., Bauer, J., Masuch, M., & Franke, J. (2020, September). An Analysis of Black Energy 3, Crashoverride, and Trisis, Three Malware Approaches Targeting Operational Technology Systems. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). https://doi.org/10.1109/etfa46521.2020.9212128
  • Guarino, S., Faramondi, L., Setola, R. & Flammini, F. (2021). A hardware-in-the-loop water distribution testbed (WDT) dataset for cyber-physical security testing. IEEE Dataport. https://dx.doi.org/10.21227/rbvf-2h90
  • Habib, A. A., Hasan, M. K., Alkhayyat, A., Islam, S., Sharma, R., & Alkwai, L. M. (2023, April). False data injection attack in smart grid cyber physical system: Issues, challenges, and future direction. Computers and Electrical Engineering, 107, 108638. https://doi.org/10.1016/j.compeleceng.2023.108638
  • Han, M.L., Kwak, B.I., & Kim, H.K. (2018). Anomaly intrusion detection method for vehicular networks based on survival analysis. Vehicular Communications, Volume 14, 2018, Pages 52-63. https://doi.org/10.1016/j.vehcom.2018.09.004
  • Hou, H., Di, Z., Zhang, M., & Yuan, D. (2022, May). An Intrusion Detection Method for Cyber Monintoring Using Attention based Hierarchical LSTM. 2022 IEEE 8th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). https://doi.org/10.1109/bigdatasecurityhpscids54978.2022.00032
  • Information Technologies and Communications Authori. (2013, January). National Cyber Security Strategy and 2013-2014 Action Plan. Retrieved December 12, 2023, from https://www.btk.gov.tr/uploads/pages/2-1-strateji-eylem-plani-2013-2014-5a3412cf8f45a.pdf
  • Kazanç, M. (2022). Resim formatindaki dijital dokümanlarin bilgisayar görüsü ve makine öğrenmesi yöntemleri kullanilarak LaTex formatina çevrilmesi [MSc Thesis, İstanbul University-Cerrahpaşa].
  • Li, K., Zhou, H., Tu, Z., Wang, W., Zhang, H. (2020). Distributed network intrusion detection system in satellite-terrestrial integrated networks using federated learning. IEEE Access, vol. 8, pp. 214852-214865. https://doi.org/10.1109/ACCESS.2020.3041641
  • Liu, Q., & Wu, Y. (2012). Supervised Learning. Encyclopedia of the Sciences of Learning, 3243–3245. https://doi.org/10.1007/978-1-4419-1428-6_451
  • Lu, K. D., & Wu, Z. G. (2022, July 9). An Ensemble Learning-Based Cyber-Attacks Detection Method of Cyber-Physical Power Systems. 2022 International Conference on Advanced Robotics and Mechatronics (ICARM). https://doi.org/10.1109/icarm54641.2022.9959185
  • Marino, D. L., Wickramasinghe, C. S., Singh, V. K., Gentle, J., Rieger, C., & Manic, M. (2021). The Virtualized Cyber-Physical Testbed for Machine Learning Anomaly Detection: A Wind Powered Grid Case Study. IEEE Access, 9, 159475–159494. https://doi.org/10.1109/access.2021.3127169
  • Mitarai, K., Negoro, M., Kitagawa, M., & Fujii, K. (2018, September 10). Quantum circuit learning. Physical Review A, 98(3). https://doi.org/10.1103/physreva.98.032309
  • Ozogur, G., Erturk, M. A., Gurkas Aydin, Z., & Aydin, M. A. (2023, January 22). Android Malware Detection in Bytecode Level Using TF-IDF and XGBoost. The Computer Journal, 66(9), 2317–2328. https://doi.org/10.1093/comjnl/bxac198
  • Perrone, P., Flammini, F., & Setola, R. (2021, July 26). Machine Learning for Threat Recognition in Critical Cyber-Physical Systems. 2021 IEEE International Conference on Cyber Security and Resilience (CSR). https://doi.org/10.1109/csr51186.2021.9527979
  • Qu, X., Yang, L., Guo, K., Ma, L., Sun, M., Ke, M., & Li, M. (2019, October 2). A Survey on the Development of Self-Organizing Maps for Unsupervised Intrusion Detection. Mobile Networks and Applications, 26(2), 808–829. https://doi.org/10.1007/s11036-019-01353-0
  • Sharafaldin, I., Habibi Lashkari, A., & Ghorbani, A. A. (2018). Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. Proceedings of the 4th International Conference on Information Systems Security and Privacy. https://doi.org/10.5220/0006639801080116
  • Shi, L., Krishnan, S., Wen, S., & Xiang, Y. (2022). Supporting Cyber-Attacks and System Anomaly Detection Research with an Industry 4.0 Dataset. Network and System Security, 335–353. https://doi.org/10.1007/978-3-031-23020-2_19
  • Singapore University of Technology and Design (2022, June). Secure Water Treatment (SWaT). Retrieved December 20, 2023, from https://itrust.sutd.edu.sg/itrust-labs-home/itrust-labs_swat/
  • Suhail, S., Iqbal, M., Hussain, R., & Jurdak, R. (2023, October). ENIGMA: An explainable digital twin security solution for cyber–physical systems. Computers in Industry, 151, 103961. https://doi.org/10.1016/j.compind.2023.103961
  • Tavallaee, M., Bagheri, E., Lu, W. & Ghorbani, A. A., A detailed analysis of the KDD CUP 99 data set, 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada, 2009, pp. 1-6, https://doi.org/10.1109/CISDA.2009.5356528
  • Teixeira, M., Salman, T., Zolanvari, M., Jain, R., Meskin, N., & Samaka, M. (2018, August 9). SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach. Future Internet, 10(8), 76. https://doi.org/10.3390/fi10080076
  • Tharwat, A. (2020). Classification assessment methods. Applied Computing and Informatics, 17(1), 168–192. https://doi.org/10.1016/j.aci.2018.08.003
  • Turnipseed, I. (2015). A new scada dataset for intrusion detection research [Master of Science Thesis, Mississippi State University]. https://scholarsjunction.msstate.edu/td/209/
  • Verma, M.E., Iannacone, M.D., Bridges, R.A., Hollifield, S.C., Kay, B., & Combs, F.L. (2020). ROAD: The Real ORNL Automotive Dynamometer Controller Area Network Intrusion Detection Dataset (with a comprehensive CAN IDS dataset survey & guide). ArXiv, abs/2012.14600
  • Wang, Z., Li, Z., He, D., & Chan, S. (2022, November). A lightweight approach for network intrusion detection in industrial cyber-physical systems based on knowledge distillation and deep metric learning. Expert Systems with Applications, 206, 117671. https://doi.org/10.1016/j.eswa.2022.117671
  • Wazid, M., Das, A. K., Chamola, V., & Park, Y. (2022, September). Uniting cyber security and machine learning: Advantages, challenges and future research. ICT Express, 8(3), 313–321. https://doi.org/10.1016/j.icte.2022.04.007
  • Zhou, X., Pang, J., Yue, F., Liu, F., Guo, J., Liu, W., Song, Z., Shu, G., Xia, B., & Shan, Z. (2022, May 16). A new method of software vulnerability detection based on a quantum neural network. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-11227-3
There are 39 citations in total.

Details

Primary Language English
Subjects Information Systems (Other)
Journal Section Articles
Authors

Zeynep Gürkaş Aydın 0000-0002-4125-0589

Murat Kazanç 0000-0002-8405-0181

Publication Date December 31, 2023
Submission Date December 13, 2023
Acceptance Date December 31, 2023
Published in Issue Year 2023 Volume: 11 Issue: 2

Cite

APA Gürkaş Aydın, Z., & Kazanç, M. (2023). Using Artificial Intelligence in the Security of Cyber Physical Systems. Alphanumeric Journal, 11(2), 193-206. https://doi.org/10.17093/alphanumeric.1404181

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