Machine Learning Approaches for Predictive Analysis of Cybersecurity Threats in Telehealth Systems: A Systematic Review

Background: In the dynamic technological environment, telehealth platforms experience growing vulnerability risks that originate from increased connectivity and adoption. Intelligent threat detection methods, such as machine learning, promise rapid responses to manage complex data and device assets supporting life-critical care services prone to cybersecurity challenges.
Methods: Six databases, IEEE Xplore, Google Scholar, PubMed, Scopus, Embase, Web of Science, and CINAHL, were searched to retrieve studies for performance metrics comparisons. A systematic literature review identified 4220 studies, of which 18 were selected for machine learning cybersecurity approaches applied in telehealth environments. The methodology was strengthened through screening, risk-of-bias assessments, the CASP Qualitative Checklist (2019), and the Keele et al. (2007) accumulated list, with adherence to PRISMA guidelines.
Results: Among the reviewed studies, 38.9% focused on supervised learning techniques, unsupervised learning methods at 21.74%, deep learning, at 22% and reinforcement learning at 13.04%.
Conclusions: This study's findings supported upgrading to machine learning security implementations, immediate investments, and indispensable improvements for telehealth ecosystems to safeguard against increasing data breaches and service-disruption threats that endanger patient safety and care delivery services.

Machine Learning, telehealth, predictive analytics, patient data privacy, and Artificial Intelligence.

1. Ahad, A., Jiangbina, Z., Tahir, M., Shayea, I., Sheik, M. A., & Rasheed, F. (2024). 6G and Intelligent Healthcare: Taxonomy, Technologies, Open Issues and Future Research Directions. Internet of Things, 101068. [Google Scholar] [Crossref]

2. Akhtar, Z., & Buriro, A. (2021). Multitrait Selfie: Low-Cost Multi-modal Smartphone User Authentication. Biometric Identification Technologies Based on Modern Data Mining Methods, 159-175. [Google Scholar] [Crossref]

3. Aldahiri, A., Alrashed, B., & Hussain, W. (2021). Trends in using IoT with machine learning in health prediction systems. Forecasting, 3(1), 181-206. [Google Scholar] [Crossref]

4. Alder, S. (2020). Universal Health Services confirms all US hospitals are affected by a ransomware attack. HIPAA Journal. https://www.hipaajournal.com/universal-health-services-ransomware-attackcost/ [Google Scholar] [Crossref]

5. Alder, S. (2021). Scripps Health ransomware attack cost estimate revised to $112.7 million. HIPAA Journal. https://www.hipaajournal.com/scripps-health-ransomware-attack-cost-113-million/ [Google Scholar] [Crossref]

6. Alder, S. (2023). CommonSpirit Health increases ransomware attack cost estimate to $160 million. HIPAA Journal. https://www.hipaajournal.com/commonspirit-health-increases-ransomwareattack-cost-estimate-to-160-million/ [Google Scholar] [Crossref]

7. Alder, S. (2025). Healthcare data breach statistics. HIPAA Journal. [Google Scholar] [Crossref]

8. https://www.hipaajournal.com/healthcare-data-breach-statistics/ [Google Scholar] [Crossref]

9. Alipio, M., & Bures, M. (2023). Current testing and performance evaluation methodologies of LoRa and LoRaWAN in IoT applications: Classification, issues, and future directives. Internet of Things, 101053. [Google Scholar] [Crossref]

10. Almestad, E. (2023). Exploring Explainable AI Adoption in Medical Diagnosis and the Empowering Potential of Collaboration at NTNU. [Google Scholar] [Crossref]

11. Al-Qarni, E. A. (2023). Cybersecurity in healthcare: A review of recent attacks and mitigation strategies. International Journal of Advanced Computer Science and Applications, 14(5), Article 0140513. https://doi.org/10.14569/IJACSA.2023.0140513 [Google Scholar] [Crossref]

12. Alqarni, M. A., Chaudhary, S. H., Malik, M. N., Ehatisham-ul-Haq, M., & Azam, M. A. (2020). Identifying smartphone users based on how they interact with their phones. Human-centric Computing and Information Sciences, 10(1), 7. [Google Scholar] [Crossref]

13. Alsellami, B., Deshmukh, P. D., Ahmed, Z. A., Tawfik, M., & Al-madani, A. M. (2021). Overview of Biometric Traits. 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), [Google Scholar] [Crossref]

14. Alshaibi, A., Al-Ani, M., Al-Azzawi, A., Konev, A., & Shelupanov, A. (2022). The comparison of cybersecurity datasets. Data, 7(2), 22. [Google Scholar] [Crossref]

15. Al-Thani, D., Monteiro, S., & Tamil, L. S. (2020). Design for eHealth and telehealth. In Design for Health (pp. 67-86). Elsevier. [Google Scholar] [Crossref]

16. Alwahedi, F., Aldhaheri, A., Ferrag, M. A., Battah, A., & Tihanyi, N. (2024). Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models—Internet of Things and Cyber-Physical Systems. [Google Scholar] [Crossref]

17. Alwidian, J., Elhassan, A., & Ghnemat, R. (2020). Predicting autism spectrum disorder using a machine learning technique. International Journal of Recent Technology and Engineering, 8(5), 4139-4143. [Google Scholar] [Crossref]

18. Alzahrani, A. O., & Alenazi, M. J. (2021). Designing a network intrusion detection system based on machine learning for software-defined networks. Future Internet, 13(5), 111. [Google Scholar] [Crossref]

19. AlZubi, A. A., Al-Maitah, M., & Alarifi, A. (2021). Cyber-attack detection in healthcare using cyber-physical systems and machine learning techniques. Soft Computing, 25(18), 12319-12332. [Google Scholar] [Crossref]

20. Anand, A., Rani, S., Anand, D., Aljahdali, H. M., & Kerr, D. (2021). An efficient CNN-based deep learning model to detect malware attacks (CNN-DMA) in 5G-IoT healthcare applications. Sensors, 21(19), 6346. [Google Scholar] [Crossref]

21. Angelopoulou, E., Papachristou, N., Bougea, A., Stanitsa, E., Kontaxopoulou, D., Fragkiadaki, S., Pavlou, D., Koros, C., Değirmenci, Y., & Papatriantafyllou, J. (2022). How can telemedicine improve the quality of care for patients with Alzheimer’s disease and related dementias? A narrative review. Medicina, 58(12), 1705. [Google Scholar] [Crossref]

22. Balducci, F., De Carolis, B., Impedovo, D., & Pirlo, G. (2019). Touch dynamics for affective state recognition: your smartphone knows how you feel as soon as you unlock it. SAT@ SMC, [Google Scholar] [Crossref]

23. Baliga, R. R., & Itchhaporia, D. (2022). Digital Health, An Issue of Heart Failure Clinics, E-Book (Vol. 18). Elsevier Health Sciences. [Google Scholar] [Crossref]

24. Batista, E., Moncusi, M. A., López-Aguilar, P., Martínez-Ballesté, A., & Solanas, A. (2021). Sensors for context-aware smart healthcare: A security perspective. Sensors, 21(20), 6886. [Google Scholar] [Crossref]

25. Bellucci, N. (2022). Disruptive Innovation and Technological Influences on Healthcare. Journal of Radiology Nursing, 41(2), 98-101. [Google Scholar] [Crossref]

26. Bharadwaj, H. K., Agarwal, A., Chamola, V., Lakkaniga, N. R., Hassija, V., Guizani, M., & Sikdar, B. (2021). A review of the role of machine learning in enabling IoT-based healthcare applications. IEEE Access, 9, 38859-38890. [Google Scholar] [Crossref]

27. Bhattacharjee, A., Borgohain, S. K., Soni, B., Verma, G., & Gao, X.-Z. (2020). Machine Learning, Image Processing, Network Security and Data Sciences: Second International Conference, MIND 2020, Silchar, India, July 30-31, 2020, Proceedings, Part II (Vol. 1241). Springer Nature. [Google Scholar] [Crossref]

28. Bhuva, D. R., & Kumar, S. (2023). A novel continuous authentication method using biometrics for IOT devices. Internet of Things, 24, 100927. [Google Scholar] [Crossref]

29. Bhuyan, S. S., Kabir, U. Y., Escareno, J. M., Ector, K., Palakodeti, S., Wyant, D., Kumar, S., Levy, M., Kedia, S., Dasgupta, D., & Dobalian, A. (2020). Transforming healthcare cybersecurity from reactive to proactive: Current status and future recommendations. Journal of Medical Systems, 44(5), Article 98. https://doi.org/10.1007/s10916-019-1507-y [Google Scholar] [Crossref]

30. Binbeshr, F., Kiah, M. M., Por, L. Y., & Zaidan, A. A. (2021). A systematic review of PIN-entry methods resistant to shoulder-surfing attacks. Computers & Security, 101, 102116. [Google Scholar] [Crossref]

31. Bokolo, A. J. (2021). Application of telemedicine and eHealth technology for clinical services in response to the COVID‑19 pandemic. Health and technology, 11(2), 359-366. [Google Scholar] [Crossref]

32. Bouchama, F., & Kamal, M. (2021). Enhancing Cyber Threat Detection through Machine Learning-Based Behavioral Modeling of Network Traffic Patterns. International Journal of Business Intelligence and Big Data Analytics, 4(9), 1-9. [Google Scholar] [Crossref]

33. Brito, L. C., Susto, G. A., Brito, J. N., & Duarte, M. A. V. (2021). Fault detection of bearing: An unsupervised machine learning approach exploiting feature extraction and dimensionality reduction. Informatics, [Google Scholar] [Crossref]

34. Buriro, A., Crispo, B., & Conti, M. (2019). AnswerAuth: A bimodal behavioral biometric-based user authentication scheme for smartphones. Journal of information security and applications, 44, 89-103. [Google Scholar] [Crossref]

35. Cascella, M., Coluccia, S., Monaco, F., Schiavo, D., Nocerino, D., Grizzuti, M., Romano, M. C., & Cuomo, A. (2022). Different machine learning approaches for implementing telehealth-based cancer pain management strategies—Journal of Clinical Medicine, 11(18), 5484. [Google Scholar] [Crossref]

36. Chan, K. Y., Abu-Salih, B., Qaddoura, R., Ala’M, A.-Z., Palade, V., Pham, D.-S., Del Ser, J., & Muhammad, K. (2023). Deep neural networks in the cloud: Review, applications, challenges, and research directions: neurocomputing, 545, 126327. [Google Scholar] [Crossref]

37. Cola, G., Vecchio, A., & Avvenuti, M. (2021). Continuous authentication through gait analysis on a wrist-worn device: Pervasive and Mobile Computing, 78, 101483. [Google Scholar] [Crossref]

38. Cui, Z., Zhao, Y., Li, C., Zuo, Q., & Zhang, H. (2019). An adaptive authentication based on reinforcement learning. 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), [Google Scholar] [Crossref]

39. Drăgulinescu, A. M. C., Manea, A. F., Fratu, O., & Drăgulinescu, A. (2020). LoRa-based medical IoT system architecture and testbed. Wireless Personal Communications, 1-23. [Google Scholar] [Crossref]

40. Eke, C. I., Norman, A. A., & Mulenga, M. (2023). Machine learning approach for detecting and combating bring-your-own-device (BYOD) security threats and attacks: a systematic mapping review. Artificial Intelligence Review, 56(8), 8815-8858. [Google Scholar] [Crossref]

41. Elahe, M. F., Jin, M., & Zeng, P. (2021). Review of load data analytics using deep learning in smart grids: Open load datasets, methodologies, and application challenges. International Journal of Energy Research, 45(10), 14274-14305. [Google Scholar] [Crossref]

42. Ellavarason, E., Guest, R., & Deravi, F. (2020). Evaluation of the stability of swipe gesture authentication across usage scenarios of a mobile device. EURASIP Journal on Information Security, 2020, 1-14. [Google Scholar] [Crossref]

43. Francis, J. G., & Francis, L. P. (2021). Sustaining surveillance: the importance of information for public health (Vol. 6). Springer. [Google Scholar] [Crossref]

44. Gadal, S., Mokhtar, R., Abdelhaq, M., Alsaqour, R., Ali, E. S., & Saeed, R. (2022). Machine learning-based anomaly detection using K-means array and sequential minimal optimization. Electronics, 11(14), 2158. [Google Scholar] [Crossref]

45. Garfan, S., Alamoodi, A. H., Zaidan, B., Al-Zobbi, M., Hamid, R. A., Alwan, J. K., Ahmaro, I. Y., Khalid, E. T., Jumaah, F., & Albahri, O. S. (2021). Telehealth utilization during the COVID-19 pandemic: A systematic review. Computers in biology and medicine, 138, 104878. [Google Scholar] [Crossref]

46. Ghosh, S., & Sharma, V. (2024). Tracking of Disease—A Review of the State of the Art of Technology for Next Generation Healthcare. Deep Learning in Internet of Things for Next Generation Healthcare, 242-268. [Google Scholar] [Crossref]

47. Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2022). Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems, 2, 12-30. [Google Scholar] [Crossref]

48. Hameed, S. S., Hassan, W. H., Latiff, L. A., & Ghabban, F. (2021). A systematic review of security and privacy issues in the internet of medical things: the role of machine learning approaches: PeerJ Computer Science, 7, e414. [Google Scholar] [Crossref]

49. Hazratifard, M., Agrawal, V., Gebali, F., Elmiligi, H., & Mamun, M. (2023). Review of using machine learning in secure IoT healthcare. In Accelerating Strategic Changes for Digital Transformation in the Healthcare Industry (pp. 237-269). Elsevier. [Google Scholar] [Crossref]

50. Hazratifard, M., Gebali, F., & Mamun, M. (2022). Using machine learning for dynamic authentication in telehealth: A tutorial. Sensors, 22(19), 7655. [Google Scholar] [Crossref]

51. Healthcare Information and Management Systems Society (HIMSS). (2024). 2024 HIMSS healthcare cybersecurity survey. HIMSS. https://www.himss.org/resources/himss-healthcare-cybersecurity-survey/ [Google Scholar] [Crossref]

52. Hilty, D., Peled, A., & Luxton, D. D. (2023). Predictive Modeling, Artificial Intelligence, and Machine Learning in Psychiatric Assessment and Treatment. In Tasman’s Psychiatry (pp. 1-22). Springer. [Google Scholar] [Crossref]

53. Huang, Y., Huang, L., & Zhu, Q. (2022). Reinforcement learning for feedback-enabled cyber resilience. Annual reviews in control, 53, 273-295. [Google Scholar] [Crossref]

54. Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2021). Machine learning towards intelligent systems: applications, challenges, and opportunities. Artificial Intelligence Review, 54(5), 3299-3348. [Google Scholar] [Crossref]

55. Ismail, A., Abdlerazek, S., & El-Henawy, I. M. (2020). Development of a smart healthcare system based on speech recognition using a support vector machine and dynamic time warping. Sustainability, 12(6), 2403. [Google Scholar] [Crossref]

56. Joymangul, J. S., Sekhari, A., Moalla, N., & Grasset, O. (2019). Data-oriented approach to improve adherence to CPAP therapy during the initiation phase. 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), [Google Scholar] [Crossref]

57. Junaid, S. B., Imam, A. A., Balogun, A. O., De Silva, L. C., Surakat, Y. A., Kumar, G., Abdulkarim, M., Shuaibu, A. N., Garba, A., & Sahalu, Y. (2022). Recent advancements in emerging technologies for healthcare management systems: A survey. Healthcare, [Google Scholar] [Crossref]

58. Kaiafas, G., Hammerschmidt, C., Lagraa, S., & State, R. (2019). Auto Semi-supervised Outlier Detection for Malicious Authentication Events. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, [Google Scholar] [Crossref]

59. Li, N., Xu, M., Li, Q., Liu, J., Bao, S., Li, Y., Li, J., & Zheng, H. (2023). A review of security issues and solutions for precision health in Internet-of-Medical-Things systems. Security and Safety, 2, 2022010. [Google Scholar] [Crossref]

60. Li, W., Tan, J., Meng, W., Wang, Y., & Li, J. (2019). SwipeVLock: a supervised unlocking mechanism based on swipe behavior on smartphones. Machine Learning for Cyber Security: Second International Conference, ML4CS 2019, Xi’an, China, September 19-21, 2019, Proceedings 2, [Google Scholar] [Crossref]

61. Li, Y., Zhang, Z., Dai, C., Dong, Q., & Badrigilan, S. (2020). Accuracy of deep learning for automated detection of pneumonia using chest X-Ray images: A systematic review and meta-analysis—Computers in Biology and Medicine, 123, 103898. [Google Scholar] [Crossref]

62. Life, B. U.-N. (2023). Qualitative Study on the Telehealth Experience of Mental Health Clinicians Who Provided Outpatient Services During the COVID-19 Pandemic, Capella University. [Google Scholar] [Crossref]

63. M. Bublitz, F., Oetomo, A., S. Sahu, K., Kuang, A., X. Fadrique, L., E. Velmovitsky, P., M. Nobrega, R., & P. Morita, P. (2019). Disruptive technologies for environment and health research: an overview of artificial intelligence, blockchain, and internet of things. International journal of environmental research and public health, 16(20), 3847. [Google Scholar] [Crossref]

64. Miloslavskaya, N. (2020). Stream data analytics for predicting network attacks. Procedia Computer Science, 169, 57-62. [Google Scholar] [Crossref]

65. Motwani, A., Shukla, P. K., & Pawar, M. (2022). Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review. Artificial Intelligence in Medicine, 134, 102431. [Google Scholar] [Crossref]

66. Nandy, G. (2022). Telehealth Security from a User’s Perspective: Moving beyond COVID-19 and into a New Normal, University of Nebraska at Omaha. [Google Scholar] [Crossref]

67. Newaz, A. I., Sikder, A. K., Rahman, M. A., & Uluagac, A. S. (2021). A survey on security and privacy issues in modern healthcare systems: Attacks and defenses. ACM Transactions on Computing for Healthcare, 2(3), 1-44. [Google Scholar] [Crossref]

68. Nifakos, S., Chandramouli, K., Nikolaou, C. K., Papachristou, P., Koch, S., Panaousis, E., & Bonacina, S. (2021). Influence of human factors on cyber security within healthcare organizations: A systematic review. Sensors, 21(15), 5119. https://doi.org/10.3390/s21155119 [Google Scholar] [Crossref]

69. Osama, M., Ateya, A. A., Sayed, M. S., Hammad, M., Pławiak, P., Abd El-Latif, A. A., & Elsayed, R. A. (2023). Internet of medical things and healthcare 4.0: Trends, requirements, challenges, and research directions. Sensors, 23(17), 7435. [Google Scholar] [Crossref]

70. Page, M.J., et al. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. Systematic Reviews, 10, Article No. 89. [Google Scholar] [Crossref]

71. https://doi.org/10.1186/s13643-021-01626-4 [Google Scholar] [Crossref]

72. Parashar, A., Parashar, A., Shabaz, M., Gupta, D., Sahu, A. K., & Khan, M. A. (2024). Advancements in artificial intelligence for biometrics: a deep dive into model-based gait recognition techniques. Engineering Applications of Artificial Intelligence, 130, 107712. [Google Scholar] [Crossref]

73. Paul, M., Maglaras, L., Ferrag, M. A., & Almomani, I. (2023). Digitization of the healthcare sector: A study on privacy and security concerns. ICT Express, 9(4), 571–588. [Google Scholar] [Crossref]

74. https://doi.org/10.1016/j.icte.2023.02.007 [Google Scholar] [Crossref]

75. Rabbani, M., Wang, Y., Khoshkangini, R., Jelodar, H., Zhao, R., Bagheri Baba Ahmadi, S., & Ayobi, S. (2021). A review of machine learning approaches for network malicious behavior detection in emerging technologies. Entropy, 23(5), 529. [Google Scholar] [Crossref]

76. Rasool, R. U., Ahmad, H. F., Rafique, W., Qayyum, A., & Qadir, J. (2022). Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML. Journal of Network and Computer Applications, 201, 103332. [Google Scholar] [Crossref]

77. Ray, S., Mishra, K. N., & Dutta, S. (2022). Detection and prevention of DDoS attacks on M-healthcare sensitive data: A novel approach. International Journal of Information Technology, 14(3), 1333– 1341. https://doi.org/10.1007/s41870-022-00869-1 [Google Scholar] [Crossref]

78. Rose, R. V., Kumar, A., & Kass, J. S. (2023). Protecting privacy: Health Insurance Portability and Accountability Act of 1996, Twenty-First Century Cures Act, and social media. Neurologic Clinics, 41(3), 513-522. [Google Scholar] [Crossref]

79. Schünke, L. C., Mello, B., da Costa, C. A., Antunes, R. S., Rigo, S. J., de Oliveira Ramos, G., da Rosa Righi, R., Scherer, J. N., & Donida, B. (2022). A rapid review of machine learning approaches for telemedicine in the scope of COVID-19. Artificial Intelligence in Medicine, 129, 102312. [Google Scholar] [Crossref]

80. Senbekov, M., Saliev, T., Bukeyeva, Z., Almabayeva, A., Zhanaliyeva, M., Aitenova, N., Toishibekov, Y., & Fakhradiyev, I. (2020). The recent progress and applications of digital technologies in healthcare: A review. International Journal of Telemedicine and Applications, 2020, 1–18. https://doi.org/10.1155/2020/8830200 [Google Scholar] [Crossref]

81. Sharma, A., Sharma, A., Virmani, R., Kumar, G., Virmani, T., & Chitranshi, N. (2023). Deep learning IoT in medical and healthcare. In Deep Learning in Personalized Healthcare and Decision Support (pp. 245-261). Elsevier. [Google Scholar] [Crossref]

82. Sharma, S., Singh, G., & Sharma, M. (2021). A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans. Computers in biology and medicine, 134, 104450. [Google Scholar] [Crossref]

83. Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., Chen, S., Liu, D., & Li, J. (2020). Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies, 13(10), 2509. [Google Scholar] [Crossref]

84. Smith-Creasey, M., & Rajarajan, M. (2019). A novel word-independent gesture-typing continuous authentication scheme for mobile devices. Computers & Security, 83, 140-150. [Google Scholar] [Crossref]

85. Tawfik, G. M., Dila, K. A. S., Mohamed, M. Y. F., Tam, D. N. H., Kien, N. D., Ahmed, A. M., & Huy, N. T. (2019). A step-by-step guide for conducting a systematic review and meta-analysis with simulation data. Tropical medicine and health, 47(1), 1-9. [Google Scholar] [Crossref]

86. Tay Wee Teck, J., Butner, J. L., & Baldacchino, A. (2023). Understanding the use of telemedicine across different opioid use disorder treatment models: A scoping review. Journal of Telemedicine and Telecare, 1357633X231195607. [Google Scholar] [Crossref]

87. van Kolfschooten, H., & van Oirschot, J. (2024). The EU Artificial Intelligence Act (2024): Implications for healthcare. Health Policy. https://doi.org/10.1016/j.healthpol.2024.105152 [Google Scholar] [Crossref]

88. Wang, R., & Tao, D. (2019). DTW-KNN implementation for touch-based Authentication System. 2019 5th International Conference on Big Data Computing and Communications (BIGCOM), [Google Scholar] [Crossref]

89. Webster, M. (2021). Do No Harm: Protecting Connected Medical Devices, Healthcare, and Data from Hackers and Adversarial Nation States. John Wiley & Sons. [Google Scholar] [Crossref]

90. Wherton, J., Greenhalgh, T., Hughes, G., & Shaw, S. E. (2022). The role of information infrastructures in scaling up video consultations during COVID-19: mixed methods case study into opportunity, disruption, and exposure. Journal of Medical Internet Research, 24(11), e42431. [Google Scholar] [Crossref]

91. Wickramasinghe, I., & Kalutarage, H. (2021). Naive Baes: applications, variations, and vulnerabilities: a review of literature with code snippets for implementation. Soft Computing, 25(3), 2277-2293. [Google Scholar] [Crossref]

92. Xiao, L., Lu, X., Xu, T., Zhuang, W., & Dai, H. (2021). Reinforcement learning-based physical-layer authentication for controller area networks. IEEE Transactions on Information Forensics and Security, 16, 2535-2547. [Google Scholar] [Crossref]

93. Xu, T., Lu, X., Xiao, L., Tang, Y., & Dai, H. (2019). Voltage-based authentication for controller area networks with reinforcement learning. ICC 2019-2019 IEEE International Conference on Communications (ICC), [Google Scholar] [Crossref]

94. Yi, T., Chen, X., Zhu, Y., Ge, W., & Han, Z. (2023). Review of the application of deep learning in network attack detection. Journal of Network and Computer Applications, 212, 103580. [Google Scholar] [Crossref]

95. Yu, C., Li, H., & Wang, X. (2019). SVD‐based image compression, encryption, and identity authentication algorithm on the cloud. IET Image Processing, 13(12), 2224-2232. [Google Scholar] [Crossref]

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