ARCHIVES
Year 2026 · Volume 6 · Issue 5
Review Article
Security and Privacy for Connected Systems: A Layered Review of Cryptography, Intrusion Detection and Privacy-Preserving Learning in IoT, Cloud and Mobile Networks
Sheela Maharajpet1
R. Kennedy2
M. Karthi Rajan3
1 Assistant Professor, Department of MCA, Acharya Institute of Technology, Bengaluru, Karnataka, India. 2 Assistant Professor, Department of Physics, The American College, Madurai, Tamilnadu, India. 3 Librarian, Sethu Bhaskara Agricultural College & Research Foundation, Karaikudi, Tamilnadu, India.
Published Online: September-October 2026
Pages: 482-487
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260605054
References
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2. S. Pandikumar, K. V. Shaheena, and T. Dinesh, “Upgrading industrial automation with 5G and IoT,” in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications, S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, pp. 57–77, doi: 10.48001/978-81-980647-5-2-5.
3. Y. A. Qadri, A. Nauman, Y. B. Zikria, A. V. Vasilakos, and S. W. Kim, “The future of healthcare Internet of Things: A survey of emerging technologies,” IEEE Commun. Surveys Tuts., vol. 22, no. 2, pp. 1121–1167, 2020, doi: 10.1109/COMST.2020.2973314.
4. C. Jamunadevi, S. Pandikumar, N. N, S. Prasath, and D. S, “Enhancing cybersecurity using machine learning strategies for phishing defense,” in Proc. 9th Int. Conf. Commun. Electron. Syst. (ICCES), Coimbatore, India, 2024, pp. 801–808, doi: 10.1109/ICCES63552.2024.10859402.
5. M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for Internet of Things (IoT) security,” IEEE Commun. Surveys Tuts., vol. 22, no. 3, pp. 1646–1685, 2020, doi: 10.1109/COMST.2020.2988293.
6. K. S. Divya, T. Margaret Mary, S. Chakrabarti, V. S. Prakash, and K. S. Chitra, “Optimizing lightweight cryptographic protocols for secure communication in Internet of Things (IoT) devices,” in Proc. IEEE 5th Int. Conf. Communication, Computing and Industry 6.0 (C2I6), 2024, doi: 10.1109/C2I663243.2024.10894917.
7. C. Dobraunig, M. Eichlseder, F. Mendel, and M. Schläffer, “Ascon v1.2: Lightweight authenticated encryption and hashing,” J. Cryptol., vol. 34, no. 3, p. 33, 2021, doi: 10.1007/s00145-021-09398-9.
8. T. K. S. Rathish Babu, S. Pandikumar, K. Sathishkumar, Z. Kurbonov, A. Thahaseen, and A. M. J. M. Z. Rahman, “Blockchain-enhancedsecure RF link transmission in antenna-constrained industrial IoT systems,” Natl. J. Antennas Propag., vol. 7, no. 1, pp. 270–279, 2025, doi: 10.31838/NJAP/07.01.30.
9. J.-H. Cho, D. P. Sharma, H. Alavizadeh, S. Yoon, N. Ben-Asher, T. J. Moore, D. S. Kim, H. Lim, and F. F. Nelson, “Toward proactive, adaptive defense: A survey on moving target defense,” IEEE Commun. Surveys Tuts., vol. 22, no. 1, pp. 709–745, 2020, doi: 10.1109/COMST.2019.2963791.
10. S. Pandikumar, A. Bostani, A. Srinivasan, G. Menaka, J. Chandhini, R. Sabitha, and K. Sathishkumar, “Fuzzy logic-driven nonlinear optimization for resource allocation in 6G networks,” Results Nonlinear Anal., vol. 8, no. 2, pp. 239–252, 2025, doi: 10.31838/rna/2025.08.02.019.
11. H. Tabrizchi and M. Kuchaki Rafsanjani, “A survey on security challenges in cloud computing: Issues, threats, and solutions,” J. Supercomput., vol. 76, no. 12, pp. 9493–9532, 2020, doi: 10.1007/s11227-020-03213-1.
12. V. S. Prakash, K. K. Thavamani, A. Sivakumar, R. Kumar, T. Margaret Mary, and P. Poongothai, “Experimental evaluation of an elliptical curve cryptographic model based data security over communication channels using cybersecurity logics,” in Proc. Int. Conf. Intelligent Systems for Cybersecurity (ISCS), 2024, doi: 10.1109/ISCS61804.2024.10581344.
13. M. Balamurugan, G. Gunasekaran, T. Margaret Mary, and R. Kalaiarasi, “Optimized cloud security using time-oriented latency approximation-based data encryption with Levy-flight whale optimization,” SN Comput. Sci., vol. 7, no. 5, p. 445, 2026, doi: 10.1007/s42979-026-05052-y.
14. M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
15. S. Pandikumar, M. O. Pallavi, C. Dhanush, and M. Arun, “Quantum safe cryptography – An overview,” in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications, S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, pp. 30–56, doi: 10.48001/978-81-980647-5-2-4.
16. National Institute of Standards and Technology, “Module-lattice-based key-encapsulation mechanism standard,” Federal Information Processing Standards Publication FIPS 203, Gaithersburg, MD, USA, Aug. 2024, doi: 10.6028/NIST.FIPS.203.
17. S. Pandikumar and S. Ambethkar, “A design of authentication and access control model for remote access using GSM technologies,” Int. J. Comput. Appl., vol. 57, no. 14, pp. 12–15, Nov. 2012, doi: 10.5120/9181-3601.
18. H.-N. Dai, Z. Zheng, and Y. Zhang, “Blockchain for Internet of Things: A survey,” IEEE Internet Things J., vol. 6, no. 5, pp. 8076–8094, 2019, doi: 10.1109/JIOT.2019.2920987.
19. M. A. Ferrag, O. Friha, D. Hamouda, L. Maglaras, and H. Janicke, “Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning,” IEEE Access, vol. 10, pp. 40281–40306, 2022, doi: 10.1109/ACCESS.2022.3165809.
20. V. Pandimurugan, J. Umamageswaran, S. Karattil, V. Rajaram, B. Balakiruthiga, and A. Angayarkanni, “Security challenges in the Internet of Medical Things and cyber-physical systems,” in AI-enabled Sustainable Healthcare. Amsterdam, The Netherlands: Elsevier, 2026, pp. 41–65, doi: 10.1016/B978-0-443-33700-0.00004-2.
21. H. Bangali, P. Rodrigues, V. Pandimurugan, S. Rajasoundaran, S. V. N. Santhosh Kumar, M. Selvi, and A. Kannan, “Prediction of middle box-based attacks in Internet of Healthcare Things using ranking subsets and convolutional neural network,” Wireless Netw., vol. 30, no. 3, pp. 1493–1511, 2024, doi: 10.1007/s11276-023-03603-2.
22. M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, “A survey of network-based intrusion detection data sets,” Comput. Secur., vol. 86, pp. 147–167, 2019, doi: 10.1016/j.cose.2019.06.005.
23. M. Alhefdi, Y. Alotaibi, P. Rodrigues, and V. Pandimurugan, “Enhanced threat detection in health care systems with random coupled bootstrapped ensemble classifier,” Health Inform. J., vol. 32, no. 1, Art. no. 14604582261429465, 2026, doi: 10.1177/14604582261429465.
24. D. Gopinath, M. Balamurugan, T. Margaret Mary, A. Kanagaraj, and S. Sharmila, “Dynamic mimic-based moving target defense mechanisms for enhancing security and resilience in mobile ad hoc networks,” in Proc. 4th Int. Conf. Intelligent Computing, Information and Control Systems (ICOIICS), 2025, pp. 487–493, doi: 10.1109/ICOIICS67115.2025.11390105.
25. S. Pandikumar, A. Bostani, D. Sundaranarayana, I. Fakhriddin, D. Alimjon, U. Ilkhom, and A. Juraboyev, “AI-driven nonlinear optimization for knowledge extraction and pattern analysis in IoT-enabled data mining systems,” Results Nonlinear Anal., vol. 8, no. 3, pp. 36–46, 2025, doi: 10.31838/rna/2025.08.03.005.
26. [26] D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for Internet of Things: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 23, no. 3, pp. 1622–1658, 2021, doi: 10.1109/COMST.2021.3075439.
27. P. Kairouz et al., “Advances and open problems in federated learning,” Found. Trends Mach. Learn., vol. 14, no. 1–2, pp. 1–210, 2021, doi: 10.1561/2200000083.
28. S. Pandikumar, V. Pandimurugan, A. Rama Prasath, S. Karattil, S. C. B. Jaganathan, and P. Vigneshwaran, “A novel framework for differential privacy based federated continual learning for dynamic medical data analysis,” SN Comput. Sci., vol. 7, no. 6, p. 664, 2026, doi: 10.1007/s42979-026-05245-5.
29. Y. Wang, Z. Su, N. Zhang, R. Xing, D. Liu, T. H. Luan, and X. Shen, “A survey on metaverse: Fundamentals, security, and privacy,” IEEE Commun. Surveys Tuts., vol. 25, no. 1, pp. 319–352, 2023, doi: 10.1109/COMST.2022.3202047.
30. C. Anusha, P. S. Ambili, P. D. Sowmya, A. Surendran, and T. Margaret Mary, “The role of AI technologies in data privacy and protection in the metaverse era: A detailed review,” in Proc. Int. Conf. Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility (INSPIRE), 2025, pp. 712–717, doi: 10.1109/INSPIRE67328.2025.11300487.
2. S. Pandikumar, K. V. Shaheena, and T. Dinesh, “Upgrading industrial automation with 5G and IoT,” in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications, S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, pp. 57–77, doi: 10.48001/978-81-980647-5-2-5.
3. Y. A. Qadri, A. Nauman, Y. B. Zikria, A. V. Vasilakos, and S. W. Kim, “The future of healthcare Internet of Things: A survey of emerging technologies,” IEEE Commun. Surveys Tuts., vol. 22, no. 2, pp. 1121–1167, 2020, doi: 10.1109/COMST.2020.2973314.
4. C. Jamunadevi, S. Pandikumar, N. N, S. Prasath, and D. S, “Enhancing cybersecurity using machine learning strategies for phishing defense,” in Proc. 9th Int. Conf. Commun. Electron. Syst. (ICCES), Coimbatore, India, 2024, pp. 801–808, doi: 10.1109/ICCES63552.2024.10859402.
5. M. A. Al-Garadi, A. Mohamed, A. K. Al-Ali, X. Du, I. Ali, and M. Guizani, “A survey of machine and deep learning methods for Internet of Things (IoT) security,” IEEE Commun. Surveys Tuts., vol. 22, no. 3, pp. 1646–1685, 2020, doi: 10.1109/COMST.2020.2988293.
6. K. S. Divya, T. Margaret Mary, S. Chakrabarti, V. S. Prakash, and K. S. Chitra, “Optimizing lightweight cryptographic protocols for secure communication in Internet of Things (IoT) devices,” in Proc. IEEE 5th Int. Conf. Communication, Computing and Industry 6.0 (C2I6), 2024, doi: 10.1109/C2I663243.2024.10894917.
7. C. Dobraunig, M. Eichlseder, F. Mendel, and M. Schläffer, “Ascon v1.2: Lightweight authenticated encryption and hashing,” J. Cryptol., vol. 34, no. 3, p. 33, 2021, doi: 10.1007/s00145-021-09398-9.
8. T. K. S. Rathish Babu, S. Pandikumar, K. Sathishkumar, Z. Kurbonov, A. Thahaseen, and A. M. J. M. Z. Rahman, “Blockchain-enhancedsecure RF link transmission in antenna-constrained industrial IoT systems,” Natl. J. Antennas Propag., vol. 7, no. 1, pp. 270–279, 2025, doi: 10.31838/NJAP/07.01.30.
9. J.-H. Cho, D. P. Sharma, H. Alavizadeh, S. Yoon, N. Ben-Asher, T. J. Moore, D. S. Kim, H. Lim, and F. F. Nelson, “Toward proactive, adaptive defense: A survey on moving target defense,” IEEE Commun. Surveys Tuts., vol. 22, no. 1, pp. 709–745, 2020, doi: 10.1109/COMST.2019.2963791.
10. S. Pandikumar, A. Bostani, A. Srinivasan, G. Menaka, J. Chandhini, R. Sabitha, and K. Sathishkumar, “Fuzzy logic-driven nonlinear optimization for resource allocation in 6G networks,” Results Nonlinear Anal., vol. 8, no. 2, pp. 239–252, 2025, doi: 10.31838/rna/2025.08.02.019.
11. H. Tabrizchi and M. Kuchaki Rafsanjani, “A survey on security challenges in cloud computing: Issues, threats, and solutions,” J. Supercomput., vol. 76, no. 12, pp. 9493–9532, 2020, doi: 10.1007/s11227-020-03213-1.
12. V. S. Prakash, K. K. Thavamani, A. Sivakumar, R. Kumar, T. Margaret Mary, and P. Poongothai, “Experimental evaluation of an elliptical curve cryptographic model based data security over communication channels using cybersecurity logics,” in Proc. Int. Conf. Intelligent Systems for Cybersecurity (ISCS), 2024, doi: 10.1109/ISCS61804.2024.10581344.
13. M. Balamurugan, G. Gunasekaran, T. Margaret Mary, and R. Kalaiarasi, “Optimized cloud security using time-oriented latency approximation-based data encryption with Levy-flight whale optimization,” SN Comput. Sci., vol. 7, no. 5, p. 445, 2026, doi: 10.1007/s42979-026-05052-y.
14. M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
15. S. Pandikumar, M. O. Pallavi, C. Dhanush, and M. Arun, “Quantum safe cryptography – An overview,” in Innovations and Trends in Modern Computer Science Technology – Overview, Challenges and Applications, S. Pandikumar and M. K. Thakur, Eds. QTanalytics, 2024, pp. 30–56, doi: 10.48001/978-81-980647-5-2-4.
16. National Institute of Standards and Technology, “Module-lattice-based key-encapsulation mechanism standard,” Federal Information Processing Standards Publication FIPS 203, Gaithersburg, MD, USA, Aug. 2024, doi: 10.6028/NIST.FIPS.203.
17. S. Pandikumar and S. Ambethkar, “A design of authentication and access control model for remote access using GSM technologies,” Int. J. Comput. Appl., vol. 57, no. 14, pp. 12–15, Nov. 2012, doi: 10.5120/9181-3601.
18. H.-N. Dai, Z. Zheng, and Y. Zhang, “Blockchain for Internet of Things: A survey,” IEEE Internet Things J., vol. 6, no. 5, pp. 8076–8094, 2019, doi: 10.1109/JIOT.2019.2920987.
19. M. A. Ferrag, O. Friha, D. Hamouda, L. Maglaras, and H. Janicke, “Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning,” IEEE Access, vol. 10, pp. 40281–40306, 2022, doi: 10.1109/ACCESS.2022.3165809.
20. V. Pandimurugan, J. Umamageswaran, S. Karattil, V. Rajaram, B. Balakiruthiga, and A. Angayarkanni, “Security challenges in the Internet of Medical Things and cyber-physical systems,” in AI-enabled Sustainable Healthcare. Amsterdam, The Netherlands: Elsevier, 2026, pp. 41–65, doi: 10.1016/B978-0-443-33700-0.00004-2.
21. H. Bangali, P. Rodrigues, V. Pandimurugan, S. Rajasoundaran, S. V. N. Santhosh Kumar, M. Selvi, and A. Kannan, “Prediction of middle box-based attacks in Internet of Healthcare Things using ranking subsets and convolutional neural network,” Wireless Netw., vol. 30, no. 3, pp. 1493–1511, 2024, doi: 10.1007/s11276-023-03603-2.
22. M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, “A survey of network-based intrusion detection data sets,” Comput. Secur., vol. 86, pp. 147–167, 2019, doi: 10.1016/j.cose.2019.06.005.
23. M. Alhefdi, Y. Alotaibi, P. Rodrigues, and V. Pandimurugan, “Enhanced threat detection in health care systems with random coupled bootstrapped ensemble classifier,” Health Inform. J., vol. 32, no. 1, Art. no. 14604582261429465, 2026, doi: 10.1177/14604582261429465.
24. D. Gopinath, M. Balamurugan, T. Margaret Mary, A. Kanagaraj, and S. Sharmila, “Dynamic mimic-based moving target defense mechanisms for enhancing security and resilience in mobile ad hoc networks,” in Proc. 4th Int. Conf. Intelligent Computing, Information and Control Systems (ICOIICS), 2025, pp. 487–493, doi: 10.1109/ICOIICS67115.2025.11390105.
25. S. Pandikumar, A. Bostani, D. Sundaranarayana, I. Fakhriddin, D. Alimjon, U. Ilkhom, and A. Juraboyev, “AI-driven nonlinear optimization for knowledge extraction and pattern analysis in IoT-enabled data mining systems,” Results Nonlinear Anal., vol. 8, no. 3, pp. 36–46, 2025, doi: 10.31838/rna/2025.08.03.005.
26. [26] D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated learning for Internet of Things: A comprehensive survey,” IEEE Commun. Surveys Tuts., vol. 23, no. 3, pp. 1622–1658, 2021, doi: 10.1109/COMST.2021.3075439.
27. P. Kairouz et al., “Advances and open problems in federated learning,” Found. Trends Mach. Learn., vol. 14, no. 1–2, pp. 1–210, 2021, doi: 10.1561/2200000083.
28. S. Pandikumar, V. Pandimurugan, A. Rama Prasath, S. Karattil, S. C. B. Jaganathan, and P. Vigneshwaran, “A novel framework for differential privacy based federated continual learning for dynamic medical data analysis,” SN Comput. Sci., vol. 7, no. 6, p. 664, 2026, doi: 10.1007/s42979-026-05245-5.
29. Y. Wang, Z. Su, N. Zhang, R. Xing, D. Liu, T. H. Luan, and X. Shen, “A survey on metaverse: Fundamentals, security, and privacy,” IEEE Commun. Surveys Tuts., vol. 25, no. 1, pp. 319–352, 2023, doi: 10.1109/COMST.2022.3202047.
30. C. Anusha, P. S. Ambili, P. D. Sowmya, A. Surendran, and T. Margaret Mary, “The role of AI technologies in data privacy and protection in the metaverse era: A detailed review,” in Proc. Int. Conf. Intelligent Systems and Pioneering Innovations in Robotics and Electric Mobility (INSPIRE), 2025, pp. 712–717, doi: 10.1109/INSPIRE67328.2025.11300487.
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