Study on Real-Time Data Integrity and Security Module Using Machine Learning


Authors : Greeva Dinesh Kumar Patel; Kartikeydheer Srivastava; Mansi Mehta

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/53ry8psp

Scribd : https://tinyurl.com/3c9a2tmj

DOI : https://doi.org/10.5281/zenodo.14964519

Abstract : The rapid increase of interconnected virtual ecosystems, pushed by means of IoT devices, cloud infrastructures, and software program-described Networks (SDNs), has amplified the call for for sturdy actual-time information safety and integrity. This studies introduces a real-Time data Integrity and security Module (RTDISM) that leverages system getting to know (ML) to tackle these challenges. by way of integrating SDN architectures, light-weight federated getting to know, and adaptive authentication protocols, the RTDISM provides a scalable, multi-layered defense gadget suitable for diverse environments. Key capabilities include adaptive ML-pushed risk mitigation, low-latency responses, and efficient aid usage. The module is particularly proper for vital domain names together with healthcare, commercial automation, and self reliant systems. Innovat ions include SDN-primarily based security for improved network flexibility, light-weight ML models for aid optimization, and federated getting to know for decentralized, privateness-maintaining operations. Experimental opinions demonstrate the RTDISM's superior overall performance in accuracy, reaction time, and useful resource performance in comparison to present solutions, organising it as a benchmark for subsequent- technology cybersecurity structures.

Keywords : Real-Time Security, ML, IoT, SDN, Anomaly Detection, Edge AI, Federated Learning, Threat Mitigation.

References :

  1. M. Asmar and A. Tuqan, "Integrating machine learning for sustaining cybersecurity in digital banks," Heliyon, vol. 10, p. e37571, 2024. Available: https://doi.org/10.1016/j.heliyon.2024.e37571
  2. K. Masthan, M. Shabana, and M. Rafi, "Enhancing cloud security using machine learning," Journal of Systems Engineering and Electronics, vol. 34, no. 10, pp. 66–70, 2024. Available: https://www.researchgate.net/publication/386872942
  3. M. Austin, K. Austin, and M. Osaka, "Machine learning for data security in cloud computing environments," Research Proposal, 2024. DOI: 10.13140/RG.2.2.12840.14088. Available: http://dx.doi.org/10.13140/RG.2.2.12840.14088
  4. D. Alexander and Z. Chain, "Data Integrity Challenges and Solutions in Machine Learning-driven Clinical Trials," Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Aug. 2023. [Online]. Available: https://www.researchgate.net/publication/373214664
  5. E. Altulaihan, M. A. Almaiah, and A. Aljughaiman, "Cybersecurity Threats, Countermeasures and Mitigation Techniques on the IoT: Future Research Directions," Electronics, vol. 11, no. 3330, Oct. 2022. [Online]. Available: https://doi.org/10.3390/electronics11203330
  6. T. S. AlSalem, M. A. Almaiah, and A. Lutfi, "Cybersecurity Risk Analysis in the IoT: A Systematic Review," Electronics, vol. 12, no. 3958, Sep. 2023. [Online]. Available: https://doi.org/10.3390/electronics12183958
  7. S. K. Sahu and K. Mazumdar, "Exploring Security Threats and Solutions Techniques for Internet of Things (IoT): From Vulnerabilities to Vigilance," Frontiers in Artificial Intelligence, vol. 7, no. 1397480, May 2024. [Online]. Available: https://doi.org/10.3389/frai.2024.1397480
  8. S. B. Masud, M. M. Rana, H. J. Sohag, F. Shikder, M. R. Faraji, and M. M. Hasan, "Understanding the Financial Transaction Security Through Blockchain and Machine Learning for Fraud Detection in Data Privacy and Security," Pakistan Journal of Life and Social Sciences, vol. 22, no. 2, pp. 17782-17803, Dec. 2024. [Online]. Available: http://dx.doi.org/10.57239/PJLSS-2024-22.2.001296
  9. S. K. Devineni, S. Kathiriya, and A. Shende, "Machine Learning-Powered Anomaly Detection: Enhancing Data Security and Integrity," Journal of Artificial Intelligence & Cloud Computing, vol. 2, no. 2, pp. 1-9, May 2023. [Online]. Available: http://dx.doi.org/10.47363/JAICC/2023(2)184
  10. J. Bhayo, S. A. Shah, S. Hameed, A. Ahmed, J. Nasir, and D. Draheim, "Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks," Engineering Applications of Artificial Intelligence, vol. 123, p. 106432, May 2023. [Online]. Available: https://doi.org/10.1016/j.engappai.2023.106432
  11. F. Alwahedi, A. Aldhaheri, M. A. Ferrag, A. Battah, and N. Tihanyi, "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, vol. 4, pp. 167–185, Jan. 2024. [Online]. Available: https://doi.org/10.1016/j.iotcps.2023.12.003
  12. H. El-Sofany, S. A. El-Seoud, O. H. Karam, and B. Bouallegue, "Using machine learning algorithms to enhance IoT system security," Scientific Reports, vol. 14, no. 12077, 2024. [Online]. Available: http://dx.doi.org/10.1038/s41598-024-62861-y
  13. M. Salayma, "Risk and threat mitigation techniques in internet of things (IoT) environments: A survey," Frontiers in the Internet of Things, vol. 2, no. 1306018, Jan. 2024. [Online]. Available: https://doi.org/10.3389/friot.2023.1306018
  14. L. Doris and R. Shad, "Using machine learning models to identify and predict security-related anomalies in real-time for proactive maintenance," ResearchGate, Dec. 2024. Available: https://www.researchgate.net/publication/386573125_USING_MACHINE_LEARNING_MO DELS_TO_ IDENTIFY_AND_PREDICT_SECURITY-_RELATED_ANOMALIES_IN_REALTIME_ FOR_PROACTIVE_MAINTENA NCE
  15. G. Arjunan, "Optimizing Edge AI for Real-Time Data Processing in IoT Devices: Challenges and Solutions," International Journal of Scientific Research and Management (IJSRM), vol. 11, no. 6, pp. 944-953, June 2023. [Online]. Available: DOI:10.18535/ijsrm/v11i06.ec2
  16. A. S. Shaik and A. Shaik, "Code Injection Attack Prevention with AI- Integrated Machine Learning Approach Using CNN," ShodhKosh: Journal of Visual and Performing Arts, vol. 3, no. 2, pp. 848-854, Dec. 2022. [Online]. Available: https://doi.org/10.29121/shodhkosh.v3.i2.2022.3181
  17. M. Sugadev et al., "Implementation of Combined Machine Learning with the Big Data Model in IoMT Systems for the Prediction of Network Resource Consumption and Improving the Data Delivery," Computational Intelligence and Neuroscience, vol. 2022, Article ID 6510934, 12 pages, July 2022. [Online]. Available: https://doi.org/10.1155/2022/6510934
  18. M. A. Ferrag, L. A. Maglaras, H. Janicke, J. Jiang, and L. Shu, "Authentication Protocols for Internet of Things: A Comprehensive Survey," Security and Communication Networks, vol. 2017, Article ID 6562953, pp. 1– 41, 2017, [Online]. Available: https://doi.org/10.1155/2017/6562953
  19. N. Singh, R. Buyya, and H. Kim, "Securing Cloud-Based Internet of Things: Challenges and Mitigations," Sensors, vol. 25, no. 79, pp. 1–45, 2025, [Online]. Available: https://doi.org/10.3390/s25010079

The rapid increase of interconnected virtual ecosystems, pushed by means of IoT devices, cloud infrastructures, and software program-described Networks (SDNs), has amplified the call for for sturdy actual-time information safety and integrity. This studies introduces a real-Time data Integrity and security Module (RTDISM) that leverages system getting to know (ML) to tackle these challenges. by way of integrating SDN architectures, light-weight federated getting to know, and adaptive authentication protocols, the RTDISM provides a scalable, multi-layered defense gadget suitable for diverse environments. Key capabilities include adaptive ML-pushed risk mitigation, low-latency responses, and efficient aid usage. The module is particularly proper for vital domain names together with healthcare, commercial automation, and self reliant systems. Innovat ions include SDN-primarily based security for improved network flexibility, light-weight ML models for aid optimization, and federated getting to know for decentralized, privateness-maintaining operations. Experimental opinions demonstrate the RTDISM's superior overall performance in accuracy, reaction time, and useful resource performance in comparison to present solutions, organising it as a benchmark for subsequent- technology cybersecurity structures.

Keywords : Real-Time Security, ML, IoT, SDN, Anomaly Detection, Edge AI, Federated Learning, Threat Mitigation.

Video Explanation for Published paper

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