Research Article | Open Access | Download PDF
Volume 74 | Issue 10 | Year 2026 | Article Id. IJETT-V74I10P101 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I10P101Development of Data Structures using the DataGuard SIEM-DLP Algorithm for Data Loss Prevention and Data Analysis in Distributed Communication Networks
Tipaporn Supamid, Suwit Wongkumsin
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 07 May 2026 | 25 Jul 2026 | 18 Sep 2026 | 27 Oct 2026 |
Citation :
Tipaporn Supamid, Suwit Wongkumsin, "Development of Data Structures using the DataGuard SIEM-DLP Algorithm for Data Loss Prevention and Data Analysis in Distributed Communication Networks," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 10, pp. 1-14, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I10P101
Abstract
The research entitled “Development of Data Structures Using the DataGuard SIEM-DLP Algorithm for Data Loss Prevention and Data Analysis in Distributed Communication Networks” aims to develop the DataGuard SIEM-DLP algorithm. This study established two main research hypotheses: (1) to enhance the efficiency of security and safety in distributed communication networks, and (2) to develop a data structure based on the DataGuard SIEM-DLP algorithm that can operate with stability and support real-time processing within the specified time frame. The results of the data analysis can be summarized as follows: (1) system performance analysis showed an average of 95.7%, (2) time efficiency analysis during testing ranged between 25–100%, (3) data replication experiments achieved an average of 98.5%, and (4) data loss prevention testing showed an average of 91–94%. The datasets used for testing the DataGuard SIEM-DLP algorithm were obtained from the UNSW-NB15 and CIC-IDS2017 databases. Overall, the analysis revealed that the DataGuard SIEM-DLP algorithm reduced data loss during communication by up to 30% and increased data analysis efficiency by up to 90%. In addition, the distributed communication network system operated with significantly greater stability and efficiency.
Keywords
Data structure, Data loss prevention, Distributed communication networks.
References
- Andrew S. Tanenbaum, Distributed Systems: Principles and Paradigms, 2nd ed., Pearson Education, 2015.
[Google Scholar] [Publisher Link] - Kai Hwang, Jack Dongarra, and Geoffrey C. Fox, Distributed and Cloud Computing: From Parallel Processing to the Internet of Thing, 1st ed., Elsevier, 2012.
[Google Scholar] [Publisher Link] - J. Zhao, and L. Zhang, “An Efficient Data Replication Strategy for Distributed File Systems,” Journal of Computer Science and Technology, vol. 25, no. 4, pp. 754-767, 2010.
[Google Scholar] - C.E. Shannon, “A Mathematical Theory of Communication,” The Bell System Technical Journal, vol. 27, no. 3, pp. 379-423, 1948.
[CrossRef] [Google Scholar] [Publisher Link] - R.W. Hamming, “Error Detecting and Error Correcting Codes,” The Bell System Technical Journal, vol. 29, no. 2, pp. 147-160, 1950.
[CrossRef] [Google Scholar] [Publisher Link] - Nour Moustafa, and Jill Slay, “UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 Network Data Set),” 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, ACT, Australia, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link] - Praveen Yalagandula, and Mike Dahlin, “A Scalable Distributed Information Management System,” ACM SIGCOMM Computer Communication Review, vol. 34, no. 4, pp. 379-390, 2004.
[CrossRef] [Google Scholar] [Publisher Link] - R.L. Rivest, Adi Shamir, and Len Adleman, “A Method for Obtaining Digital Signatures and Public-Key Cryptosystems,” Communications of the ACM, vol. 21, no. 2, pp. 120-126, 1978.
[CrossRef] [Google Scholar] [Publisher Link] - Whitfield Diffie, and M. Hellman, “New Directions in Cryptography,” IEEE Transactions on Information Theory, vol. 22, no. 6, pp. 644-654, 1976.
[CrossRef] [Google Scholar] [Publisher Link] - Intrusion Detection Evaluation Dataset, Canadian Institute for Cybersecurity, University of New Brunswick, 2017. [Online]. Available: https://www.unb.ca/cic/datasets/ids-2017.html
- Brendan Burns, Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Systems using Kubernetes, O'Reilly Media, 2024.
[Google Scholar] [Publisher Link] - Debargha Ganguly, “Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks,” Advances in Neural Information Processing Systems 38 Main Conference (NeurIPS 2025), San Diego, California, USA, pp. 38457-38499, 2025.
[CrossRef] [Google Scholar] [Publisher Link] - Mykhailo Klymash et al., “Research of Distributed Data Processing in Corporate Information Systems,” 2021 IEEE 16th International Conference on the Experience of Designing and Application of CAD Systems (CADSM), Lviv, Ukraine, pp. 24-27, 2021.
[CrossRef] [Google Scholar] [Publisher Link] - Phanumas Buachin, Sophana Sudsomboon, and Arnon Jeenawat, “Administrative Factors Affecting the Information Technology using in the Administration of Schools under the Pathum Thani Secondary Educational Service Area Office,” Sukhothai Thammathirat Open University, vol. 11, no. 3, pp. 1-18, 2023.
[Google Scholar] [Publisher Link] - Tanyaporn Sridokmai, “Security Database on Encrypted Data using Homomorphic Encryption,” Dhonburi Rajabhat University Journal, vol. 13, no. 2, pp. 226-238, 2019.
[Google Scholar] [Publisher Link] - Shengdong Zhang et al., “A Novel Ultrathin Elevated Channel Low-Temperature Poly-Si TFT,” IEEE Electron Device Letters, vol. 20, no. 11, pp. 569-571, 1999.
[CrossRef] [Google Scholar] [Publisher Link] - Tatiana Chakravorti, Sai Koneru, and Sarah Rajtmajer, “Reproducibility and Replicability in Research: What 452 Professors Think in Universities Across the USA and India,” PLOS One, vol. 20, no. 3, pp. 1-19, 2025.
[CrossRef] [Google Scholar] [Publisher Link] - Sudhanshu Sekhar Tripathy, and Bichitrananda Behera, “A Review of Various Datasets for Machine Learning Algorithm-based Intrusion Detection System: Advances and Challenges,” arxiv, pp. 3833-3857, 2025.
[CrossRef] [Google Scholar] [Publisher Link] - Fatemeh Karamimirazizi, Seyed Mahdi Jameii, and Amir Masoud Rahmani, “Data Replication Methods in Cloud, Fog, and Edge Computing: A Systematic Literature Review,” Wireless Personal Communications, vol. 135, no. 1, pp. 531-561, 2024.
[CrossRef] [Google Scholar] [Publisher Link] - Jad Silbak, and Daniel Wichs, “Binary Codes for Error Detection and Correction in a Computationally Bounded World,” Advances in Cryptology – EUROCRYPT 2025, Springer, Cham, vol. 15601, pp. 186-211, 2025.
[CrossRef] [Google Scholar] [Publisher Link] - Asma Ben Abacha et al., “MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes,” Association for Computational Linguistics, pp. 22539-22550, 2025.
[CrossRef] [Google Scholar] [Publisher Link] - Jyoti Prakash Sahoo et al., “QCShield: A Quantum-Classical Hybrid Framework for Intrusion Detection in Edge-IIoT,” Advances in Distributed Computing and Machine Learning, Springer, Singapore, vol. 1406, pp. 1-18, 2025.
[CrossRef] [Google Scholar] [Publisher Link] - Phung Lai et al., “A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning,” 2025 IEEE 45th International Conference on Distributed Computing Systems (ICDCS), Glasgow, United Kingdom, pp. 1-11, 2025.
[CrossRef] [Google Scholar] [Publisher Link] - Srikanth Bellamkonda, The Future Encryption: Protecting Data in an Era of Rising Cyber Threats, Forbes Technology Council, 2025. [Online]. Available: https://www.forbes.com/councils/forbestechcouncil/2025/03/26/the-future-of-encryption-protecting-data-in-an-era-of-rising-cyber-threats/
- Craig Gentry, Shai Halevi, and Nigel P. Smart, “Homomorphic Evaluation of the AES Circuit,” Annual Cryptology Conference, Santa Barbara, CA, USA, vol. 1, pp. 850-867, 2012.
[CrossRef] [Google Scholar] [Publisher Link]