Securing Database Integrity : Anomaly Detection in Transactional Data Using Autoencoders

Authors

  • Sandeep Kumar Dasa  Independent Researcher, USA

Keywords:

Autoencoders, Anomaly Detection, Transactional Data, Database Integrity, Machine Learning, Reconstruction Errors, Data Security, Fraud Detection, Unsupervised Learning, Real-Time Monitoring

Abstract

The present paper investigates the applicability of autoencoders to flag anomalous activity in transactional data to maintain database health and increase protection. Autodecoders are certain types of unsupervised machine learning models taught to examine original normal-type patterns and then utilize the reconstruction error to detect variations as anomalies. The major goal is to evaluate the possibility of identifying such adversities as fraud, system failure, or invasion of databases using autoencoders. The method entails feeding the autoencoder with normal transactional data; subsequent real-time data is evaluated for reconstruction error and flagged as abnormal. Evaluations presented in this paper demonstrate that autoencoders may enhance the detection of anomalies with fewer false positives and increase the model's effectiveness when working with small datasets. The value of this research is founded on the applicability of the machine learning tool to improve the qualities of transactional databases by supplementing or, in some cases, replacing conventional technologies for data protection from various threats.

References

  1. Alam, M. S., Fernando, B. R., Jaoudi, Y., Yakopcic, C., Hasan, R., Taha, T. M., & Subramanyam, G. (2019, July). Memristor based autoencoder for unsupervised real-time network intrusion and anomaly detection. In Proceedings of the International Conference on Neuromorphic Systems (pp. 1-8). https://dl.acm.org/doi/pdf/10.1145/3354265.3354267
  2. Gunnam, V. G., Kilaru, N. B., & Cheemakurthi, S. K. M. (2022). Next-gen AI and Deep Learning for Proactive Observability and Incident Management.Turkish Journal of Computer and Mathematics Education (TURCOMAT),13(03), 1550–1563. https://doi.org/10.61841/turcomat.v13i03.14765
  3. Gunnam, V. G., Kilaru, N. B., & Cheemakurthi, S. K. M. (2022). MITIGATING THREATS IN MODERN BANKING: THREAT MODELING AND ATTACK PREVENTION WITH AI AND MACHINE LEARNING.Turkish Journal of Computer and Mathematics Education (TURCOMAT),13(03), 1564–1575. https://doi.org/10.61841/turcomat.v13i03.14766
  4. Vasa, Y., & Singirikonda, P. (2022). Proactive Cyber Threat Hunting With AI: Predictive And Preventive Strategies. International Journal of Computer Science and Mechatronics, 8(3), 30–36.
  5. Vasa, Y., Cheemakurthi, S. K. M., & Kilaru, N. B. (2022). Deep Learning Models For Fraud Detection In Modernized Banking Systems Cloud Computing Paradigm. International Journal of Advances in Engineering and Management, 4(6), 2774–2783. https://doi.org/10.35629/5252-040627742783
  6. Katikireddi, P. M. (2022). Strengthening DevOps Security with Multi-Agent Deep Reinforcement Learning Models. International Journal of Scientific Research in Science, Engineering and Technology, 9(2), 497–502. https://doi.org/https://doi.org/10.32628/IJSRSET2411159
  7. Mallreddy, S. R., & Vasa, Y. (2022). Autonomous Systems In Software Engineering: Reducing Human Error In Continuous Deployment Through Robotics And AI. NVEO - Natural Volatiles & Essential Oils, 9(1), 13653–13660. https://doi.org/https://doi.org/10.53555/nveo.v11i01.5765
  8. Belidhe, S. (2022b). Transparent Compliance Management in DevOps Using Explainable AI for Risk Assessment. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(2), 547–552. https://doi.org/https://doi.org/10.32628/CSEIT2541326
  9. Gunnam, V. G., Kilaru, N. B., & Cheemakurthi, S. K. M. . (2022). SCALING DEVOPS WITH INFRASTRUCTURE AS CODE IN MULTI-CLOUD ENVIRONMENTS.Turkish Journal of Computer and Mathematics Education (TURCOMAT),13(2), 1189–1200. https://doi.org/10.61841/turcomat.v13i2.14764
  10. Vasa, Y., & Mallreddy, S. R. (2022). Biotechnological Approaches To Software Health: Applying Bioinformatics And Machine Learning To Predict And Mitigate System Failures. Natural Volatiles & Essential Oils, 9(1), 13645–13652. https://doi.org/https://doi.org/10.53555/nveo.v9i2.5764
  11. Katikireddi, P. M., & Jaini, S. (2022). IN GENERATIVE AI: ZERO-SHOT AND FEW-SHOT. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT) , 8(1), 391–397. https://doi.org/https://doi.org/10.32628/CSEIT2390668
  12. Nunnagupala, L. S. C. ., Mallreddy, S. R., & Padamati, J. R. . (2022). Achieving PCI Compliance with CRM Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13(1), 529–535.
  13. Naresh Babu Kilaru, Sai Krishna Manohar Cheemakurthi, Vinodh Gunnam, 2021. "SOAR Solutions in PCI Compliance: Orchestrating Incident Response for Regulatory Security"ESP Journal of Engineering & Technology Advancements 1(2): 78-84.
  14. Jangampeta, S., Mallreddy, S.R., & Padamati, J.R. (2021). Anomaly Detection for Data Security in SIEM: Identifying Malicious Activity in Security Logs and User Sessions. 10(12), 295-298
  15. Vasa, Y. (2021). Robustness and adversarial attacks on generative models. International Journal for Research Publication and Seminar, 12(3), 462–471. https://doi.org/10.36676/jrps.v12.i3.1537
  16. Kilaru, N. B., Cheemakurthi, S. K. M., & Gunnam, V. (n.d.). Advanced Anomaly Detection In Banking: Detecting Emerging Threats Using Siem. International Journal of Computer Science and Mechatronics, 7(4), 28–33.
  17. Naresh Babu Kilaru. (2021). AUTOMATE DATA SCIENCE WORKFLOWS USING DATA ENGINEERING TECHNIQUES. International Journal for Research Publication and Seminar, 12(3), 521–530. https://doi.org/10.36676/jrps.v12.i3.1543
  18. Gunnam, V., & Kilaru, N. B. (2021). Securing Pci Data: Cloud Security Best Practices And Innovations. Nveo, 8(3), 418–424. https://doi.org/https://doi.org/10.53555/nveo.v8i3.5760
  19. Katikireddi, P. M., Singirikonda, P., & Vasa, Y. (2021). Revolutionizing DEVOPS with Quantum Computing: Accelerating CI/CD pipelines through Advanced Computational Techniques. Innovative Research Thoughts, 7(2), 97–103. https://doi.org/10.36676/irt.v7.i2.1482
  20. Vasa, Y. (2021). Quantum Information Technologies in cybersecurity: Developing unbreakable encryption for continuous integration environments. International Journal for Research Publication and Seminar, 12(2), 482–490. https://doi.org/10.36676/jrps.v12.i2.1539
  21. Jangampeta, S., Mallreddy, S. R., & Padamati, J. R. (2021). Data Security: Safeguarding the Digital Lifeline in an Era of Growing Threats. International Journal for Innovative Engineering and Management Research, 10(4), 630-632.
  22. Singirikonda, P., Jaini, S., & Vasa, Y. (2021). Develop Solutions To Detect And Mitigate Data Quality Issues In ML Models. NVEO - Natural Volatiles & Essential Oils, 8(4), 16968–16973. https://doi.org/https://doi.org/10.53555/nveo.v8i4.5771
  23. Vasa, Y., Jaini, S., & Singirikonda, P. (2021). Design Scalable Data Pipelines For Ai Applications. NVEO - Natural Volatiles & Essential Oils, 8(1), 215–221. https://doi.org/https://doi.org/10.53555/nveo.v8i1.5772
  24. Sukender Reddy Mallreddy(2020).Cloud Data Security: Identifying Challenges and Implementing Solutions.JournalforEducators,TeachersandTrainers,Vol.11(1).96 -102.
  25. Vasa, Y. (2021). Develop Explainable AI (XAI) Solutions For Data Engineers. NVEO - Natural Volatiles & Essential Oils, 8(3), 425–432. https://doi.org/https://doi.org/10.53555/nveo.v8i3.5769
  26. Singirikonda, P., Katikireddi, P. M., & Jaini, S. (2021). Cybersecurity In Devops: Integrating Data Privacy And Ai-Powered Threat Detection For Continuous Delivery. NVEO - Natural Volatiles & Essential Oils, 8(2), 215–216. https://doi.org/https://doi.org/10.53555/nveo.v8i2.5770
  27. Kilaru, N. B., & Cheemakurthi, S. K. M. (2021). Techniques For Feature Engineering To Improve Ml Model Accuracy. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO, 194-200.

International Journal of Scientific Research in Science and Technology

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Published

2022-07-14

Issue

Volume 9, Issue 4, July-August-2022

Section

Research Articles

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How to Cite

[1]
Sandeep Kumar Dasa "Securing Database Integrity : Anomaly Detection in Transactional Data Using Autoencoders" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 4, pp.768-774, July-August-2022.