Vol. 5 No. 1 (2025): Issue 5
Articles

Enhancement of data centric security through predictive ridge regression

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  • Shao Dan,
  • Qinyi Zhu
Shao Dan
ASCENDING Inc., Fairfax VA 22031, USA
Qinyi Zhu
Indiana University, 107 S Indiana Ave, Bloomington, IN 47405, USA
DOI: https://doi.org/10.71070/oaml.v5i1.113

Published 2025-05-09

Keywords

  • Data Centric Security,
  • Predictive Analytics,
  • Ridge Regression,
  • Security Breaches,
  • Protection Measures

How to Cite

Dan, S., & Zhu, Q. (2025). Enhancement of data centric security through predictive ridge regression. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.113
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Data centric security is becoming increasingly crucial in the digital age, as the volume and importance of data continue to grow exponentially. Current research has focused on developing strategies and technologies to safeguard data, but faces challenges in accurately predicting and preventing security breaches. This paper addresses these challenges by proposing a novel approach using predictive ridge regression to enhance data centric security. By integrating predictive analytics with ridge regression, our research aims to provide a more robust and proactive solution for data security, effectively mitigating risks and optimizing protection measures. Through empirical studies and practical implementations, this paper illustrates the effectiveness and potential of predictive ridge regression in fortifying data security systems, paving the way for future advancements in this critical domain.

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