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Intelligent Protection for Cloud Infrastructure: Integrating Machine Learning into Security Practices
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 24 |
| Year of Publication: 2025 |
| Authors: Gavini Sreelatha, M. Shalini, Hanvitha Gavini |
10.5120/ijca2025925410
|
Gavini Sreelatha, M. Shalini, Hanvitha Gavini . Intelligent Protection for Cloud Infrastructure: Integrating Machine Learning into Security Practices. International Journal of Computer Applications. 187, 24 ( Jul 2025), 58-69. DOI=10.5120/ijca2025925410
Abstract
Cloud computing has emerged as a ubiquitous storage, processing, and data management technology. However, ensuring robust security measures within cloud infrastructure remains a paramount concern. Traditional security practices often need help to keep pace with the dynamic threat landscape and the scale of cloud environments. This proposal explores integrating machine learning techniques into security practices to establish intelligent protection for cloud infrastructure. By leveraging machine learning algorithms and models, this research seeks to enhance threat detection, anomaly detection, and access control mechanisms to safeguard sensitive data and mitigate emerging threats in the cloud. We will explore various machine learning techniques, including anomaly detection, behavior analysis, and predictive modeling, to enhance the accuracy and efficiency of security measures. We will develop a simulated cloud environment replicating real-world scenarios to evaluate the proposed approach. We will collect and preprocess representative datasets to train and validate machine learning models for threat detection, intrusion prevention, and access control. The performance of the integrated machine learning-based security framework will be evaluated using established metrics, such as detection rate, false positive rate, and response time. The expected contributions of this research include the development of an intelligent security framework that leverages machine learning algorithms to enhance cloud infrastructure protection. The proposed framework identifies and mitigates security threats by incorporating adaptive and proactive defense mechanisms. This research aims to expand the current understanding of how cloud security and machine learning can be integrated. The findings will assist cloud service providers, security practitioners, and researchers develop advanced security solutions for cloud infrastructure. Ultimately, this research endeavors to enhance the overall security posture of cloud computing, enabling organizations to harness the full potential of the cloud while safeguarding their critical assets and sensitive information.
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