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Research Paper | Computer Technology | India | Volume 12 Issue 11, November 2023 | Popularity: 4.6 / 10

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Enhancing Security in Cloud-Based Storage Systems Using Machine Learning

Praveen Kumar Thopalle

Abstract: As a Senior Engineer in the semiconductor security protection team, I tackle the pressing challenge of ensuring that trademarks and confidential data are shielded from unauthorized access and breaches. This paper delves into the deployment of an optimal machine learning (ML) model aimed at fortifying the security frameworks of cloud-based storage systems, with a particular focus on safeguarding sensitive corporate data. The study begins by identifying prevalent security vulnerabilities within cloud storage environments that pose risks to confidential information. We introduce a highly effective ML model, chosen for its proven ability in detecting and mitigating security threats related to unauthorized accesses. This model leverages labeled datasets containing examples of both typical and atypical access patterns, enabling the training of an advanced anomaly detection system capable of real-time threat identification. We assess the model's effectiveness by analyzing key performance metrics before and after its implementation, highlighting significant improvements in the detection of security threats, a reduction in false positives, and enhanced adaptability to new security challenges. The model's broad applicability and effectiveness are further evidenced through case studies in various industries, underscoring its potential utility in similar roles elsewhere [1]. The paper concludes with strategic insights on how to integrate this ML model into existing security frameworks effectively. Reflecting on my first project in my previous company three years ago, which was to secure company confidential data, this paper builds on those foundational principles to propose next-generation solutions [2]. Future research directions are discussed, emphasizing the potential integration of emerging technologies like federated learning for decentralized data privacy management and blockchain for robust transaction logs, aiming to advance cloud storage security further.

Keywords: Cloud Security, Machine Learning, Cloud-Based Storage, Data Protection, Cybersecurity, Threat Detection, Anomaly Detection, Encryption, Secure Cloud Storage, Data Privacy, Security Algorithms, Cloud Computing, ML Security Models, Cloud Infrastructure, Network Security

Edition: Volume 12 Issue 11, November 2023

Pages: 2216 - 2222

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