An Integrated Framework for Data Security Using Advanced Machine Learning Classification and Best Practices
Abstract
In the current interconnected digital environment, data security has become a paramount concern, as cyberattacks and data breaches are increasing in frequency and complexity. Both organizations and people face challenges in safeguarding sensitive information, requiring resilient security systems that can adjust to various threats. This paper presents a comprehensive approach to data security, focusing on integrating advanced classification techniques and best practices to secure data proactively. This study uses and analyzes advanced classification algorithms like decision trees, support vector machines (SVM), and neural networks to determine how well they work to find, sort, and keep sensitive data safe across various security needs. The results indicate substantial improvements in classification accuracy, with the optimal model attaining an accuracy rate of 98.83%. The other models, including decision tress and SVM provide 89% and 92% accuracy, respectively. This highlights the dependability and resilience of these methods in detecting possible security concerns across various datasets. In addition to these classification results, we comprehensively analyze industry best practices in data security, encompassing encryption technologies, dynamic access control, and continuous monitoring to mitigate vulnerabilities and improve threat detection. Integrating sophisticated classification methodologies with these optimal practices provides a comprehensive security framework that enhances data protection and mitigates risk. This study offers significant insights for practitioners and organizations aiming to implement a more systematic and efficient data security approach, enhancing academic and practical discussions in this domain. This work seeks to strengthen the effectiveness of data security practices by introducing a novel method that integrates high-accuracy categorization with proactive security protocols.References
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