International Journal of Futuristic Innovation in Engineering, Science and Technology
DOI:
https://doi.org/10.59367/hvyd5106Keywords:
AI-driven threat detection, cloud cybersecurity, automated incident response, machine learning, cloud securityAbstract
With the rapid expansion of cloud computing, the need for robust cybersecurity measures has become paramount. As organizations increasingly migrate their data and applications to the cloud, they encounter numerous cybersecurity risks that threaten the integrity, confidentiality, and availability of their information. Traditional risk assessment methods often fall short in addressing the dynamic and complex nature of cloud environments. This paper explores a novel approach to cybersecurity risk assessment in cloud computing using machine learning techniques. We propose a comprehensive framework that leverages machine learning algorithms to detect, predict, and mitigate potential cybersecurity threats. The framework incorporates various supervised and unsupervised learning models, including decision trees, support vector machines, and neural networks, to analyze large datasets and identify patterns indicative of security breaches. Our approach also includes feature selection methods to optimize the performance of these models by focusing on the most relevant risk factors. We conducted extensive experiments on publicly available cloud security datasets, which demonstrated the efficacy of our machine learning-based risk assessment framework in identifying threats with high accuracy and minimal false positives. The results indicate that our approach significantly outperforms traditional risk assessment techniques in terms of speed, scalability, and adaptability to evolving threat landscapes. This study contributes to the field by providing a scalable and efficient solution for enhancing cybersecurity in cloud environments. It highlights the potential of machine learning to revolutionize how we assess and manage cybersecurity risks, offering a proactive stance against emerging threats. Future work will focus on refining the model by incorporating real-time data and exploring advanced machine learning techniques such as deep learning and reinforcement learning to further enhance its predictive capabilities.
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