Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography - Padma Bhogaraju; D. Deepthi (International Journal of Advanced Control and System Engineering, 2024)
In modern technological landscapes, private cloud security is of paramount concern due to the ever-increasing volume and complexity of cyber threats. This research work explores the integration of machine learning and cryptography to enhance security within private cloud environments. This study aims to mitigate vulnerabilities that may compromise data integrity, confidentiality, and availability in private cloud infrastructures by using machine learning algorithms and strong cryptography. By detecting anomalous cloud patterns and behaviours, machine learning algorithms enable proactive threat detection. Machine learning models can distinguish normal network activity from malicious incursions by analysing large datasets, improving early threat detection and response. By using cryptographic protocols like homomorphic encryption and multi-party computation, private clouds can protect sensitive data while maintaining computational capabilities. This study examines how machine learning and cryptography can strengthen private cloud security against sophisticated cyber-attacks. Industry practitioners, academic scholars, and cyber security professionals seeking innovative private cloud asset protection strategies will benefit from the findings. As organizations move to private cloud infrastructures for flexibility and scalability, advanced security paradigms are needed to adapt to changing threats. This study suggests that combining cutting-edge technologies like machine learning and cryptography can enhance private cloud defences, both theoretically and practically
Keywords: Homomorphic encryption; cryptography and private cloud Security; machine learning models; multi-party computations