An Improved Model for Enhancing Cloud Security Through Hybrid Optimization of Intrusion Detection

Nowadays, the cloud computing plays a crucial role in digital transformation and is a vital technology for processing huge amounts of data. It has become essential for the smooth functioning of various business infrastructures. However, cloud-delivered services and applications face several security threats and challenges. To address these issues, it’s necessary to ensure the security, confidentiality, integrity and availability of data in the cloud. The use of Intrusion Detection Systems (IDS) is one of the fundamental solutions available to tackle these challenges. In this study, our objective is to enhance the cloud security by proposing and implementing a new optimized intrusion detection system capable of improving detection accuracy and reducing the rate of false alerts. We define a hybrid technique that combines optimization with classification algorithms in machine learning as well as in deep learning. The main purpose of this hybrid approach is to boost accuracy, precision and performance of the cloud intrusion detection systems while reducing false alerts. To verify security and assess the validity of our proposal, we conduct experiments to validate its accuracy, precision, robustness and correctness.