Optimization Accuracy of Secured Cloud Systems Using Deep Learning Model

Cloud security is vital in attracting data security and privacy customers. The online attackers disrupt the use of the on-demand services provided by the cloud service providers for their clients. It is distributed in nature for resource sharing over the Internet. Thus, security has become the main agenda for cloud computing usage by cloud customers. As a result, the financial growth of cloud-based organizations is increasing gradually. The critical situation of cloud security is identifying the types of attacks and deciding how to defend such attacks to protect the cloud data from attackers. Numerous methodologies are reviewed to determine the robust security mechanisms for cloud security to protect against DoS or DDoS attacks (by malware attacks, MITM attacks, or other medium attacks). The ML concept has incorporated algorithm conceptions like deep neural networks and quantum neural networks to enhance prediction and protection accuracy. These proposed models not only help to reduce the attack level up to $100 \%$ but also increase the trust of cloud users with financial growth to cloud service providers (CSPs).