Secured Cloud Systems using Machine Learning

Abstract

Cloud security plays an important role to attract customers for data security and data privacy. The online attackers disrupt to use 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 the security has become main agenda of cloud computing usage by the customers of cloud. As a result, the financial growths of cloud based organizations are increasing gradually. The critical situation of cloud security is to identify the types of attacks and decides how to defend such attacks in order to protect the cloud data from the attackers. Numerous methodologies are reviewed to decide the strong security mechanisms for cloud security in order to protection from DoS or DDoS attacks (by malware attacks, MITM attacks, or other medium attacks). The surveys also reveal that Machine learning is not enough to protect the cloud system. So, the issues found in the methodologies reviews have motivated to develop novel advance security mechanisms for cloud computing. Thus, this research also moves towards high-level technologies like Blockchain and Quantum computing with Machine Learning (ML) concepts. Also, the ML concept has incorporated with different algorithm conceptions like deep neural network, and quantum neural network to enhance the prediction and protection accuracy. These proposed models not only help to reduce the attacks level up to 100% but also increase the trust of cloud users with financial growth to cloud service providers (CSPs). The contribution of this research endeavours are to eradicate such issues and advocates in end to end protection and secrecy of users data reside in cloud environment provider just because data security and data privacy are the prime concerns in this digital world. This outcome shows that the cloud environment becomes very much handy for end users in security aspects.