A Deep Belief Network based Attack Detection using a Secure SaaS Framework

Abstract

Software-as-a-service (SaaS) is a license to get a particular software via Internet. Moreover, these services are overdue and completely interrupted due to the Internet's unavailability which offers more number of threats. Research regarding cloud security concentrates more on declining the unauthorized persons to initiate the attacks by using cloud. This work introduces an innovative framework for SaaS security by detecting the attacks. The major contribution of this work offers an attack detection process with Deep Belief Network (DBN) and an Enhanced Sea Lion Optimization algorithm (ESLnO) which is extended form of the Sea Lion Optimization algorithm. The results show that the proposed technique outperformed with other conventional models.