A Framework for Providing Security for Cloud SaaS Model through an Enhanced Sea Lion Optimization Algorithm

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Reddy Saisindhutheja, Gopal K. Shyam


The Cloud paradigm is increasing very rapidly due to its on-demand services. Software-as-a Service (SaaS) is one amongst the most outstanding and fastest-growing fields in the era of Cloud computing. Organizations are adopting SaaS solutions, which offer several advantages, mostly in minimizing cost and time. Over all the excitement around SaaS, security is one of the foremost critical issues for its growth in Cloud computing. Hence this paper introduces a novel framework for detecting the DoS attacks using an enhanced Sea Lion Optimization Algorithm (SLnO) known as Fitness updated Sea Lion Optimization Algorithm (FSLnO). The proposed work has two stages (i) feature selection using FSLnO and (ii) classification through Recurrent Neural Network (RNN). It ensures the separation of normal and compromised date. For evaluation KDD cup 99 dataset is used and evaluated in terms of Precision, Accuracy, False Positive and Negative rates. Results prove that the proposed work outperformed the other conventional models.

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