Cloud computing is a paradigm that allows on-demand network access to a shared pool of configurable and reliable computing resources to cloud customers in pay-per-use, fashion. Despite the existence of such merits, there are Security issues such as data integrity, users’ confidentiality, and service availability because of its open and distributed architecture that place restrictions on the use of cloud computing. A preventive approach is to identify such issues and eliminate before it can cause the serious impact to the cloud users. Nowadays, Intrusion Detection Systems (IDSs) are the most widely used method to detect attacks on cloud. Recently, learning-based techniques for security applications are gaining popularity in the literature with the emergence in machine learning. A deep learning is a novel approach to detect cloud threats. The existing Cloud IDSs suffer from low detection accuracy and a high false positive rate. In this research, proposed solution will use deep learning algorithm to improve the effectiveness of our proposed solution. Furthermore, the comparisons with other deep learning algorithm to demonstrate the effectiveness of our proposed solution are given.

Cloud Security, Network Intrusion Detection System, Deep Learning

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