Hough Transform based Deep Belief Network and Improved Homomorphic Encryption for Cloud Security based Intrusion Discovery

Abstract

The enlarge development in information technology is cloud computing, which offers minimized infrastructure cost, lower maintenance, greater flexibility and scalability. Nowadays, the network security plays vital role in enterprises and organizations. The influence vulnerabilities were occurred due to attackers based on network configuration. Because of cloud and IoT growth, enlarge amount of data obtained from IoT sensor and devices are transmitted to cloud data centers. Several security issues like focused web servers in the cloud and information collection mishandling are faced by storage and cloud-based computing when offering us considerable convenience. For that reason, this article proposes a deep learning-based cloud security oriented intrusion discovery. Primarily, the input dataset is pre-processed by using normalization techniques followed by the features are selected using an Adaptive White Shark Optimization (AWSO) algorithm. The normal and intrusion data is classified by using Hough Transform based Deep Belief Network (HT-DBN) after that the sensitive data are secured with the help of an Improved Homomorphic Encryption (IHE) model. The simulation tool of MATLAB is been used to simulate the proposed implementation part and the experimental results outperformed the detection accuracy of 97% than other previous approaches.