Integration of recurrent convolutional neural network and optimal encryption scheme for intrusion detection with secure data storage in the cloud. (23rd October 2020) - Prabhakaran, Varun; Kulandasamy, Ashokkumar
Abstract: Data communication security is growing day after day with the proliferation of cloud computing. It is primarily because of the few security constraints and challenges occurring in the cloud environment during data transmission. Existing research has shown that the intrusion detection system (IDS) centered on the cloud is more complicated. In this article, we address the above issues by proposing an attention‐based recurrent convolutional neural network (RCNN). This proposed RCNN is used to detect whether the text data are intrusion or nonintrusion. The nonintrusion text information is then used for further processing and encrypted using a two‐way encryption scheme. We introduce the elliptical curve cryptography (ECC) approach to increase the security‐level performance of nonintrusion data. Moreover, the integration of ECC with the modified flower pollination algorithm (MFP‐ECC) creates the two‐way encryption scheme, and it is used to produce an optimal private key. The encrypted data are then stored in a cloud environment by steganography and the data with the sensitive information are replaced by some other text, thus providing security to the data at rest. The proposed MFP‐ECC approach shows maximum breaking time results and can also withstand different classical attacks when compared with other methods. As a result, the proposed intrusion detection and secure data storage mechanism is highly secured and it is never affected by any kinds of conspiracy attacks.