Neural Network for Automatic Encryption Using Key Component Analysis to Detect Network Intrusion in Cloud Computing

Abstract

Cloud computing is one of the networks that has attracted more people's attention than other networks in today's world, and the reason for this is that it stores data in itself. This network consists of different computers that are scattered in different places and each user will be able to be a member in this space by registering in it. Among the many important issues related to the environment is the issue of security, which has become a very complex challenge. In order to secure networks, tools have been created, and one of these tools is the use of artificial intelligence methods to establish security in this environment. In most of the researches carried out in this field, either the duration of the algorithm execution was very long or the researches did not have enough accuracy, so  article, we tried to detect the penetration in cloud computing. This was done in two different scenarios for feature selection and reduction. In one scenario, we reduced the dimensions using the fundamental analysis method, and in the other scenario, we did this using the bat meta-heuristic algorithm. In both scenarios, we used self-encrypting neural network for classification, and the structure of this network was the same in both scenarios. The reason for using these two scenarios was that we want to compare the accuracy obtained and the execution time of the algorithm for the dimensionality reduction method with principal component analysis and meta-heuristic algorithm.  , the obtained results showed that the accuracy of the dimension reduction method with the meta-heuristic algorithm was more accurate than the principal component analysis method, but its execution time was approximately 13 minutes more than the principal component analysis method.