Network Intrusion Detection System Based Security System for Cloud Services Using Novel Recurrent Neural Network - Autoencoder (NRNN-AE) and Genetic

Abstract

Cloud Computing (CC) is a platform where resources and services are huge such as platforms, infrastructure, software and much more. Cloud computing builds its entire environment on the framework based on the user's requirement. Although many interventions are implemented for the problems that are identified in cloud security systems, intrusion and security issues on various services are rising day by day. This research focuses on cloud security systems where trusted access can be guaranteed for various resources and services using deep learning techniques. Deep Learning techniques can detect the anomaly variation based on selected features to find the intruder in the service provider's environment. A Novel Recurrent Neural Network (NRNN) - Auto Encoder (AE) model with a dataset is used to identify the abnormal and behavioral variation in the network. The proposed algorithm NRNN-AE is basically identifying the uncertainty of different types of malicious theft where the auto-encoder predicts the attacks against the unexpected network security challenges along with a genetic algorithm for optimization. Attacks based on the service are identified on each hidden layer based on classification that is processed in the cloud system. The results are obtained from the comparison of NSL-KDD dataset and KDD Cup 99 dataset for monitoring the behavioral and frequent changes in patterns. The system can improve the detection rate and achieve accuracy of 96% compared to the existing RC-NN model. Also the detection rate is reduced to 0.0008 which has a precision value in both positive and negative rate as a gradual increase in performance.