Novel Network IDS in Cloud Environment Based on Optimized BP Neural Network Using Genetic Algorithm

Abstract

Nowadays, Cloud Computing (CC) had become an integral part of IT industry. It represents the maturing of technology and is a pliable, cost-effective platform which provides business/IT services over the Internet. Although there are several benefits of adopting this technology, there are some significant hurdles to it and one of them is security. In fact, due to the distributed and open nature of the cloud, resources, applications, and data are vulnerable and prone to intrusions that affect confidentiality, availability and integrity of Cloud resources and offered services. Network Intrusion Detection System (NIDS) has become the most commonly used component of computer system security and compliance practices that defends network accessible Cloud resources and services from various kinds of threats and attacks, while maintaining performance and service quality. In this work, in order to detect intrusions in CC environment, we propose a novel anomaly NIDS based on Back Propagation Neural Network (BPNN) classifier optimized using Genetic Algorithm. Since, Learning rate and Momentum term are among the most relevant parameters that impact the performance of BPNN classifier, we have employed Genetic Algorithm to find the optimal values of these two parameters which ensure high detection rate, high accuracy and low false alarm rate. Experimental results on KDD CUP' 99 dataset indicate that in comparison to several traditional and new techniques, our proposed approach achieves higher detection rate and lower false positive rate.