Scalable Network Intrusion Detection in Cloud Environments through Parallelized Swarm-Optimized Neural Networks

Abstract

Cloud computing (CC) offers on-demand, flexible resources and services over the internet, to secure cloud assets and resources, privacy and security remain a difficult challenge. To overcome this problem, we proposed a Modified Dove Swarm Optimization Based Enhanced Feed Forward Neural Network (MDSO-EFNN) to examine the network traffic flow that targets a cloud environment. Network Intrusion detection systems (NIDSs) are crucial in identifying assaults in the cloud environment, which helps to reduce the problem. In this study, we gather an NSL-KDD network traffic dataset. Secondly, collected data is preprocessed using Z-Score normalization to clean the data. Thirdly, Continuous wavelet transform (CWT) is employed to extract the unwanted data. Ant colony optimization (ACO) is used to choose the appropriate data. The selected appropriate data is used to test the process using MDSO-EFNN. The simulation findings of the result use a Python tool. As a result, our proposed method achieves significant outcomes with classification of accuracy (95%), precision rate (97%), sensitivity (98%), and specificity (96%).