An optimized radial bias function neural network for intrusion detection of distributed denial of service attack in the cloud

Cloud system is a set of resources meant to provide cloud customers with on-demand services. Accessibility to cloud environment is provided through internet services, making data stored on cloud more accessible to attackers internally and externally. Several intrusion detection systems and authentication methods have been developed in previous studies to identify the intruders, however they are generally unsuccessful with certain drawbacks. Numerous existing researchers have focused on machine learning techniques for identifying intrusions. This article intends to introduce a new intrusion detection model in the cloud and it involves three processes like preprocessing, feature extraction and detection. Initially, preprocessing takes place and the preprocessed data are subjected to feature extraction process. The flow-based features, statistical, and higher-order statistical features with improved holoentropy features, and the technical indicators are extracted. After extracting the features, they are subjected to a detection process, where the optimized radial bias function neural network (RBF-NN) is exploited. For precise detection, the weight of RBF-NN is tuned optimally by (harmonic mean based poor and rich optimization) HMPRO Algorithm. The main aim of the proposed model is to detect attacks in the cloud. Finally, the performance of the presented scheme is computed over the existing approaches using the CICDDoS2019 and UNSW-NB_15 dataset in terms of different metrics.