Design of a Hypermodel using Transfer Learning to Detect DDoS Attacks in the Cloud Security

Abstract

The present research proposes a detective approach to analyzing the performance of various algorithms used for more accurate detection of Distributed Denial-of-Service (DDoS) attacks in cloud computing. From the start, this study uses machine learning and deep learning to explore whether information security has evolved in recent years. The deployment of intrusion detection systems and distributed denial-of-service attacks are then discussed. The most common DDoS attack types were summarized. In addition, this study reviewed the existing approaches and techniques for DDoS attack detection. Various pre-processing subsystems as well as attribute-based selection techniques for preventing the detection of DDoS were briefly described. The proposed Intrusion detection system uses transfer learning for detecting DDoS attacks in the Networks. The proposed system used for the data set for the Network Intrusion Detection System is SDN Dataset which has more features and is suitable to use to detect in Network Intrusions. It contains 23 features that are used to detect Intrusions in the network SDN Dataset which consists of training and testing data to detect the attacks in the network. The detection and prevention subsystems through ML and DL strategies were briefly discussed. The proposed deep learning model for DDoS attack detection in cloud storage applications is explained. After that, various preprocessing strategies employed in the detection are described, among them rebalancing data, data cleaning, data splitting, and data normalization like min-max normalization. The author created a hypermodel that consists the parameters of baseline classifiers like Support Vector Machine, K-Nearest Neighbors Algorithm, XGboost, and other various machine learning models. The proposed model gives very good accuracy compared to other machine learning models.