A Subset Scaling Recursive Feature Collection Based DDoS Detection Using Behavioural Based Ideal Neural Network For Security In A Cloud Environment

Abstract

The Cloud Computing model improves cloud resources and reduces cloud user latency. The Cloud Computing model expands services such as network equipment, computer capabilities and storage devices. Cloud services are distributed naturally so that millions of users can share. Because of this, the cloud environment has many security tasks. Distributed Denial of Service (DDoS) Attacks and Techniques for Detecting and Preventing analysis in a cloud computing environment. Previous analysis has some drawbacks in DDoS attack detection, including security issues, Low Accuracy, and data loss. Identifying a DDoS attack is very difficult because it is a computational problem that needs to be addressed. To overcome the issues, this work proposed the method, Subset Scaling Recursive Factor Feature selection (S2RF2S), used to detect DDoS attacks based on Lattice Structural access rate using Soft-Max Behavioral Based Ideal Neural Network (SxB2IN2) used to detect DDoS attack detection. Initially, using the collection of the dataset for analysis in preprocessing step and reducing the imbalanced or irrelevant data from the dataset. Then, Subset Scaling Recursive Factor Feature selection (S2RF2S) for filtering the relational features based on the Lattice structural access rate. The lack of traffic bandwidth aspect balances; Social Spider Optimization analyzes these mutual balances to select Attack Features (S2OSAF) using features based on each feature's weights. Soft-Max activation for creating neurons to evaluate the features into subgroup feature selection and training with Behavioral Based Ideal Neural Network (SxB2IN2). This proposed system performs better for data loss and detecting DDoS attacks. The simulation results show the performance of the proposed method to avoid security issues in Cloud Computing.