Ensemble-RNN: A Robust Framework for DDoS Detection in Cloud Environment

The advent of cloud computing has made it simpler for users to gain access to data regardless of their physical location. It works for as long as they have access to the internet through an approach where the users pay based on how they use these resources in a model referred to as “pay-as-per-usage”. Despite all these advantages, cloud computing has its shortcomings. The biggest concern today is the security risks associated with the cloud. One of the biggest problems that might arise with cloud services availability is Distributed Denial of Service attacks (DDoS). DDoS attacks work by multiple machines attacking the user by sending packets with large data overhead. Therefore, the network is overwhelmed with unwanted traffic. This paper proposes an intrusion detection framework using Ensemble feature selection with RNN (ERNN) to tackle the problem at hand. It combines an Ensemble of multiple Machine Learning (ML) algorithms with a Recurrent Neural Network (RNN). The framework aims to address the issue by selecting the most relevant features using the ensemble of six ML algorithms. These selected features are then used to classify the network traffic as either normal or attack, employing RNN. The effectiveness of the proposed model is evaluated using the CICDDoS2019 dataset, which contains new types of attacks. To assess the performance of the model, metrics like precision, accuracy, F-1 score, and recall are taken into consideration.