A Deep Learning based Hybrid Approach for DDoS Detection in Cloud Computing Environment

Cloud computing refers to the availability of IT resources based on the, particularly data storage and processing power, without the direct intervention of users to manage them. Perpetrators are taking advantage of its multi-tenant feature to deny services to the clients by orchestrating attacks called Denial of Service (DoS) attacks. A more advanced version of DoS attacks called Distributed Denial of Service (DDoS) attacks has been causing a lot of damage to service providers and clients. As a result, the importance of detecting these attacks is critical. This paper provides a hybrid approach for detecting the DDoS attacks using Self Organizing Map (SOM) and Artificial Neural Network (ANN). SOM is used to find the outliers, i.e. attacks, by reducing the high-dimensional dataset into a 2-dimensional representation of input space. ANN is trained on the outliers from SOM to predict attacks. The dataset used for the experiment is the real cyber defense dataset CICIDS 2017. The proposed approach achieves high accuracy of 99.66% and low false positive rate of 0.004.