A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

Abstract

The recent development of cloud computing offers various services on demand for organization and individual users, such as storage, shared computing space, networking, etc. Although Cloud Computing provides various advantages for users, it remains vulnerable to many types of attacks that attract cyber criminals. Distributed Denial of Service (DDoS) is the most common type of attack on cloud computing. Consequently, Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks. Since DDoS attacks have become increasingly widespread, it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks. Further, the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and urgent. In this research work, DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory (LSTM) model have been proposed with NSL-KDD dataset. In Hybrid LSTM method, the Particle Swarm Optimization (PSO) technique, which is combined to optimize the weights of the LSTM neural network, reduces the prediction error. This deep belief network method is used to extract the features of IP packets, and it identifies DDoS attacks based on PSO-LSTM model. Moreover, it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks. The proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine (SVM) and LSTM in terms of attack detection performance along with the results of the measurement of accuracy, recall, f-measure, precision.