Deep SSAE-BiLSTM Model for DDoS Detection In SDN

Abstract

As a new network paradigm, Software-defined networking (SDN) realizes centralized management of the network by separating the control plane and the data plane. While SDN greatly improves network management capabilities, it also brings some security risks such as Distributed Denial of Service (DDoS) attack. How to effectively detect abnormal traffic has always been a hot issue in the field of network security. This paper proposes an improved attack detection model SSAE-BiLSTM based on deep learning. The stacked sparse autoencoder (SSAE) is used to extract high-dimensional features of data, and bidirectional long short-term memory (BiLSTM) is used to classify network traffic. This model can effectively detect network attacks with higher accuracy and lower false alarm rate on the benchmark dataset UNSW-NB15.