Deep Learning Algorithms for Detecting Denial of Service Attacks in Software-Defined Networks

Abstract

In Software-Defined Networking (SDN) the controller is the only entity that has the complete view on the network, and it acts as the brain, which is responsible for traffic management based on its global knowledge of the network. Therefore, an attacker attempts to direct malicious traffic towards the controller, which could lead to paralyze the entire network. In this work, Deep Learning algorithms are used to protect the controller by applying high-security measures, which is essential for the continuous availability and connectivity in the network. Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are proposed to recognize and prevent the intrusion attacks. We evaluate our models using a recently released dataset (InSDN dataset). Finally, our experiments manifest that our models achieve very high accuracy for the detection of Denial of Service (DoS) attacks. Thus, a significant improvement in attack detection can be shown compared to one of the benchmarking state of the art approaches.