Hybrid Deep Learning: An Efficient Reconnaissance and Surveillance Detection Mechanism in SDN

Abstract

Software defined network (SDN) centralized control intelligence and network abstraction aims to facilitate applications, service deployment, programmability, innovation and ease in configuration management of the underlying networks. However, the centralized control intelligence and programmability is primarily a potential target for the evolving cyber threats and attacks to throw the entire network into chaos. The authors propose a control plane-based orchestration for varied sophisticated threats and attacks. The proposed mechanism comprises of a hybrid Cuda-enabled DL-driven architecture that utilizes the predictive power of Long short-term memory (LSTM) and Convolutional Neural Network (CNN) for an efficient and timely detection of multi-vector threats and attacks. A current state of the art dataset CICIDS2017 and standard performance evaluation metrics have been employed to thoroughly evaluate the proposed mechanism. We rigorously compared our proposed technique with our constructed hybrid DL-architectures and current benchmark algorithms. Our analysis shows that the proposed approach out-performs in terms of detection accuracy with a trivial trade-off speed efficiency. We also performed a 10-fold cross validation to explicitly show unbiased results.

Keywords

• Security
• hybrid deep learning model
• software defined networks
• long short-term memory
• convolutional neural network