A Deep Neural Network Architecture for Intrusion Detection in Software-Defined Networks

For more comprehensive security of a computer network as well as the use of firewall and anti-virus security equipment, intrusion detection systems (IDSs) are needed to detect the malicious activity of intruders. Therefore, the introduction of a high-precision intrusion detection system is critical for the network. Generally, the general framework of the proposed intrusion detection models is the use of text classification, and today deep neural networks (DNNs) are one of the top classifiers. A variety of DNN-based intrusion detection models have been proposed for software-defined networks (SDNs); however, these methods often report performance metrics solely on one well-known dataset. In this paper, we present a DNN-based IDS model with a ۱۲-layer arrangement which works well on three datasets, namely, NSL-KDD, KDD۹۹, and UNSW-NB۱۵. The layered layout of the proposed model is considered the same for all the three datasets, which is one of the strengths of the proposed model. To evaluate the proposed solution, six other DNN-based IDS models have been designed. The values of the evaluation metrics, including accuracy, precision, recall, F-measure, and loss function, show the superiority of the proposed model over these six models. In addition, the proposed model is compared with several recent articles in this field, and the superiority of the proposed solution is shown.

Keywords:

Intrusion detection , Software-defined network , deep Learning , Network security