Performance analysis of flow-based attacks detection on CSE-CIC-IDS2018 dataset using deep learning

Abstract

The emergence of the internet of things (IOT) as a result of the development of the communications system has made the study of cyber security more important. Day after day, attacks evolve and new attacks are emerged. Hence, network anomaly-based intrusion detection system is become very important, which plays an important role in protecting the network through early detection of attacks. Because of the development in  machine learning and the emergence of deep learning field,  and its ability to extract high-level features with high accuracy, these systems have been included to work with real network traffic CSE-CIC-IDS2018 for a wide range of intrusions and normal behavior as an ideal method of testing and evaluation. In this paper, we test and evaluate our deep model (DNN) which has achieved a good detection accuracy of about 90%.