University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study

Ferrag, Mohamed Amine, Maglaras, Leandros, Moschoyiannis, Sotiris and Janicke, Helge (2019) Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study Journal of Information Security and Applications.

[img] Text
Deep_Learning_for_Cyber_Security_Intrusion_Detection__Approaches__Datasets__and_Comparative_Study.pdf - Accepted version Manuscript
Restricted to Repository staff only

Download (300kB)


In this paper, we present a survey of deep learning approaches for cybersecurity intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines, and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Ferrag, Mohamed Amine
Maglaras, Leandros
Janicke, Helge
Date : 2019
Copyright Disclaimer : © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Related URLs :
Depositing User : Clive Harris
Date Deposited : 18 Nov 2019 14:17
Last Modified : 19 Nov 2019 14:43

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800