Distributed denial of service attack detection using autoencoder and deep neural networks

Article type: Research Article

Authors: Catak, Ferhat Ozgur^{a; *} | Mustacoglu, Ahmet Fatih^b

Affiliations: [a] TUBITAK-BILGEM Cyber Security Institute, Kocaeli, Turkey | [b] Istanbul Sehir University, Cyber Security Engineering, Istanbul, Turkey

Correspondence: [*] Corresponding author. Ferhat Ozgur Catak, TUBITAK-BILGEM Cyber Security Institute, 41400 Kocaeli, Turkey. E-mail: [email protected].

Abstract: Today, many companies are faced with the huge network traffics mainly consisting of the various type of network attacks due to the increased usage of the botnet, fuzzier, shellcode or network related vulnerabilities. These types of attacks are having a negative impact on the organization because they block the day-to-day operations. By using the classification models, the attacks could be identified and separated earlier. The Distributed Denial of Service Attacks (DDoS) primarily focus on preventing or reducing the availability of a service to innocent users. In this research, we focused primarily on the classification of network traffics based on the deep learning methods and technologies for network flow models. In order to increase the classification performance of a model that is based on the deep neural networks has been used. The model used in this research for the classification of network traffics evaluated and the related metrics showing the classification performance have been depicted in the figures and tables. As the results indicate, the proposed model can perform well enough for detecting DDoS attacks through deep learning technologies.

Keywords: cyber security, ddos, deep learning, autoencoder

DOI: 10.3233/JIFS-190159

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 3, pp. 3969-3979, 2019