Cyber Attack Detection by Using Neural Network Approaches: Shallow Neural Network, Deep Neural Network and AutoEncoder

ÜSTEBAY S. , Turgut Z. , AYDIN M.

26th International Conference on Computer Networks (CN), Kamien Slaski, Poland, 25 - 27 June 2019, vol.1039, pp.144-155 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 1039
  • Doi Number: 10.1007/978-3-030-21952-9_11
  • City: Kamien Slaski
  • Country: Poland
  • Page Numbers: pp.144-155
  • Keywords: Shallow Neural Network, Auto Encoder, Deep Neural Network, IDS, Cyberattack

Abstract

As the accuracy rate of artificial intelligence based applications increased, they have started to be used in different areas. Artifical Neural Networks (ANN) can be very successful for extracting meaningful data from features by processing complex data. Well-trained models can solve difficult problems with high a high accuracy rate. In this study, 2 different ANN models have been developed to detect malicious users who want to access high-security servers. These models are tested from simple to complex: Shallow Neural Network (SNN), Deep Neural Network (DNN), and Auto Encoder are used to reduce features. All models are trained with CICIDS2017 dataset. Server connection requests are classified as normal or malicious (Brute Force, Web Attack, In ltration, Botnet or DDoS) with 98.45% accuracy rate.