Network Anomaly Detection with Stochastically Improved Autoencoder Based Models


AYGÜN R. C. , YAVUZ A. G.

4th IEEE International Conference on Cyber Security and Cloud Computing, CSCloud 2017 and 3rd IEEE International Conference of Scalable and Smart Cloud, SSC 2017, New-York, United States Of America, 26 - 28 June 2017, pp.193-198 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/cscloud.2017.39
  • City: New-York
  • Country: United States Of America
  • Page Numbers: pp.193-198

Abstract

Intrusion detection systems do not perform well when it comes to detecting zero-day attacks, therefore improving their performance in that regard is an active research topic. In this study, to detect zero-day attacks with high accuracy, we proposed two deep learning based anomaly detection models using autoencoder and denoising autoencoder respectively. The key factor that directly affects the accuracy of the proposed models is the threshold value which was determined using a stochastic approach rather than the approaches available in the current literature. The proposed models were tested using the KDDTest+ dataset contained in NSL-KDD, and we achieved an accuracy of 88.28% and 88.65% respectively. The obtained results show that, as a singular model, our proposed anomaly detection models outperform any other singular anomaly detection methods and they perform almost the same as the newly suggested hybrid anomaly detection models.