A1 Journal article – refereed
Anomaly-based Intrusion Detection Using Deep Neural Networks

List of Authors: Farahnakian Fahimeh, Heikkonen Jukka
Publisher: Advanced Institute of Convergence Information Technology
Publication year: 2018
Journal: International Journal of Digital Content Technology and Its Applications
Volume number: 12
Issue number: 3
eISSN: 2233-9310


Identification of network attacks is a matter of great concern for network operators due to extensive the number of vulnerabilities in computer systems and creativity of the attackers. Anomaly-based Intrusion Detection Systems (IDSs) present a significant opportunity to identify possible incidents, logging information and reporting attempts. However, these systems generate a low detection accuracy rate with changing network environment or services. To overcome this problem, we present a deep neural network architecture based on a combination of a stacked denoising autoencoder and a softmax classifier. Our architecture can extract important features from data and learn a model for detecting abnormal behaviors. The model is trained locally to denoise corrupted versions of their inputs based on stacking layers of denoising autoencoders in order to achieve reliable intrusion detection. Experimental results on real KDD-CUP'99 dataset show that our architecture outperformed shallow learning architectures and other deep neural network architectures.

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Last updated on 2019-20-07 at 07:27