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DeepNet: A Deep Learning Architecture for Network-Based Anomaly Detection

  • Javad Zabihi
  • Vandana JanejaEmail author
Conference paper
  • 8 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11878)

Abstract

Anomaly detection has been one of the most interesting research areas in the field of cybersecurity. Supervised anomaly detection systems have not been practical and effective enough in real-world scenarios. As a result, different unsupervised anomaly detection pipelines have gained more attention due to their effectiveness. Autoencoders are one of the most powerful unsupervised approaches which can be used to analyze complex and large-scale datasets. This study proposes a method called DeepNet, which investigates the potential of adopting an unsupervised deep learning approach by proposing an autoencoder architecture to detect network intrusion. An autoencoder approach is implemented on network-based data while taking different architectures into account. We provide a comprehensive comparison of the effectiveness of different schemes. Due to the unique methodology of autoencoders, specific methods have been suggested to evaluate the performance of proposed models. The results of this study can be used as a foundation to build a robust anomaly detection system with an unsupervised approach.

Keywords

Anomaly detection Deep learning Autoencoder 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Information Systems DepartmentUniversity of MarylandBaltimore CountyUSA

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