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WEDL-NIDS: Improving Network Intrusion Detection Using Word Embedding-Based Deep Learning Method

  • Jianjing Cui
  • Jun Long
  • Erxue Min
  • Yugang Mao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11144)

Abstract

A Network Intrusion Detection System (NIDS) helps system administrators to detect security breaches in their organization. Current research focus on machine learning based network intrusion detection method. However, as numerous complicated attack types have growingly appeared and evolved in recent years, obtaining high detection rates is increasingly difficult. Also, the performance of a NIDS is highly dependent on feature design, while a feature set that can accurately characterize network traffic is still manually designed and usually costs lots of time. In this paper, we propose an improved NIDS using word embedding-based deep learning (WEDL-NIDS), which has the ability of dimension reduction and learning features from data with sophisticated structure. The experimental results show that the proposed method outperforms previous methods in terms of accuracy and false alarm rate, which successfully demonstrates its effectiveness in both dimension reduction and practical detection ability.

Keywords

Network intrusion detection Deep neural networks Word embedding Long short-term memory networks 

Notes

Acknowledgement

This research work is supported by National Natural Science Foundation of China under grant number 61105050.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceNational University of Defense TechnologyChangshaChina

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