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International Conference of Pioneering Computer Scientists, Engineers and Educators

ICPCSEE 2018: Data Science pp 182–191Cite as

Research on Network Intrusion Data Based on KNN and Feature Extraction Algorithm

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 901))

Abstract

In order to solve the problem of high data dimension in network intrusion detection, the paper proposes KNN classifier and two kinds of effective feature selection algorithms that include Automatic encoder (Autoencoder) and Principal Component Analysis (PCA). The algorithms that the paper proposes will be applied in the field of network intrusion detection. First of all, we combine the KNN classifier with the effective feature selection algorithms to form a novel intrusion detection model. Secondly, we input the preprocessed data into the model. Finally, the experimental results show that the combination of KNN classifier and Autoencoder (KNN-Autoencoder) makes the accuracy of intrusion detection reach 93%, and the combination of KNN classifier and PCA (KNN-PCA) makes the accuracy of intrusion detection reach 91%. Apparently, the efficient feature selection algorithms can effectively reduce the impact of non-related attributes on the classification results. The performance of the automatic encoder is better than the principal component analysis. All experiments are done based on the KDD UP99 data set.

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ACKNOWLEDGEMENT

This work was supported by Shandong provincial Natural Science Foundation, China (************) and by Key Research and Development Plan of Shandong Province (*************).

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Authors and Affiliations

  1. School of Information, Qilu University of Technology, Shandong, China

    Shuai Dong & Xingang Wang

Authors
  1. Shuai Dong
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  2. Xingang Wang
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Corresponding authors

Correspondence to Shuai Dong or Xingang Wang .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, Henan, China

    Qinglei Zhou

  2. Zhengzhou University of Light Industry, Zhengzhou, Henan, China

    Yong Gan

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. Zhengzhou Institute of Technology, Zhengzhou, China

    Yan Wang

  6. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Cite this paper

Dong, S., Wang, X. (2018). Research on Network Intrusion Data Based on KNN and Feature Extraction Algorithm. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_14

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  • DOI: https://doi.org/10.1007/978-981-13-2203-7_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2202-0

  • Online ISBN: 978-981-13-2203-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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