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Analysis of LSTM-RNN Based on Attack Type of KDD-99 Dataset

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Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11063))

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Abstract

Method and model of machine learning have applied to many industry fields. Employing RNN to detect and recognize network events and intrusions is extensively studied. This paper divides KDD-99 dataset into 4 subsets according to data item’s ‘attack type’ field. And then, LSTM-RNN is trained and verified on each subset in order to optimize model parameters. Experiments show the strategy of training for LSTM-RNN could boost model accuracy.

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Acknowledgment

This paper is supported by the National Natural Science Foundation of China under Grant No. 61572153 and the National Key research and Development Plan (Grant No. 2018YFB0803504).

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Authors and Affiliations

  1. Institute of Computer Application, China Academy of Engineer Physics, Beijing, China

    Chaochao Luo

  2. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

    Le Wang & Hui Lu

Authors
  1. Chaochao Luo
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  2. Le Wang
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  3. Hui Lu
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Corresponding author

Correspondence to Le Wang .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Luo, C., Wang, L., Lu, H. (2018). Analysis of LSTM-RNN Based on Attack Type of KDD-99 Dataset. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_29

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  • DOI: https://doi.org/10.1007/978-3-030-00006-6_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00005-9

  • Online ISBN: 978-3-030-00006-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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