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A Network Intrusion Detection Model Based on Convolutional Neural Network

  • Wenwei TaoEmail author
  • Wenzhe Zhang
  • Chao Hu
  • Chaohui Hu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)

Abstract

Intrusion detection is an important research direction in the field of power monitoring network security. The increase of data volume and the diversification of intrusion modes make the traditional detection methods unable to meet the requirements of the current network environment. The emergence of convolutional neural network provides a new way to solve this dilemma. An intrusion detection model based on convolutional neural network is proposed in this paper. The method that converts the flow data into an image is used to represent the flow data in the form of a grayscale image, and use the texture representation in the image to classify the intrusion modes. Through the conversion of traffic data to images, the intrusion detection problem is transformed into image recognition problem, which substitute convolutional neural network technology into the intrusion detection problem. Firstly, the intrusion data set KDD 99 is preprocessed, and generate a two-dimensional image matrix group that meets the requirements. Then, the appropriate model structure for training is selected through comparison experiments. Finally, comparing the trained model with the other machine learning methods is to verify the model about reliability and effectiveness.

Keywords

Convolutional neural network Image matrix group Intrusion detection 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.China Southern Power Grid Co., Ltd.GuangzhouChina
  2. 2.NARI Information & Communication Technology Co., Ltd.NanjingChina
  3. 3.Dingxin Information Technology Co., Ltd.GuangzhouChina

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