One-hot encoding and convolutional neural network based anomaly detection

被引：0
|
作者：
Liang J. [1 ]
Chen J. [2 ]
Zhang X. [2 ]
Zhou Y. [2 ]
Lin J. [2 ]
机构：
[1] China Information Security Certification Center, Beijing
[2] College of Information Science and Engineering, East China University of Science and Technology, Shanghai
来源：
Qinghua Daxue Xuebao/Journal of Tsinghua University | 2019年 / 59卷 / 07期
关键词：
Anomaly detection; Convolutional neural network; One-hot encoding; UNSW-NB15; dataset;
D O I：
10.16511/j.cnki.qhdxxb.2018.25.061
中图分类号：
学科分类号：
摘要：
Deep learning based network anomaly detection is a new research field with previous studies using preprocessed datasets based on data mining or other methods. This paper transforms and encodes the UNSW-NB15 dataset using one-hot encoding to a two-dimensional dataset. Then, GoogLeNet is used for deep learning network to extract the features and train the classifier. Tests show that this method can effectively process the original network packet with a classification accuracy over 99%, which is much higher than deep learning detection methods based on preprocessed data. © 2019, Tsinghua University Press. All right reserved.
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收藏
页码：523 / 529
页数：6
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