| COMPUTER SCIENCE AND TECHNOLOGY |
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One-hot encoding and convolutional neural network based anomaly detection |
| LIANG Jie1, CHEN Jiahao2, ZHANG Xueqin2, ZHOU Yue2, LIN Jiajun2 |
1. China Information Security Certification Center, Beijing 100085, China;
2. College of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China |
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Abstract 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.
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| Keywords
anomaly detection
convolutional neural network
one-hot encoding
UNSW-NB15 dataset
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Issue Date: 21 June 2019
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2019,
Vol. 59
