目前基于深度学习的网络异常检测是入侵检测领域新的研究方向，但是大部分研究都是利用数据挖掘处理后的特征数据进行特征学习和分类。该文利用UNSW-NB15作为主要研究数据集，利用独热编码对数据集中的原始网络包进行编码，维度重构后形成二维数据，并利用GoogLeNet网络进行特征提取学习，最后训练分类器模型进行检测。实验结果表明：该方法能有效处理原始网络包并进行网络攻击检测，检测精度达到99%以上，高于基于特征数据进行的深度学习检测方法。
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.
关键词
网络异常检测 /
卷积神经网络(CNN) /
独热编码 /
UNSW-NB15数据集
Key words
anomaly detection /
convolutional neural network /
one-hot encoding /
UNSW-NB15 dataset
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参考文献
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基金
国家自然科学基金资助项目（U1536119）
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