基于组合神经网络的物联网入侵检测方法

Intrusion detection method of Internet of Thingsbased on combined neural network

针对物联网流量入侵检测的全局特征提取问题,对现有的网络入侵检测方法进行了改进,提出了一种基于组合神经网络的入侵检测方法。首先利用一维密集连接卷积神经网络对数据集中流量的空间特征进行提取;然后利用门控循环神经单元进一步提取时序特征,完成对物联网流量数据的时空特征提取;最后采用UNSW-NB15和Bot-iot数据集对组合神经网络模型进行多分类训练和测试。实验结果表明,所提方法在准确率以及其他评价指标方面均有一定的提高,表明了该方法的有效性。

Aiming at the global feature extraction problem of traffic data of Internet of Things,the existing methods for network intrusion detection are improved,and an intrusion detection method based on combined neural network is proposed.Firstly,the spatial features of the traffic data in the data set are extracted by one-dimensional densely connected convolutional neural network,and then the gated recurrent unit is used to further extracts time series features to complete the spatiotemporal feature extraction of traffic data in Internet of Things.Finally,the UNSW-NB15 and Bot-iot datasets are used for multi-class training and testing of the combined neural network model.The experimental results show that the proposed method has certain improvement in the accuracy and other evaluation indicators,which shows the effectiveness of the method.