INTRUSION DETECTION METHOD BASED ON DEEP AUTOENCODER AND EXTENDED ISOLATED FOREST IN FOG ENVIRONMENT
Aimed at the variability of intrusion behavior in the Internet of things,a hybrid intrusion detection method based on deep autoencoder and extended isolated forest is proposed for fog computing mode.The autoencoder based on one-dimensional convolutional neural network(1 D-CNN)was used to detect the network traffic data collected by fog nodes,and the attack and normal traffic data were divided into two groups.The extended isolated forest algorithm was used to detect the anomaly of attack traffic and normal traffic,and try to identify the mismatched data points in attack group and normal group,so as to improve the overall detection accuracy and reduce the false alarm rate of the proposed method.Compared with other intrusion detection methods,the proposed method achieves the best results among multiple indicators,and can effectively identify rapidly evolving network attacks.
Fog computingDeep autoencoderExtended isolated forestIntrusion detection method
万方数据
维普
