IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning
作者：
Highlights：
• IoT feature extraction and intrusion detection algorithm for intelligent city based on deep migration learning model is proposed.
• The deep learning model is enhanced and optimized to be fit for the smart city scenarios.
• Parameter transfer mines model parameters model is designed to establish connection between target task and source task.
• Feature transfer focuses on finding a common feature representation implicit in source and target domain feature spaces.
• The proposed model is tested on pubic massive complex data sets to validate the robustness.
摘要
•IoT feature extraction and intrusion detection algorithm for intelligent city based on deep migration learning model is proposed.•The deep learning model is enhanced and optimized to be fit for the smart city scenarios.•Parameter transfer mines model parameters model is designed to establish connection between target task and source task.•Feature transfer focuses on finding a common feature representation implicit in source and target domain feature spaces.•The proposed model is tested on pubic massive complex data sets to validate the robustness.
论文关键词：Deep learning,Migration learning model,Sensor network,Smart City,Internet of things,Information feature extraction,Intrusion detection, machine learning
论文评审过程：Received 29 October 2018, Revised 31 March 2019, Accepted 12 April 2019, Available online 10 May 2019, Version of Record 11 October 2019.
论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2019.04.006
