New Research Progress on Intrusion Detection Techniques for the Internet of Things
Compared to traditional intrusion detection mechanisms,the intelligent intrusion detection technology can fully extract data features,demonstrating higher detection efficiency,however,it also imposes greater demands on data sample labels.Considering data sample labels,this article provided a comprehensive review of the latest developments in the intrusion detection technology for the Internet of things(IoT)from the perspectives of supervised and unsupervised learning.Firstly,it outlined signature-based intrusion detection methods and analyzed recent traditional machine learning based intrusion detection methods based on the classification of supervised and unsupervised learning.Then,it analyzed recent deep learning based intrusion detection methods based on supervised,unsupervised,generative adversarial network,and deep reinforcement learning,respectively.Finally,it summarized the research challenges and future trends in the IoT intrusion detection technology.
Internet of thingsintrusion detectionmachine learningdeep learninggenerative adversarial network
NETL
NSTL
万方数据
维普
