Abstract
AbstractMobile smart devices and mobile applications carry a lot of personal information and office entertainment functions. By means of analyzing the network traffic generated by mobile applications during use, it can provide valuable information in terms of network management, privacy protection, and behavior recognition. This paper designs a recognition model based on dual-domain attention mechanism and meta-learning. First, feature extraction is performed through a deep separable convolution module, and secondly, attention is extracted from the channel and space dimensions through the attention mechanism module to enhance behavior recognition samples. At the same time, the meta-learning strategy is used for multi-task learning, so that the model can have a faster and more efficient recognition effect when facing new small sample recognition tasks. Experimental results show that compared with other small sample recognition models, the model in this paper can effectively recognize mobile application behavior.KeywordsMobile application behavior recognitionDeep learningAttention mechanismMeta-learningDeep separable convolutionSmall sample classification
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
