Research Article
Research on Text Communication Security Based on Deep Learning Model
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@INPROCEEDINGS{10.1007/978-3-031-04409-0_14, author={Guanghua Yu and Wanjuan Cong}, title={Research on Text Communication Security Based on Deep Learning Model}, proceedings={Machine Learning and Intelligent Communications. 6th EAI International Conference, MLICOM 2021, Virtual Event, November 2021, Proceedings}, proceedings_a={MLICOM}, year={2022}, month={5}, keywords={Spam message Naive Bayesian Model}, doi={10.1007/978-3-031-04409-0_14} }- Guanghua Yu
Wanjuan Cong
Year: 2022
Research on Text Communication Security Based on Deep Learning Model
MLICOM
Springer
DOI: 10.1007/978-3-031-04409-0_14
Abstract
In response to the current spam flooding problem, this paper uses Python language machine learning and natural language processing technology to study the identification classification of spam messages. The Jieba algorithm is used to distinguish the Chinese word, and the TF-IDF algorithm is used to conduct feature extraction. On the basis of the analysis of the classifier algorithm, the experimental data is finalized. The results show that the classification effect of the polynomial plain Bayes classifier is optimal, and the identification of garbage text is best optimized.
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