基于深度学习的浏览器记录分析研究

Haomin Pang, Zhaoxu Wu, Haibo Luo, Biwu Yi
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引用次数: 0

摘要

随着互联网打击黑灰生产等犯罪活动的蓬勃发展,数据分类问题逐渐受到重视。因此,通过对已获取的浏览器历史记录进行建模和分析,利用神经语言编程(NLP)领域的中文分词技术进行分词,利用词汇表模型进行特征提取,利用神经网络算法进行分类处理。通过特征提取和神经网络对浏览器历史数据进行仿真实验,训练模型对浏览器历史记录分析和测试数据分类的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning-based browser record analysis research
With the vigorous development of the Internet to combat criminal activities such as black and gray production, the problem of data classification is gradually being taken seriously. Therefore, by modeling and analyzing the browser history records that have been acquired, in which Chinese word separation in the field of neuro-linguistic programming (NLP) is used for word separation, feature extraction using a vocabulary table model, and classification processing by a neural network algorithm. Simulation experiments on browser history data through feature extraction and neural networks are conducted to train the accuracy of the model for analyzing browser history records and classifying the test data.
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