精品文档---下载后可任意编辑伪装型垃圾网页检测技术的讨论与实现的开题报告【摘要】随着互联网的快速进展，我们的生活中越来越多的信息来源于网络。但与此同时，网络中存在大量的垃圾网页，它们不仅占用了宝贵的网络资源，还给用户带来了不便和危害。其中，伪装型垃圾网页是一种比较难以检测的垃圾网页，本文讨论了伪装型垃圾网页检测技术的方法和实现。本文首先介绍了伪装型垃圾网页的定义和分类。然后，详细阐述了常见的伪装手段和检测方法，包括 HTML 特征、文本特征、链接特征等，并指出了各种检测方法的优缺点。在此基础上，本文提出了一种基于机器学习的伪装型垃圾网页检测方法。该方法首先对网页进行特征提取，然后使用支持向量机（SVM）分类器进行分类。实验结果表明，该方法具有较高的准确率和鲁棒性。最后，本文对未来的讨论方向进行了展望，并总结了本文的贡献和不足之处。【关键词】伪装型垃圾网页；特征提取；机器学习；分类器；支持向量机【Abstract】With the rapid development of the Internet, more and more information in our lives comes from the network. However, at the same time, there are a large number of junk web pages on the network, which not only occupy valuable network resources, but also bring inconvenience and harm to users. Among them, disguised junk web pages are a type of junk web pages that are difficult to detect. This paper studies the methods and implementations of disguised junk web page detection technology.This paper first introduces the definition and classification of disguised junk web pages. Then, it elaborates on common disguising methods and detection methods, including HTML features, text features, link features, etc., and points out the advantages and disadvantages of various detection methods.Based on this, this paper proposes a machine learning-based disguised junk web page detection method. The method first extracts features from the web page and then uses a 精品文档---下载后可任意编辑support vector machine (SVM) classifier for classification. The experimental results show that the method has high accuracy and robustness.Finally, this paper looks forward to future research directions and summarizes the contributions and shortcomings of this paper.【Keywords】Disguised junk web page; feature extraction; machine learning; classifier; support vector machine
