基于免疫克隆选择算法的垃圾网页检测
发布时间：2018-12-25 21:12
【摘要】：垃圾网页是指一些网页通过不正当的手段来误导搜索引擎,使网页获得高于其应有的排名,从而获得更多的访问量。它不仅降低了网页的质量,同时也导致了严重的Web信息安全问题。传统的垃圾网页检测通常使用经典的机器学习方法包括贝叶斯算法、SVM、C4.5等,这些算法对垃圾网页的检测有一定的效果。在前人的研究基础上提出一种基于免疫克隆选择的垃圾网页检测方法。利用人工免疫系统的自学习及自适应能力来检测利用新作弊技术的垃圾网页,并与广泛用于垃圾网页检测的贝叶斯算法对比。实验表明该方法能有效、可靠地检测出垃圾网页。
[Abstract]:Spam page refers to some pages through improper means to mislead the search engine, so that the page gets higher than it should rank, so as to get more visitors. It not only reduces the quality of web pages, but also leads to serious Web information security problems. Classical machine learning methods such as Bayesian algorithm, SVM,C4.5 and so on are usually used in the traditional spam detection. These algorithms have a certain effect on the detection of garbage pages. On the basis of previous studies, a method of spam page detection based on immune clone selection is proposed. The self-learning and adaptive ability of the artificial immune system is used to detect spam pages using the new cheating technology and compared with Bayesian algorithm which is widely used in spam detection. Experiments show that the method is effective and reliable in detecting garbage pages.
【作者单位】： 西南交通大学信息科学与技术学院;
【基金】：四川省学术带头人培养基金项目(x8000912371309)
【分类号】：TP393.092
[Abstract]:Spam page refers to some pages through improper means to mislead the search engine, so that the page gets higher than it should rank, so as to get more visitors. It not only reduces the quality of web pages, but also leads to serious Web information security problems. Classical machine learning methods such as Bayesian algorithm, SVM,C4.5 and so on are usually used in the traditional spam detection. These algorithms have a certain effect on the detection of garbage pages. On the basis of previous studies, a method of spam page detection based on immune clone selection is proposed. The self-learning and adaptive ability of the artificial immune system is used to detect spam pages using the new cheating technology and compared with Bayesian algorithm which is widely used in spam detection. Experiments show that the method is effective and reliable in detecting garbage pages.
【作者单位】： 西南交通大学信息科学与技术学院;
【基金】：四川省学术带头人培养基金项目(x8000912371309)
【分类号】：TP393.092
【参考文献】
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1 周茜,赵明生,扈e，
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