中文摘要：

提出了一种自学习的轻量级网页分类方法SLW.SLW首次引入了访问关系的概念,使其具有反馈和自学习的特点.SLW从已有的恶意网页集合出发,自动发现可信度低的用户和对应的访问关系,从而进一步利用低可信度用户对其他网页的访问关系来发现未知的恶意网址集合.实验结果表明,在相同数据集上,相比于传统检测方法,SLW方法可以显著提高恶意网页检测效果,大幅降低平均检测时间.

英文摘要：

A self-learning light-wight （SLW） is proposed.SLW is the first to introduce access relations and have the characteristics of feedback and self-learning.SLW approach starts from the seed set which includes known malicious pages.Then,it automatically figures out users with low credibility based on the seed set and the visit relation database.Finally,the access records of these users are used to identify other malicious pages.Experimental results indicate that SLW approach can significantly improve the efficiency of malicious pages detection and reduce the average detection time compared with other conventional methods.