基于链接分析和规则分类的恶意网站识别技术研究

Malicious Websites Identification based on Hyperlink Analysis and Classification Rule

采用多源链接分析指标,构建了基于链接分析和规则分类技术的恶意网站识别模型。通过分析四种规则分类技术的识别性能和识别规则,并与四种传统的机器学习分类技术进行对比,验证所构建模型的有效性。在所提取的识别规则中,来自Alexa和Moz平台上的网站链接指标在恶意网站识别中有重要作用;与传统的机器学习分类技术相比,基于链接分析和规则分类的识别模型不仅能提取出多组易于理解的恶意网站识别规则,还具有更好的识别性能。本研究不仅拓展了链接分析在恶意网站识别中的应用,有效提升了恶意网站识别的准确性,还提取出易于理解的恶意网站识别规则。

With multi-source hyperlink indices,this study proposes a malicious websites identification model based on hyperlink analysis and classification rule.The performance and associative rules of four types of classification rule in identifying malicious websites are analyzed by comparing with four typical machine learning classifiers.By analyzing the extracted rules,the hyperlink indices from Alexa and Moz play an important role for the malicious website identification.Compared with four typical machine learning classifiers,the proposed identification model not only extracts a group of identification rules for malicious websites,but also has better performance in identifying malicious websites.This study can not only expand the use of hyperlink analysis in the area of malicious websites identification,but also build an efficient model and extract easy-to-understand rules in identifying malicious websites.