对反垃圾邮件行为识别技术进行了研究.提出了一种基于会话层的垃圾邮件识别方法,在分析发送过程中的邮件行为特征基础上,提取出能够区分垃圾邮件和正常邮件的行为特征,并采用支持向量机分类算法建立行为特征识别模型,找出垃圾邮件行为规律.该方法在邮件正文发送之前对垃圾邮件进行过滤,能够有效地节省带宽.采用真实的邮件数据集合分别使用行为识别技术与基于内容的过滤技术进行实验,验证该技术具有较好的邮件分类能力.%Anti-spam behavior recognition technology was studied. Introduce a method of spam behavior recognition based on the session layer was proposed. Based on analyzing behavioral characteristics of email data, the behavioral characteristics which are able to distinguish between spam and normal mail were extracted. Then establish behavioral characteristics recognition model using support vector machine algorithm. The rule of the spam behavior was found. This method filters the spam before the content of spam have been sent, saves the bandwidth effectively. Finally, experimenl used behavior recognition technology and content based filtering on a real mail data set shows behavior recognition technology provides a better result for spam classification.
展开▼
