摘要
针对电子邮件病毒危害大和更新速度快的特点,以及传统的使用手工编制的启发知识在新病毒上的软弱,研究了数据挖掘的归纳学习和分类方法,利用数据挖掘方法产生新邮件病毒的知识来解决传统方法的缺陷。通过贝叶斯方法建立了检测知识的识别模型和自学习模型,构建了检测系统的结构框架,并作出了深入的讨论。
Aiming at the characteristics that E-mail virus is badly harmful and its renewal speed is highly quick, and traditionally using the heuristic knowledge that is compiled manually has the weakness in detecting new virus, the paper studies the induction learning and sorting method of data mining and takes advantage of data mining methods to generate the detection knowledge of new E-Mail virus, which can resolve the defect of traditional method. In this paper, the recognition model and self-learning model of detection knowledge have been built by bayes method, and the structural frame of the detecting system has been constructed, and makes some detailed discussions.
出处
《机械管理开发》
2005年第6期70-72,共3页
Mechanical Management and Development
