垃圾邮件行为建模的机器学习方法
Machine Learning Approaches for Modeling Spammer Behavior
摘要：
垃圾邮件通常被称为未经请求或不需要的电子邮件信息在互联网上造成潜在的威胁，互联网安全。用户花费大量宝贵的时间来删除垃圾邮件。更重要的是，不断增加的垃圾邮件占用服务器存储空间，消耗网络带宽。基于关键字的垃圾邮件过滤策略最终将不太成功，因为垃圾邮件发送者不断改变他们的技巧来绕过这些过滤器。垃圾邮件发送者使用的规避策略是模式，可以对这些模式进行建模以打击垃圾邮件。本文研究了利用朴素贝叶斯分类器、决策树归纳法和支持向量机等著名分类算法对垃圾邮件发送者行为模式建模的可能性。初步的实验结果表明，一个有前途的检测率约为92%，这是一个相当大的性能增强相比，类似的垃圾邮件行为建模研究。
英文摘要：
Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Naïve Bayesian classifier (Naïve Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
