垃圾邮件发送者行为建模：朴素贝叶斯与人工神经网络
Modeling Spammer Behavior: Naïve Bayes vs. Artificial Neural Networks
摘要：
针对互联网上的垃圾邮件问题，本文提出了一种基于朴素贝叶斯和人工神经网络（ANN）的垃圾邮件行为建模的比较研究。基于关键字的垃圾邮件过滤技术不足以模拟垃圾邮件发送者的行为，因为垃圾邮件发送者不断改变策略来绕过这些过滤器。垃圾邮件发送者使用的规避策略本身就是可以用来对抗垃圾邮件的模式。据观察，朴素贝叶斯和人工神经网络都是最适合建模垃圾邮件发送者的常见模式。实验结果表明，这两种方法都达到了92%左右的检测率，这是一个相当大的改进性能相比，基于关键字的当代过滤方法。
英文摘要：
Addressing the problem of spam emails in the Internet, this paper presents a comparative study on Naïve Bayes and Artificial Neural Networks (ANN) based modeling of spammer behavior. Keyword-based spam email filtering techniques fall short to model spammer behavior as the spammer constantly changes tactics to circumvent these filters. The evasive tactics that the spammer uses are themselves patterns that can be modeled to combat spam. It has been observed that both Naïve Bayes and ANN are best suitable for modeling spammer common patterns. Experimental results demonstrate that both of them achieve a promising detection rate of around 92%, which is considerably an improvement of performance compared to the keyword-based contemporary filtering approaches.
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。
