CT-T: Collaborative Research: Adaptive Attacks and Defenses in Denial of Information

垃圾邮件已经成为每一种重要通信媒介中的突出问题。大多数电子邮件用户每天都要面对垃圾邮件。整个行业都在“提高”网站在搜索引擎上的排名。此外，自动生成的垃圾邮件侵入了博客、社交网络、在线广告和VoIP连接。垃圾邮件是一个迅速增长的实际问题，因为攻击工具很容易适应绕过防御机制。例如，有用的防御技术，如统计学习过滤器和协同过滤，能够区分垃圾邮件和合法电子邮件。然而，攻击者一直在使用自动化工具绕过这些防御机制，导致攻击和防御之间似乎无休止的“军备竞赛”。例如，随机化垃圾邮件令牌并插入合法文本作为伪装会显著降低统计学习过滤器的有效性。虽然没有已知的军备竞赛的通用解决方案，称为对抗性学习，但基于利用垃圾邮件的语义必要性来包含强垃圾邮件令牌（如伟哥（或其拼写错误））的防御已经被发现并证明可以结束伪装军备竞赛。本项目寻求更多的证据来支持这样的假设，即可以找到这种结构（固有）特征，并将其用于识别多种垃圾邮件攻击。第一个研究重点是开发适应垃圾邮件攻击的防御方法。第二个研究重点是调查不同领域（例如，电子邮件和网络垃圾邮件）的垃圾邮件攻击组合和联合防御。这些攻击的成功将显著且永久地降低垃圾邮件攻击的有效性。

Spam has become a prominent problem in every important communications medium. Most email users face spam every day. An entire industry has sprung to "improve" search engine rankings of web sites. Further, automatically generated spam has invaded blogs, social networks, online advertising, and VoIP connections. Spam is a rapidly growing practical problem due to the easy adaptation of attacking tools that bypass defense mechanisms. For example, useful defense techniques such as statistical learning filters and collaborative filtering are capable of distinguishing spam from legitimate email. However, attackers have been using automated tools to bypass these defense mechanisms, resulting in a seemingly endless "arms race" between attacks and defenses. For example, randomizing spam tokens and inserting legitimate text as camouflage can significantly reduce the effectiveness of statistical learning filters. Although there is no known general solution for the arms race, known as Adversarial Learning, a defense based on the exploitation of the semantic necessity of spam email to contain strong spam tokens such as VIAGRA (or its misspellings) has been found and demonstrated to end the camouflage arms race. This project seeks additional evidence to support the hypothesis that such structural (inherent) characteristics can be found and used in the identification of many kinds of spam attacks. The first research thrust focuses on the development of defense methods resilient to adaptive spam attacks. The second research thrust investigates the combination of spam attacks in distinct areas (e.g., email and web spam) and combined defenses. Success in these thrusts will significantly and permanently reduce the effectiveness of spam attacks.