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Secure learning and learning for security: Research in the intersection.

机译：安全学习和安全学习：交叉路口的研究。

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The first part of this dissertation considers Machine Learning under the lens of Computer Security, where the goal is to learn in the presence of an adversary. Two large case-studies on email spam filtering and network-wide anomaly detection explore adversaries that manipulate a learner by poisoning its training data. In the first study, the False Positive Rate (FPR) of an open-source spam filter is increased to 40% by feeding the filter a training set made up of 99% regular legitimate and spam messages, and 1% dictionary attack spam messages containing legitimate words. By increasing the FPR the adversary affects a Denial of Service attack on the filter. In the second case-study, the False Negative Rate of a popular network-wide anomaly detector based on Principal Components Analysis is increased 7-fold (increasing the attacker's chance of subsequent evasion by the same amount) by a variance injection attack of chaff traffic inserted into the network at training time. This high-variance chaff traffic increases the traffic volume by only 10%. In both cases the effects of increasing the information or the control available to the adversary are explored; and effective counter-measures are thoroughly evaluated, including a method based on Robust Statistics for the network anomaly detection domain.;The second class of attack explored on learning systems, involves an adversary aiming to evade detection by a previously-trained classifier. In the evasion problem the attacker searches for a negative instance of almost-minimal distance to some target positive, by submitting a small number of queries to the classifier. Efficient query algorithms are developed for almost-minimizing Lp cost over any classifier partitioning feature space into two classes, one of which is convex.;The third class of attack aims to violate the confidentiality of the learner's training data given access to a learned hypothesis. Mechanisms for releasing Support Vector Machine (SVM) classifiers are developed. Algorithmic stability of the SVM is used to prove that the mechanisms preserve differential privacy, meaning that for an attacker with knowledge of all but one training example and the learning map, very little can be determined about the final unknown example using access to the trained classifier. Bounds on utility are established for the mechanisms: the privacy-preserving classifiers' predictions should approximate the SVM's predictions with high probability.;The second part of this dissertation considers Security under the lens of Machine Learning. The first application of Machine Learning is to a learning-based reactive defense. The CISO risk management problem is modeled as a repeated game in which the defender must allocate security budget to the edges of a graph in order to minimize the additive profit or return on attack (ROA) enjoyed by an attacker. By reducing to results from Online Learning, it is shown that the profit/ROA from attacking the reactive strategy approaches that of attacking the best fixed proactive strategy over time. This result contradicts the conventional dogma that reactive security is usually inferior to proactive risk management. Moreover in many cases, it is shown that the reactive defender greatly outperforms proactive approaches.;The second application of Machine Learning to Security is for the construction of an attack on open-source software systems. When an open-source project releases a new version of their system, they disclose vulnerabilities in previous versions, sometimes with pointers to the patches that fixed them. Using features of diffs in the project's open-source repository, labeled by such disclosures, an attacker can train a model for discriminating between security patches and non-security patches. As new patches land in the open-source repository, before being disclosed as security or not, and before being released to users, the attacker can use the trained model to rank the patches according to likelihood of being a security fix. The adversary can then examine the ordered patches one-by-one until finding a security patch. For an 8 month period of Firefox 3's development history it is shown that an SVM-assisted attacker need only examine one or two patches per day (as selected by the SVM) in order to increase the aggregate window of vulnerability by 5 months. (Abstract shortened by UMI.)
机译：本文的第一部分从计算机安全的角度考虑机器学习，其目标是在对手面前学习。关于电子邮件垃圾邮件过滤和网络范围异常检测的两个大型案例研究探索了攻击者，这些攻击者通过毒害其学习数据来操纵学习者。在第一项研究中，开源垃圾邮件过滤器的误报率（FPR）通过向过滤器提供由99％的常规合法邮件和垃圾邮件以及1％包含以下内容的字典攻击垃圾邮件组成的训练集而增加到40％合法的话。通过提高FPR，对手可以对过滤器造成“拒绝服务”攻击。在第二个案例研究中，基于谷壳流量的方差注入攻击将基于主成分分析的流行的全网络异常检测器的假阴性率提高了7倍（将攻击者随后逃逸的几率提高了相同数量）。在训练时插入网络。高变异性谷壳交通将交通量仅增加10％。在这两种情况下，都探索了增加信息或对手可用控制的效果；并评估了有效的对策，包括针对网络异常检测域的基于鲁棒统计的方法。；在学习系统上探讨的第二类攻击，涉及一个旨在逃避先前训练有素的分类器的攻击者。在逃避问题中，攻击者通过向分类器提交少量查询来搜索到某个目标正数的距离几乎为最小的负数实例。开发了一种有效的查询算法，可将任何分类器将特征空间划分为两类的Lp成本几乎减至最小，其中一类是凸的。开发了用于发布支持向量机（SVM）分类器的机制。 SVM的算法稳定性用于证明该机制保留了差异性隐私，这意味着对于仅了解一个训练示例和学习图的所有攻击者而言，使用经过训练的分类器就无法确定最终的未知示例。为该机制建立了效用范围：隐私保护分类器的预测应以高概率近似支持向量机的预测。；本论文的第二部分考虑了机器学习视角下的安全性。机器学习的第一个应用是基于学习的反应式防御。 CISO风险管理问题被建模为重复游戏，在该游戏中，防御者必须将安全预算分配给图形的边缘，以最大程度地减少攻击者享有的附加利润或攻击收益（ROA）。通过减少到在线学习的结果，可以看出，随着时间的推移，攻击被动策略的收益/ ROA接近攻击最佳固定主动策略的收益/ ROA。该结果与传统的教条相矛盾，即反应式安全通常不如主动式风险管理。而且，在许多情况下，它表明被动防御者大大优于主动防御方法。机器学习到安全性的第二个应用是对开放源代码软件系统的攻击的构建。当一个开放源代码项目发布其系统的新版本时，他们会披露以前版本中的漏洞，有时还会指出修复它们的补丁程序的指针。攻击者可以使用该项目的开源存储库中差异功能（由此类公开标记），从而训练出一种模型来区分安全补丁和非安全补丁。随着新补丁降落在开源存储库中，无论是否被公开为安全性，以及先发布给用户，攻击者都可以使用经过训练的模型对补丁进行排序，以作为安全修复的可能性。然后，对手可以逐个检查排序的补丁，直到找到安全补丁为止。在Firefox 3的发展历史中，有8个月的时间表明，由SVM协助的攻击者每天仅需检查一个或两个补丁程序（由SVM选择），即可将漏洞的综合窗口延长5个月。 （摘要由UMI缩短。）

著录项

  • 作者

    Rubinstein, Benjamin.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 203 p.
  • 总页数 203
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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