Classifier evaluation and attribute selection against active adversaries
Classifier evaluation and attribute selection against active adversaries
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DOI：
10.1007/s10618-010-0197-3
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发表时间：
2011-01-01
影响因子：
4.8
通讯作者：
Clifton, Chris
中科院分区：
文献类型：
--
作者：
Kantarcioglu, Murat;Xi, Bowei;Clifton, Chris
许多数据挖掘应用程序，例如垃圾邮件过滤和入侵检测，都面临着活跃的对手。在所有这些应用中，由于对手采用的转换，未来数据集和训练数据集不再来自同一群体。因此，现有分类技术的主要假设不再成立，并且最初成功的分类器很容易退化。这变成了对手和数据挖掘者之间的博弈：对手修改其策略以避免被当前分类器检测到；然后，数据挖掘器根据新威胁更新其分类器。在本文中，我们研究了在这个看似永无止境的游戏中达到均衡的可能性，其中双方都没有动力去改变。修改分类器会导致误报过多，而真报增加太少；对手的改变会降低未检测到的假阴性项目的效用。我们开发了一个博弈论框架，可以分析对抗性分类应用的平衡行为，并提供寻找平衡点的解决方案。分类器的均衡性能表明其最终的成功或失败。然后，数据挖掘者可以根据属性的均衡表现来选择属性，并构建有效的分类器。在线借贷数据的案例研究演示了如何将所提出的博弈论框架应用于实际应用。
Many data mining applications, such as spam filtering and intrusion detection, are faced with active adversaries. In all these applications, the future data sets and the training data set are no longer from the same population, due to the transformations employed by the adversaries. Hence a main assumption for the existing classification techniques no longer holds and initially successful classifiers degrade easily. This becomes a game between the adversary and the data miner: The adversary modifies its strategy to avoid being detected by the current classifier; the data miner then updates its classifier based on the new threats. In this paper, we investigate the possibility of an equilibrium in this seemingly never ending game, where neither party has an incentive to change. Modifying the classifier causes too many false positives with too little increase in true positives; changes by the adversary decrease the utility of the false negative items that are not detected. We develop a game theoretic framework where equilibrium behavior of adversarial classification applications can be analyzed, and provide solutions for finding an equilibrium point. A classifier's equilibrium performance indicates its eventual success or failure. The data miner could then select attributes based on their equilibrium performance, and construct an effective classifier. A case study on online lending data demonstrates how to apply the proposed game theoretic framework to a real application.