NDLI: Online phishing classification using adversarial data mining and signaling games

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Online phishing classification using adversarial data mining and signaling games

Content Provider	ACM Digital Library
Author	L'Huillier, Gaston Figueroa, Nicolas Weber, Richard
Abstract	In adversarial systems, the performance of a classifier decreases after it is deployed, as the adversary learns to defeat it. Recently, adversarial data mining was introduced as a solution to this, where the classification problem is viewed as a game mechanism between an adversary and an intelligent and adaptive classifier. Over the last years, phishing fraud through malicious email messages has been a serious threat that affects global security and economy, where traditional spam filtering techniques have shown to be ineffective. In this domain, using dynamic games of incomplete information, a game theoretic data mining framework is proposed in order to build an adversary aware classifier for phishing fraud detection. To build the classifier, an online version of the Weighted Margin Support Vector Machines with a game theoretic prior knowledge function is proposed. In this paper, a new content-based feature extraction technique for phishing filtering is described. Experiments show that the proposed classifier is highly competitive compared with previously proposed online classification algorithms in this adversarial environment, and promising results where obtained using traditional machine learning techniques over extracted features.
Starting Page	33
Ending Page	42
Page Count	10
File Format	PDF
ISBN	9781605586694
DOI	10.1145/1599272.1599279
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Spam and phishing detection Email filtering Adversarial classification Games of incomplete information Data mining Game theory
Content Type	Text
Resource Type	Article

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