The nature of statistical learning theory
The nature of statistical learning theory
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Can machine learning be secure?
ASIACCS '06 Proceedings of the 2006 ACM Symposium on Information, computer and communications security
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Spam and the ongoing battle for the inbox
Communications of the ACM - Spam and the ongoing battle for the inbox
Learning to detect phishing emails
Proceedings of the 16th international conference on World Wide Web
Relaxed online SVMs for spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A comparison of machine learning techniques for phishing detection
Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit
Exploiting machine learning to subvert your spam filter
LEET'08 Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Web Sites: A Knowledge Extraction from Web Data Approach - Volume 170 Frontiers in Artificial Intelligence and Applications
New filtering approaches for phishing email
Journal of Computer Security - EU-Funded ICT Research on Trust and Security
Dynamic rough clustering and its applications
Applied Soft Computing
Future trends in business analytics and optimization
Intelligent Data Analysis
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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, 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 theWeighted Margin Support Vector Machines with a game theoretic prior knowledge function is proposed. In this paper, a new contentbased 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 were obtained using traditional machine learning techniques over extracted features.