An Evaluation of Machine Learning-Based Methods for Detection of Phishing Sites

In this paper, we present the performance of machine learning-based methods for detection of phishing sites. We employ 9 machine learning techniques including AdaBoost, Bagging, Support Vector Machines, Classification and Regression Trees, Logistic Regression, Random Forests, Neural Networks, Naive Bayes, and Bayesian Additive Regression Trees. We let these machine learning techniques combine heuristics, and also let machine learning-based detection methods distinguish phishing sites from others. We analyze our dataset, which is composed of 1,500 phishing sites and the same number of legitimate sites. We then classify them using the machine learning-based detection methods, and measure the performance. In our evaluation, we used f1 measure, error rate, and Area Under the ROC Curve (AUC) as performance metrics along with our requirements for detection methods. The highest f1 measure is 0.8581, the lowest error rate is 14.15%, and the highest AUC is 0.9342, all of which are observed in ...

• Type: Conference
• Year: 2008
• Where: ICONIP
• Authors: Daisuke Miyamoto, Hiroaki Hazeyama, Youki Kadobayashi
• Keywords: Detection Method, ICONIP 2008, Information Technology, Learning-based Detection Methods, Machine Learning-based Detection