Detection of Phishing Webpages Using Heterogeneous Transfer Learning

Abstract

The detection of phishing websites using traditional machine learning methods has been demonstrated in previous studies. Traditional machine learning methods assume that the input feature space is the same between the training and testing data. There are scenarios in machine learning, where the available labeled training data has a different input feature space than the testing data. In cases where the input feature space between the testing and training data are different, traditional machine learning methods cannot be used. Heterogeneous transfer learning methods are used to transform the different input feature spaces between the testing and the training data into a unique and common set of input features. For our experiment, we construct numerous scenarios for the application of phishing website detection, where the features of the testing and training data are different. Our experiment starts with a baseline dataset for the detection of phishing websites. This baseline dataset is used to create separate training and testing datasets by splitting the features, such that the features in the training and testing data are mutually exclusive. Then, a heterogeneous transfer learning technique called Canonical Correlation Analysis is used to align the input feature space between the training and testing data. The feature aligned training and testing data is used with various traditional machine learning methods and homogeneous transfer learning methods to predict phishing websites. The performance results of the different scenarios and algorithms are reported and analyzed.