Online Imbalanced Support Vector Machine for Phishing Emails Filtering

Phishing emails are a real threat to internet communication and web economy. In real-world emails datasets, data are predominately composed of ham samples with only a small percentage of phishing ones. Standard Support Vector Machine (SVM) could produce suboptimal results in filtering phishing emails, and it often requires much time to perform the classification for large data sets. In this paper, an online version of imbalanced SVM (OISVM) is proposed. First an email is converted into 20 features which are well selected based on its content and link characters. Second, OISVM is developed to optimize the classification accuracy and reduce computation time, which is used a novel method to adjust the separation hyperplane of imbalanced date sets and an online algorithm to make the retaining process much fast. Compared to the existing methods, the experimental results show that OISVM can achieve significantly using a proposed expressive evaluation method.