A Novel Multi-Layer Heuristic Model for Anti-Phishing
Zhao Zhang, Qinggang He, Bailing Wang · 2017
Phishing website detection1 is very important for e-banking and e-commerce users. Current detection methods for anti-phishing have proved to be well-performed in term of accuracy, recall rate and F-measure. However, increasingly complex phishing methods make it more necessary to optimize the detection scheme by timely recognizing new features and accurately choosing the optimal feature subset. To react changing phishing means and resolve feature updating issues, we propose a novel multi-layer heuristic anti-phishing model with feature selection algorithms and heuristic classification algorithms. Five feature selection algorithms are utilized to pre-process feature sets. Then four classification algorithms are applied to identify phishing websites and legitimate websites. Experimental results show that the proposed model utilizing information gain algorithm in procedure of feature subset selection and Random Tree algorithm in heuristic classification achieves 96% accuracy with less time cost.