OFS-NN: An Effective Phishing Websites Detection Model Based on Optimal Feature Selection and Neural Network

• Published in: IEEE access Vol. 7; pp. 73271 - 73284
• Main Authors: Zhu, Erzhou, Chen, Yuyang, Ye, Chengcheng, Li, Xuejun, Liu, Feng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blacklisting; Computer science; Datasets; Feature extraction; Feature selection; Indexes; Information security; intrusion detection; Machine learning; Massive data points; neural network; Neural networks; Phishing; Websites
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2920655

Phishing attack is now a big threat to people's daily life and networking environment. Through disguising illegal URLs as legitimate ones, attackers can induce users to visit the phishing URLs to get private information and other benefits. Effective methods of detecting the phishing websites are urgently needed to alleviate the threats posed by the phishing attacks. As the active learning capability from massive data sets, the neural network is widely used to detect the phishing attacks. However, in the stage of training data sets, many useless and small influence features will trap the neural network model into the problem of over-fitting. This problem usually causes the trained model that cannot effectively detect phishing websites. In order to alleviate this problem, this paper proposes OFS-NN, an effective phishing websites detection model based on the optimal feature selection method and neural network. In the proposed OFS-NN, a new index, feature validity value (FVV), is first introduced to evaluate the impact of sensitive features on the phishing websites detection. Then, based on the new FVV index, an algorithm is designed to select the optimal features from the phishing websites. This algorithm is able to alleviate the over-fitting problem of the underlying neural network to a large extent. The selected optimal features are used to train the underlying neural network, and finally, an optimal classifier is constructed to detect the phishing websites. The experimental results show that the OFS-NN model is accurate and stable in detecting many types of phishing websites.