An effective method for phishing websites detection

Phishing attack is now a big risk to people's daily life and networking environment. Through disguise 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 phishing websites are urgently needed to alleviate the threats posed by phishing attacks. As the active learning capability from massive data sets, the neural network is widely used to detect 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. In order to alleviate this problem, this paper proposes of Neural Network, an effective phishing websites detection model based on optimal feature selection method and Neural Network. In the proposed ofs Neural Network, a new index Feature Validity Value is firstly introduced to evaluate the impact of sensitive features on phishing websites detection.