Due to the rapid growth of the internet, websites have become the intruder's main target. An intruder embeds malicious contents in a web page for the purpose of doing some bad and unwanted-activities such as: credential information and resource theft, luring a user to visit a dangerous website, downloading and installing software to join a botnet or to participate in distributed denial of service, and even damage the visitor system. As the number of web pages increases, the malicious web pages are also increasing and the attack is increasingly become sophisticated. In this paper, we provide a framework for detecting a malicious web page using artificial neural network learning techniques. In addition to the significant detection rate, our objective is to find also which discriminative features characterize the attack and reduce the false positive rate. The algorithm is based on two features group, the URL lexical and the page content features. The experiments has shown the expected results and the high false positive rate which produced by machine learning approaches is reduced. © 2014 Springer-Verlag Berlin Heidelberg.