Paper Title
Deep Boltzmann Machine Based Detection of Phishing URLS

The transparency of the Web and Internet exposes opportunities for criminals to upload malicious content. Simultaneously with the enhancement of online business exchanges, Phishing - the act of stealing individual information which rises in number. The phishers endeavor to make fake-sites appear to be like true legitimate sites both in terms of interface and uniform asset locator (URL) address. Subsequently, the records of casualty have been rising due to ineffectual methods using the blacklist to recognize phishing. The existing methods make use of all extracted features in the phishing URL detection, leading to high false positive rate. This paper proposes an automatic phishing identification method using deep learning approach for detecting unknown URL is either a phishing URL or benign URL. Deep Boltzmann Machine (DBM) is utilized for pre-training the model with a superior representation of information for feature selection and binary classification of benign and phishing URL is performed utilizing Deep Neural Network (DNN) which accomplishes higher recognition rate of phishing URLs with low false positive rate than other machine learning techniques. Keywords - Phishing, Phishing URL detection, Deep Boltzmann Machine, Deep Neural Network.