Ozgur Koray Sahingoz, Saide Isilay Baykal and Deniz Bulut, Istanbul Kultur University, Turkey

In recent years, Internet technologies are grown pervasively not only in information-based web pages but also in online social networking and online banking, which made people’s lives easier. As a result of this growth, computer networks encounter with lots of different security threats from all over the world. One of these serious threats is “phishing”, which aims to deceive their victims for getting their private information such as username, passwords, social security numbers, financial information, and credit card number by using fake e-mails, webpage’s or both. Detection of phishing attack is a challenging problem, because it is considered as a semantics-based attack, which focuses on users’ vulnerabilities, not networks’ vulnerabilities. Most of the anti-phishing tools mainly use the blacklist/white list methods; however, they fail to catch new phishing attacks and results a high false-positive rate. To overcome this deficiency, we aimed to use a machine learning based algorithms, Artificial Neural Networks(ANNs) and Deep Neural Networks(DNNs), for training the system and catch abnormal request by analysing the URL of web pages. We used a dataset which contains 37,175 phishing and 36,400 legitimate web pages to train the system. According to the experimental results, the proposed approaches has the accuracy in detection of phishing websites with the rate of 92 % and 96 % by the use of ANN and DNN approaches respectively.

Phishing Detection System, Artificial Neural Networks, Deep Neural Networks, Big Data, Machine Learning, Tensor flow, Feature Extraction