Biblio

In last decades, the web and online services have revolutionized the modern world. However, by increasing our dependence on online services, as a result, online security threats are also increasing rapidly. One of the most common online security threats is a so-called Phishing attack, the purpose of which is to mimic a legitimate website such as online banking, e-commerce or social networking website in order to obtain sensitive data such as user-names, passwords, financial and health-related information from potential victims. The problem of detecting phishing websites has been addressed many times using various methodologies from conventional classifiers to more complex hybrid methods. Recent advancements in deep learning approaches suggested that the classification of phishing websites using deep learning neural networks should outperform the traditional machine learning algorithms. However, the results of utilizing deep neural networks heavily depend on the setting of different learning parameters. In this paper, we propose a swarm intelligence based approach to parameter setting of deep learning neural network. By applying the proposed approach to the classification of phishing websites, we were able to improve their detection when compared to existing algorithms.