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.
When searching for a brand name in search engines, it is very likely to come across websites that sell fake brand's products. In this paper, we study how to tackle and measure this problem automatically. Our solution consists of a pipeline with two learning stages. We first detect the ecommerce websites (including shopbots) present in the list of search results and then discriminate between legitimate and fake ecommerce websites. We identify suitable learning features for each stage and show through a prototype system termed RI.SI.CO. that this approach is feasible, fast, and highly effective. Experimenting with one goods sector, we found that RI.SI.CO. achieved better classification accuracy than that of non-expert humans. We next show that the information extracted by our method can be used to generate sector-level 'counterfeiting charts' that allow us to analyze and compare the counterfeit risk associated with different brands in a same sector. We also show that the risk of coming across counterfeit websites is affected by the particular web search engine and type of search query used by shoppers. Our research offers new insights and some very practical and useful means for analyzing and measuring counterfeit ecommerce websites in search-engine results, thus enabling targeted anti-counterfeiting actions.