Using the Wisdom of Crowds to Prevent Internet Frauds
نویسندگان
چکیده
With the rapid growth of the netizen population in China, more and more internet frauds are committed. Many people suffer from internet frauds by losing wealth or other valuable things. To prevent internet frauds, we first need to discover the methods in which internet frauds are conducted. In this paper, we investigate and categorize the internet frauds in China. So far, there are typically six kinds of internet frauds, including email fraud, website fraud, e-commerce fraud, virus fraud, password fraud, and message fraud. To prevent these internet frauds, many approaches, such as intrusion detection and access control, have been proposed to help users. However, most of these methods are limited to detecting a small volume of internet frauds. To address this issue, we propose a methodology to use the wisdom of crowds, with the help of Semantic Web and Web 2.0 technologies, to detect a large volume of internet frauds. The proposed framework is composed of eight modules: internet fraud report module, key element extraction module, linked data generation module, linked data repository, query interface, query interpretation module, SPARQL module, and answer generation module. Based on the framework, users are able to input the internet fraud reports in a controlled natural language. The internet fraud reports are converted into linked data automatically. Then, the users can query the linked data in a semantic fashion. A case study and a survey preliminarily indicate that the proposed method is able to help users share and identify the internet frauds effectively.
منابع مشابه
Wised Semi-Supervised Cluster Ensemble Selection: A New Framework for Selecting and Combing Multiple Partitions Based on Prior knowledge
The Wisdom of Crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. This theory used for in clustering problems. Previous researches showed that this theory can significantly increase the stability and performance of...
متن کاملWised Semi-Supervised Cluster Ensemble Selection: A New Framework for Selecting and Combing Multiple Partitions Based on Prior knowledge
The Wisdom of Crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. This theory used for in clustering problems. Previous researches showed that this theory can significantly increase the stability and performance of...
متن کاملPornography Detection with the Wisdom of Crowds
With rapid development of the Internet, much attention has been paid to the problem of children exposed to Internet pornography. Existing detection techniques, which mainly focus on pornography content analysis have gained much success. However, they still meet challenges in practical Web environment due to the great computational costs and the difficulties in dealing with various pornography f...
متن کاملDefining a Taxonomy for Research Areas on ICT for Governance and Policy Modelling
As governments across the world provide more and more support to open data initiatives and web 2.0 channels for engaging citizens, researchers orient themselves towards future internet, wisdom of crowds and virtual world experiments. In this context, the domain of ICT for Governance and Policy Modelling has recently emerged to achieve better, participative, evidence-based and timely governance....
متن کاملModeling Wisdom of Crowds Using Latent Mixture of Discriminative Experts
In many computational linguistic scenarios, training labels are subjectives making it necessary to acquire the opinions of multiple annotators/experts, which is referred to as ”wisdom of crowds”. In this paper, we propose a new approach for modeling wisdom of crowds based on the Latent Mixture of Discriminative Experts (LMDE) model that can automatically learn the prototypical patterns and hidd...
متن کامل