Inicio  /  Applied Sciences  /  Vol: 14 Par: 2 (2024)  /  Artículo
ARTÍCULO
TITULO

Experimental Evaluation of Possible Feature Combinations for the Detection of Fraudulent Online Shops

Audrone Janaviciute    
Agnius Liutkevicius    
Gedas Dabu?inskas and Nerijus Morkevicius    

Resumen

Online shopping has become a common and popular form of shopping, so online attackers try to extract money from customers by creating online shops whose purpose is to compel the buyer to disclose credit card details or to pay money for goods that are never delivered. Existing buyer protection methods are based on the analysis of the content of the online shop, customer reviews, the URL (Uniform Resource Locator) of the website, the search in blacklists or whitelists, or the combination of the above-mentioned methods. This study aims to find the minimal set of publicly and easily obtainable features to create high-precision classification solutions that require little computing and memory resources. We evaluate various combinations of 18 features that belong to three possible categories, namely URL-based, content-based, and third-party services-based. For this purpose, the custom dataset is created, and several machine learning models are applied for the detection of fraudulent online shops based on these combinations of features. The results of this study show that even only four of the most significant features allow one to achieve 0.9342 classification accuracy, while 0.9605 accuracy is reached with seven features, and the best accuracy of 0.9693 is achieved using thirteen and fifteen features.

 Artículos similares

       
 
Daniele Granata, Alberto Savino and Alex Zanotti    
The present study aimed to investigate the capability of mid-fidelity aerodynamic solvers in performing a preliminary evaluation of the static and dynamic stability derivatives of aircraft configurations in their design phase. In this work, the mid-fidel... ver más
Revista: Aerospace

 
Chenglin Yang, Dongliang Xu and Xiao Ma    
Due to the increasing severity of network security issues, training corresponding detection models requires large datasets. In this work, we propose a novel method based on generative adversarial networks to synthesize network data traffic. We introduced... ver más
Revista: Applied Sciences

 
Changchang Li, Botao Xu, Zhiwei Chen, Xiaoou Huang, Jing (Selena) He and Xia Xie    
University students, as a special group, face multiple psychological pressures and challenges, making them susceptible to social anxiety disorder. However, there are currently no articles using machine learning algorithms to identify predictors of social... ver más
Revista: Applied Sciences

 
Zheng Li, Xinkai Chen, Jiaqing Fu, Ning Xie and Tingting Zhao    
With the development of electronic game technology, the content of electronic games presents a larger number of units, richer unit attributes, more complex game mechanisms, and more diverse team strategies. Multi-agent deep reinforcement learning shines ... ver más
Revista: Algorithms

 
Ken Jom Ho, Ender Özcan and Peer-Olaf Siebers    
Solving multiple objective optimization problems can be computationally intensive even when experiments can be performed with the help of a simulation model. There are many methodologies that can achieve good tradeoffs between solution quality and resour... ver más
Revista: Algorithms