Covering the Monitoring Network: A Unified Framework to Protect E-Commerce Security

Abstract

Multimedia applications in smart electronic commerce (e-commerce), such as online trading and Internet marketing, always face security in storage and transmission of digital images and videos. This study addresses the problem of security in e-commerce and proposes a unified framework to analyze the security data. First, to allocate the definite security resources optimally, we build our ecommerce monitoring model as an undirected network, where a monitored node is a vertex of the graph and a connection between vertices is an undirected edge. Moreover, we aim to find a minimal cover for the monitoring network as the optimal solution of resource allocation, which is defined as the network monitoring minimization problem (NMM). This problem is proved to be NP-hard. Second, by analyzing the latent threats, we design a novel and trusted monitoring system that can integrate incident monitoring, data analysis, risk assessment, and security warnings. This system does not touch users' privacy data. Third, we propose a sequential model-based risk assessment method, which can predict the risk according to the text semantics. Our experimental results on web scale data demonstrate that our system is flexible enough when monitoring, which also verify the effectiveness and efficiency of our system.

A comparative study of frequentist vs Bayesian A/B testing in the detection of E-commerce fraud

“…It has been suggested that an e-commerce trusted trading framework (ETTF) using blockchain can improve security in e-commerce (Luhach, Dwivedi, & Jha, 2014) and two-way authentication based on visual cryptography and steganography can further protect e-commerce from fraud (Ismanto, Ar, Fajar, Bachtiar, & others, 2019). Research has also studied graphical passwords for e-commerce applications that can improve the security and usability of customers (Qiu & Li, 2017). Mahto and Yadav (2015) proposed a unified framework for securing image data stored in third-party clouds and Sharma, Mathur, and Srivastava (2018) proposed a system that combines text-based steganography, visual cryptography and OTP can avoid identity theft and customer data privacy.…”