Systematic Review of Issues and Solutions for Security in E-commerce

Jamra, Resty Kurnia; Anggorojati, Bayu; Kautsarina, Kautsarina; Sensuse, Dana Indra; Suryono, Ryan Randy

doi:10.1109/iceltics50595.2020.9315437

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…5) + Operation model: Of the four models mentioned above, (1) gives the worst results because only a single algorithm is used. Although (2) produces the fewest rules, this is the most optimal set of rules because of its processing model. On the other hand, in models (3) and ( 4), the sets of rules are generated independently, and then combined, so the rules will tend to be more and overlap.…”

Section: ) Implement the Ripper + Resultsmentioning

confidence: 99%

“…The e-commerce sector is expected to account for 21.8% of total global retail sales by 2024 [1]. As the worldwide ecommerce market share increases risks also increase, trusting becoming a concern as the number of frauds and scams has increased significantly over the past few years [2]. The ecommerce market in 2021 faces a risk of US$20 billion due to fraudulent transactions [3].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Fraud Transaction using Ripper Algorithm Combines with Ensemble Learning Model

Khang¹,

Anh²,

Thuận³

2023

IJACSA

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In the context of the 4.0 technology revolution, which develops and applies strongly in many fields, in which the banking sector is considered to be the leading one, the application of algorithms to detect fraud is extremely important. necessary. In recent years, credit card transactions including physical credit card payments and online payments have become increasingly popular in many countries around the world. This convenient payment method attracts more and more criminals, especially credit card fraud. As a result, many banks around the world have developed fraud detection and prevention systems for each credit card transaction. Data mining is one of the techniques applied in these systems. This study uses the Ripper algorithm to detect fraudulent transactions on large data sets, and the results obtained with accuracy, recall, and F1 measure of more than 97%. This research then used the Ripper algorithm combined with Ensemble Learning models to detect fraudulent transactions, the results are more than 99% reliable. Specifically, this model using the Ripper algorithm combined with the Gradient Boosting method has improved the predictive ability and obtained very reliable results. The use of algorithms combined with machine learning models is expected to be a new topic and will be widely applied to banks' or organizations' activities related to e-commerce.

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Section: ) Implement the Ripper + Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Fraud Transaction using Ripper Algorithm Combines with Ensemble Learning Model

Khang¹,

Anh²,

Thuận³

2023

IJACSA

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“…Consumers define online safety differently. However, Issues such as network security, online transactions, password theft, and credit card theft contribute to general safety concerns (Jamra et al, 2020). Consumers' intentions to shop online are greatly influenced by their perception of safety (Qalati et al, 2021).…”

Section: Securitymentioning

confidence: 99%

Determinants To Consumer’s Shopping Preferences

Matyus,

Mergenthal,

Gonzalez

et al. 2023

Journal of Behavioral and Applied Management

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How people prefer to shop is an ever-changing landscape. There has been a boom in online shopping since the first online shopping purchase in 1994. With the end of the pandemic, retailers and other businesses need to know whether this increase will continue. Online shopping preferences: safety, technology, branding, and customer experience were explored to determine the current determinants of shopping preferences. A phenomenological design was used to examine the lived experiences of six shoppers who represent the most popular demographic of online shoppers. This study presents a clearer understanding of what business marketers need to understand about the online shopping decisions of shoppers.

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“…Recently, even reputable companies have fallen prey to scam attacks, losing valuable customer data. This issue is further underscored in the work of (Jamra et al, 2020), where the most prevalent security issues in e-commerce encompass credit card fraud, cyberattacks, and threats to sensitive information. The e-commerce transaction process, as elucidated by (Chatterjee, 2015), comprises several intricate steps, each necessitating security and privacy measures.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Information technology for intellectual analysis of item descriptions in e-commerce

Cherednichenko,

Ivashchenko,

Lincényi

et al. 2023

JESI

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E-commerce is experiencing a robust surge, propelled by the worldwide digital transformation and the mutual advantages accrued by both consumers and merchants. The integration of information technologies has markedly augmented the efficacy of digital enterprise, ushering in novel prospects and shaping innovative business paradigms. Nonetheless, adopting information technology is concomitant with risks, notably concerning safeguarding personal data. This substantiates the significance of research within the domain of artificial intelligence for e-commerce, with particular emphasis on the realm of recommender systems. This paper is dedicated to the discourse surrounding the construction of information technology tailored for processing textual descriptions pertaining to commodities within the e-commerce landscape. Through a qualitative analysis, we elucidate factors that mitigate the risks inherent in unauthorized data access. The cardinal insight discerned is that the apt utilization of product matching technologies empowers the formulation of recommendations devoid of entailing customers' personal data or vendors' proprietary information. A meticulously devised structural model of this information technology is proffered, delineating the principal functional components essential for processing textual data found within electronic trading platforms. Central to our exposition is the exploration of the product comparison predicated on textual depictions. The resolution of this challenge stands to enhance the efficiency of product searches and facilitate product juxtaposition and categorization. The prospective implementation of the propounded information technology, either in its entirety or through its constituent elements, augurs well for sellers, enabling them to improve a pricing strategy and heightened responsiveness to market sales trends. Concurrently, it streamlines the procurement journey for buyers by expediting the identification of requisite goods within the intricate milieu of e-commerce platforms.

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Systematic Review of Issues and Solutions for Security in E-commerce

Cited by 12 publications

References 23 publications

Detecting Fraud Transaction using Ripper Algorithm Combines with Ensemble Learning Model

Detecting Fraud Transaction using Ripper Algorithm Combines with Ensemble Learning Model

Determinants To Consumer’s Shopping Preferences

Information technology for intellectual analysis of item descriptions in e-commerce

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