Transaction Security Research Articles

Innovative Marketing Strategies Analysis of Alibaba

In the context of a globalized economy, the rapid development of cross-border e-commerce offers businesses unprecedented opportunities for market expansion. However, this expansion process requires enterprises to effectively promote their products and services to attract international buyers and build enough trust to ensure the security and reliability of transactions. As one of the world's largest B2B e-commerce platforms, Alibaba has successfully addressed these challenges through its unique business model and strategy, facilitating global trade. Through a case study, this paper examines the implementation details of Alibaba International's innovation and promotion activities, including search engine optimization, customized services, ecosystem building, implementation of a digital showroom, and the safeguards it implemented to build trust between buyers and sellers. It explains how Alibaba has made itself the world's largest B2B cross-border e-commerce platform and contributed to the digital transformation of global trade.

Financial Literacy and Electronic Money Empower Online Drivers' Impact Globally

This study investigates how financial literacy, self-control, and electronic money influence the lifestyle of online motorcycle taxi drivers in Sidoarjo, using quantitative methods and a sample size of 96 respondents. Through questionnaires and data analysis including validity tests, reliability tests, multiple linear regression analysis, and t-tests using SPSS version 26, the research finds a significant positive impact of these factors on drivers' lifestyles. The findings underscore the importance of promoting financial literacy and self-control among drivers, with implications for industry stakeholders and policymakers aiming to enhance driver well-being and sustainability in the online motorcycle taxi sector. Highlight: Financial literacy and self-control impact drivers' lifestyles significantly. Electronic money improves efficiency and security in financial transactions. Promoting literacy and control sustains well-being and industry growth. Keyword: Financial Literacy, Self-Control, Electronic Money, Online Motorcycle Taxi Drivers, Sidoarjo

Counterfeit Currency Detection Using Machine Learning

Counterfeit currency detection is a critical aspect of maintaining financial integrity. This paper introduces an innovative methodology for identifying counterfeit Indian rupee notes. By employing advanced image processing techniques and Convolutional Neural Networks (CNNs), specifically ResNet architecture, the proposed system achieves impressive accuracy in distinguishing genuine from counterfeit banknotes. The dataset, meticulously compiled through a combination of web scraping and synthetic printing, ensures robust training and evaluation. Experimental findings underscore the efficacy of the ResNet-based approach, highlighting its superiority over traditional CNN models. This research contributes significantly to bolstering currency security and trust in financial transactions, offering practical insights for combating counterfeit currency challenges.

IMPLEMENTATION OF AES AND SHA-3 CRYPTOGRAPHY ALGORITHMS IN SECURING USER SENSITIVE DATA ON THE ARTESIAN WATER BILL PAYMENT TRANSACTION WEBSITE

Transacting online is something that is familiar to almost everyone in the digital era. The presence of digital transactions brings convenience in carrying out trading activities, but the use of the internet in creating easy transactions with several challenges in the form of hacking. In order to realize secure transactions, the level of protection on digital transaction service provider sites must be made as strong as possible to prevent digital attacks. Therefore, a protection system for user payment data on one of the transaction websites was created by applying the AES and SHA-3 cryptographic algorithm methods. The AES algorithm method was chosen because its implementation is not complicated but has proven its security, while SHA-3 is used to create unique keys from user passwords with the latest hash technology, so that the data encryption and decryption process becomes more protected because it uses the secret key of each user but not directly. The application of cryptographic algorithms will provide additional protection for the security of nominal user transaction data from cyber attacks so that the data. This research will produce a cryptography program flow with the Python programming language and examples of encryption and hash process results during program execution.

Ethereum Phishing Scam Detection Based on Data Augmentation Method and Hybrid Graph Neural Network Model.

The rapid advancement of blockchain technology has fueled the prosperity of the cryptocurrency market. Unfortunately, it has also facilitated certain criminal activities, particularly the increasing issue of phishing scams on blockchain platforms such as Ethereum. Consequently, developing an efficient phishing detection system is critical for ensuring the security and reliability of cryptocurrency transactions. However, existing methods have shortcomings in dealing with sample imbalance and effective feature extraction. To address these issues, this study proposes an Ethereum phishing scam detection method based on DA-HGNN (Data Augmentation Method and Hybrid Graph Neural Network Model), validated by real Ethereum datasets to prove its effectiveness. Initially, basic node features consisting of 11 attributes were designed. This study applied a sliding window sampling method based on node transactions for data augmentation. Since phishing nodes often initiate numerous transactions, the augmented samples tended to balance. Subsequently, the Temporal Features Extraction Module employed Conv1D (One-Dimensional Convolutional neural network) and GRU-MHA (GRU-Multi-Head Attention) models to uncover intrinsic relationships between features from the time sequences and to mine adequate local features, culminating in the extraction of temporal features. The GAE (Graph Autoencoder) concept was then leveraged, with SAGEConv (Graph SAGE Convolution) as the encoder. In the SAGEConv reconstruction module, by reconstructing the relationships between transaction graph nodes, the structural features of the nodes were learned, obtaining reconstructed node embedding representations. Ultimately, phishing fraud nodes were further identified by integrating temporal features, basic features, and embedding representations. A real Ethereum dataset was collected for evaluation, and the DA-HGNN model achieved an AUC-ROC (Area Under the Receiver Operating Characteristic Curve) of 0.994, a Recall of 0.995, and an F1-score of 0.994, outperforming existing methods and baseline models.

NLP-BERT AND OPTIMIZATION OF EFFICIENCY-SECURITY IN BLOCKCHAIN-ACCOUNTING SMART CONTRACTS

In the evolving digital era, blockchain-based accounting systems leveraging smart contracts are increasingly crucial. Blockchain, known for its transparency and security, faces challenges in the efficiency and security of smart contracts, necessitating innovative solutions. This research explores the potential of Natural Language Processing (NLP) technology such as BERT to enhance these aspects within smart contracts. Grounded in blockchain and smart contract theories, the study investigates BERT's use in detecting financial fraud and improving transaction security. The research methodology employs Bibliometric Analysis with a PICO approach to formulate research questions and gather relevant data. Bibliometric analysis provides deeper insights into current developments and challenges in this field. The analysis reveals trends in document production, author collaborations, and keywords related to blockchain and BERT. Co-occurrence networks and thematic maps highlight the significance and relevance of these terms in the context of blockchain and smart contracts. In conclusion, this study makes a significant contribution to understanding how NLP technology can be optimized to enhance the efficiency and security of smart contracts in blockchain-based accounting systems. Further research is essential to develop BERT optimization in this context.

Helphands: Empowering the Fundraising, Innovating With Blockchain and Smart Contracts

The concept of crowdfunding, a method for online fundraising, has evolved to enable public contributions in support of creative projects. Leveraging blockchain technology, cro wdfunding platforms now integrate smart contracts, ensuring secure, transparent, and reliable transactions. This study focuses on the development of interactive interfaces for campaign creation and financial contributions, facilitating engagement for both creators and donors. Campaign creators can propose initiatives and submit them for approval, while donors can browse and support projects through financial contributions. Transparency is ensured through blockchain recording of all transactions, offering immutable and transparent records. The incorporation of smart contracts is pivotal in removing the need for intermediary trust in blockchain-based agreements. This work emphasizes the importance of developing executable code for blockchain execution, ensuring transaction integrity and security. Initially associated with cryptocurrencies, blockchain technology has expanded its applications across industries, offering a sustainable solution for internet transactions. Crowdfunding platforms stand to benefit significantly from blockchain integration. Challenges in the current crowdfunding landscape include inadequate oversight and fraudulent investment schemes. By leveraging Ethereum smart contracts, this study seeks to address these challenges, enforcing time limits and automating contract execution to enhance trust and transparency in the crowdfunding process.

The Relationship between Business Actors and Consumers According to the Review of Electronic Transaction Law

This research aims to analyze the relationship between business actors and consumers in electronic transactions according to a review of electronic transaction law in Indonesia. This research uses normative legal research methods with a doctrinal type of research. Primary data is obtained from primary legal materials such as laws, government regulations, and court decisions. Meanwhile, secondary data is obtained from secondary legal materials such as books, scientific journals and legal articles. The data analysis technique used is qualitative analysis. The research results show that the relationship between business actors and consumers in electronic transactions is regulated in Law Number 11 of 2008 concerning Information and Electronic Transactions (UU ITE), Law Number 8 of 1999 concerning Consumer Protection (UUPK), and statutory regulations. other related. Business actors have an obligation to provide correct and clear information about the products or services offered, as well as guarantee the security of electronic transactions. Consumers have the right to obtain correct and clear information about products or services, as well as the right to resolve disputes fairly.

CREDIT CARD FRAUD DETECTION

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. In an era where digital transactions have become ubiquitous, the security of financial transactions is of paramount importance. The advent of machine learning and data science techniques offers promising avenues for enhancing fraud detection mechanisms. Analysing a dataset is a critical step in obtaining relevant insights from massive amounts of data. This research paper delves into the use case of Data science & Machine Learning and its applications for our major project. It goes into the use of Python as a powerful tool for data analysis, emphasizing its importance in dealing with complex datasets. Furthermore, the project investigates the many Python libraries used in data science and machine learning applications, the significance of data visualization with Libraries such as Seaborn and Matplotlib, etc. After which it goes on with the main work. This project of ours delves into the realm of credit card fraud detection, leveraging Python and its powerful libraries such as NumPy, Pandas, Seaborn, TensorFlow, and Matplotlib for comprehensive data analysis and visualization. Employing a host of machine learning algorithms including K-Nearest Neighbors, Logistic Regression, Random Forest, Decision Tree, and LDA, the project aims to tackle the challenge posed by highly unbalanced datasets, specifically transactions made by European cardholders in September 2013. By discerning fraudulent transactions accurately, this endeavour seeks to bolster the integrity of financial systems, safeguarding both customers and institutions from potential losses. Keywords: Python, pandas, seaborn, data science, machine learning.

Deep learning-based credit card fraud detection in federated learning

The increased growth of various communication systems and computing technology enhanced the growth of regular and online transactions through credit cards. Similarly, it has also dramatically increased the frequency of fraudulent transactions, which significantly affects various organizations. Hence, it is necessary to implement precise techniques for secure transactions and the detection of certain credit card fraud transactions. In this investigation, a hybrid algorithmic optimization-based deep learning technique is designed. The Jellyfish Namib Beetle Optimization Algorithm-SpinalNet (JNBO-SpinalNet) is designed for fraudulent credit card transaction detection. The pre-processing of input data using quantile normalization is performed and the features are selected using various distance measures. The selected features are augmented using Bootstrapping method and SpinalNet is finally allowed to detect credit card frauds. The detection process in the classifier is improved by tuning the SpinalNet with the help of the designed JNBO model. The superiority of the designed fraudulent transaction detection method is estimated by using different evaluation parameters. The JNBO-SpinalNet model recorded higher performance with 89.10 % accuracy, 13.16 % loss function, 28.68 % Mean Square Error (MSE), 10.20 % False Positive Rate (FPR), 89.82 %, Mean Average Precision (MAP), and 53.56 % Root Mean Square Error (RMSE).

EXPLORING BLOCKCHAIN TECHNOLOGY AND ITS IMPACT IN E-COMMERCE

Blockchain technology has fundamentally transformed the landscape of digital transactions and data management, deeply influencing the domain of e-commerce. Its integration across various sectors has revolutionized the access to secure, transparent, and efficient transaction mechanisms, thereby promoting financial inclusivity and advancing contemporary business practices. Moreover, blockchain enables enhanced supply chain management, robust data protection, and the decentralization of marketplaces. The purpose of this article is to examine the multi-faceted impacts of blockchain technology on e-commerce effectiveness and to assess its potential as a cornerstone for digital transformation in commercial operations. Utilizing a case study methodology, the research explores practical applications of blockchain in e-commerce, particularly focusing on systems like the IBM Food Trust for improving supply chain transparency and consumer trust. The study employs a comparative analysis to delineate the effects of blockchain on operational efficiency and consumer satisfaction. While the findings acknowledge the profound short-term benefits of blockchain implementation, they also highlight significant challenges related to scalability and regulatory frameworks that could affect long-term integration. The article concludes that continued innovation and regulatory evolution are essential for blockchain to become a major driver of economic competitiveness and digital transformation in the global market.

ÜSTÜN MƏXFİLİYİN QORUNMASI TEXNİKALARI İLƏ AĞILLI MÜQAVİLƏLƏRDƏ MƏLUMAT MƏXFİLİYİNİN TƏKMİLLƏŞDİRİLMƏSİ

This research paper delves into the imperative domain of bolstering data confidentiality within smart contracts through the integration of advanced privacy-preserving methodologies. Smart contracts, pivotal components of blockchain technology, execute self-executing contracts with predefined conditions and are increasingly utilized across various sectors, necessitating stringent data protection measures. The paper addresses the pressing need for fortified data privacy within smart contracts and investigates cutting-edge approaches to mitigate privacy challenges. Two focal techniques under scrutiny are zero-knowledge proofs (SBÇs) and homomorphic encryption. SBÇs facilitate the validation of computations without revealing sensitive data, enabling parties to verify transaction authenticity without disclosing the underlying information. Meanwhile, homomorphic encryption permits computations on encrypted data, preserving confidentiality by allowing operations on encrypted information without the need for decryption. By analyzing these advanced privacy-preserving techniques, this study aims to address the vulnerabilities in data confidentiality present in smart contracts. Its findings hold significant promise in fortifying the security and confidentiality of transactions, thus contributing substantially to the evolution of secure blockchain technology. This research underscores the pivotal role of innovative privacy-enhancing mechanisms in safeguarding sensitive data within smart contracts, ensuring the trust and integrity essential for their widespread adoption.

Managerial and Organizational Implications Arising from the Implementation of Blockchain Technology in Supply Chains: An AS-IS and To-Be Analysis

Purpose: The main objective of this research is to highlight the managerial and organizational implications for businesses resulting from the implementation of blockchain logic and technologies. The functionalities of blockchain technologies are compared with the traditional approach to supply chain management to emphasize the impact they can have on managing intra-supply chain relationships and the organizational structure of companies involved in supply channels. Design/methodology/approach: To conduct this study, a conceptual study was carried out, starting from an analysis of the main characteristics of blockchain technologies, to build an AS-IS scenario compared with the TO-BE scenario resulting from the implementation of these technologies within supply chains. To conduct this study, conceptual categories of the viable system approach and process management perspective were utilized. The conceptual analysis was performed using a matrix representation, which correlates material, informational, and financial flows with the dimensions of traceability, transparency, and efficiency, before and after the implementation of blockchains in managing supply chain transactions. Findings: The study highlighted how the principles underlying blockchain can lead to overcoming the traditional indispensable search for trust among supply chain operators. Therefore, through these results, it is possible to hypothesize an improvement in the resilience conditions of companies operating in block–supply chains. Originality/value: This study contributes to investigating the relationship between blockchain and operational consonance among operators in managing interorganizational processes to improve conditions of efficiency, traceability, and transparency, which are functional in the resilience of companies involved in the supply chain compared to the changing dynamics of the context. Research implications: Trustless logic in the implementation of blockchain technologies leads to the configuration of a new concept of trust, connected to the entire supply chain system, as a result of the improvement in the security, visibility, and efficiency conditions of transactions. The increased visibility and transparency developed in block–supply chains prompt all actors involved in the supply chains to rethink information management, given the reduction in informational asymmetries and, thus, the optimization of transaction costs.

Analysis of Fraud Patterns in Islamic Banking Transactions: Strategies and Implementation of Prevention

The research aims to analyze patterns of fraud in Sharia banking transactions and identify effective prevention strategies. The method used is a qualitative approach with case study data collection. The research findings reveal various common patterns of fraud, such as identity theft, document forgery, transaction manipulation, and misappropriation of funds. Factors such as weaknesses in the transaction system, weak security policies, and lack of employee training influence the occurrence of fraud. Prevention strategies that can be implemented include early detection systems, strict security policies, employee training, and cooperation with authorities. It is hoped that the research findings can assist Sharia banks in enhancing transaction security, protecting customers, and minimizing fraud risks.

Determinants of Blockchain Technology Adoption in Agricultural Supply Chain

Blockchain technology adoption in agriculture can bring a win-win situation for all stakeholders in supply chain, which aids in the security of transactions and information in both forward and backward linkages in agricultural supply chain. Furthermore, blockchain platform benefits producers by enhancing their reputation, increasing their competitiveness and reducing fraud by eliminating undesirable practices in the supply chain. This research paper aims to analyze the determinants of blockchain technology adoption in agricultural supply chain. Grey relational analysis is employed to identify the determinant factors of blockchain technology adoption in the supply chain. The factors for blockchain technology adoption in supply chain are identified based on the literature review collected and focus group discussion with experts. Experts are insisted to rate the factors based on the five point likert scale. The findings revealed that data transparency, traceability, interoperability, data immutability and high value pricing, product type, organization trust, and government support can influence the blockchain adoption in supply chain. The findings conclude that priorities must be given to those influential factors which will help the organizational stakeholders in formulating suitable strategy for effectively implementing blockchain technology in the agricultural supply chain.. KEYWORDS :Blockchain technology, Adoption, Factors, Agricultural supply chain, Grey relational analysis.

Blockchain -Enhanced Dynamic Pricing Strategies in Book Redistribution: Balancing Profitability and Fairness

Dynamic pricing strategies in book redistribution is an online platform designed for the buying and reselling of second-hand books. The platform employs a dynamic pricing strategy facilitated through direct negotiation between sellers and buyers. By using technologies like React, Node.js, MongoDB, and Express, along with blockchain and smart contracts, its main aim is to balance profitability and fairness in book transactions. Specifically, the platform aims to enhance the efficiency and security of book redistribution processes and prevent fraud, including counterfeit books, to ensure trust in transactions. Each book on the platform is uniquely associated with a blockchain token, effectively mitigating the risks associated with fraud and counterfeit transactions. Smart contracts play a crucial role by enabling secure and transparent monetary transactions between parties involved in buying and selling books. Moreover, the platform fosters fair pricing through direct negotiation and provides a user-friendly interface that supports manual searches and negotiations. By integrating these technologies and functionalities, the platform not only enhances the overall efficiency of book redistribution but also promotes a secure environment that encourages trust and fairness among its users.

Association rules forecasting for the foreign exchange market

Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data mining to identify the good points of buying and selling in the foreign exchange market by utilizing technical indicators such as moving average convergence divergence (MACD) and the stochastic indicator to create association rules. The experimental findings indicate that the proposed model successfully generates strong association rules.

HybridChain: Fast, Accurate, and Secure Transaction Processing With Distributed Learning

HybridChain: Fast, Accurate, and Secure Transaction Processing With Distributed Learning

A Study on Enhancing the Securities on UPI Payments: Exploring the Measures and Technology for Secure Transactions

A Study on Enhancing the Securities on UPI Payments: Exploring the Measures and Technology for Secure Transactions

MAAS - Monitoring as a Service through Payment Gateway

Abstract: This paper describes the creation of a cloud-based Monitoring as a Service (MaaS) platform designed specifically for thorough and safe payment gateway monitoring. Scalability, strong security, and real-time transaction insights are provided by the MaaS project through the use of the ELK Stack (Elasticsearch, Logstash, Kibana). Flexibility in monitoring modules allows for the smooth integration of the ELK Stack for effective analysis and display of vital payment gateway data. An interface that is easy to use enables operators to keep an eye on transactions and spot possible problems. Further integration with internal security and fraud detection tools is made easier by thoroughly described APIs. The goal of this MaaS platform is to improve oversight capabilities for crucial payment gateway operations by providing a consolidated, secure monitoring solution. Future development will involve adding machine learning for anomaly detection and extending data source support to cover other payment channels.