Transaction Security Research Articles

Analisis Perlindungan Hukum bagi Pelaku Usaha Penyedia Sistem Pembayaran Qris (Quick Response Indonesian Standard) berdasarkan Undang-Undang Nomor 8 Tahun 1999

Technological developments have changed the paradigm of payment systems from cash to non-cash, including in the form of electronic money (e-money) and QR-Code. QR-Code has become a popular payment method, especially with the implementation of Quick Response Indonesian Standard (QRIS). However, with technological advances also come new challenges related to legal protection for businesses that provide QRIS payment systems. This paper discusses the urgency of legal protection for business actors providing QRIS payment systems based on Law Number 8 Year 1999 concerning Consumer Protection. In this context, the concepts of freedom of contract, consent, sale and purchase, and consumer protection are elaborated to understand the rights and obligations of the parties in electronic payment transactions. The importance of the application of law as a preventive and repressive measure to prevent and overcome violations is also discussed. In addition, the relationship with Law Number 19 of 2016 concerning Electronic Information and Transactions and the implementation of Bank Indonesia regulations related to QRIS are highlighted in enforcing the rules and ensuring security and legal certainty in electronic payment transactions.

Barriers and prospects for the adoption of blockchain technology in property valuation

PurposeBlockchain is an emerging digital technology proposed and trialled among different built environment professions. The technology has been proposed to introduce transparency, security and trust in property transactions. Despite this proposition, few studies have analysed the barriers and prospects in property valuation, especially in markets plagued by low transparency and a lack of stakeholder trust. Using Nigeria as a case study, this study assesses the barriers and prospects for adopting blockchain technology in property valuation.Design/methodology/approachData was collected from 180 valuers practising in Nigeria through an online survey, and the data was analysed using mean score ranking and the chi-square (χ2) test of independence.FindingsFirstly, there was a low awareness of the application of blockchain technology and an association between the number of valuation jobs executed annually and awareness of the application of blockchain technology. The most important barriers revolved around the knowledge, technical know-how of blockchain and the cost of implementing such technology. The prospects for blockchain are very high as all identified prospects were considered important, with transparency being the most crucial factor for its adoption, followed by the monitoring activities in real time and the permanence in storing records.Research limitations/implicationsThis study's implications lie in the potential benefit of transparency identified for blockchain, which could act as a tool to introduce transparency into valuation industries that battle key issues surrounding transparency and trust. Furthermore, this study can be utilised by policymakers and property industry players in mapping strategies to adopt the beneficial use of blockchain as one among the suite of proptech tools disrupting the property valuation scene, in their practice. This also presents an opportunity to draw upon insights from this study to better prepare for using blockchain in property valuation.Originality/valueThis study appears to be the first to empirically assess barriers and prospects for blockchain in property valuation practice. It contributes to the literature by identifying key factors that will deter and/or promote the application of blockchain, an emerging and disruptive digital technology.

Adaptive machine learning models: Concepts for real-time financial fraud prevention in dynamic environments

Adaptive machine learning models are revolutionizing real-time financial fraud prevention in dynamic environments, offering unparalleled accuracy and responsiveness to evolving fraud patterns. Financial institutions face constant threats from increasingly sophisticated fraud schemes that adapt and change over time. Traditional static models often fall short in addressing these rapidly shifting threats, necessitating the adoption of adaptive machine learning techniques. Adaptive machine learning models are designed to evolve continuously by learning from new data and adjusting to emerging fraud patterns. These models employ advanced algorithms, such as reinforcement learning, online learning, and deep learning, to maintain their effectiveness in detecting and preventing fraud. Reinforcement learning algorithms optimize detection strategies by receiving feedback from their actions, continually improving their decision-making processes. Online learning algorithms update models incrementally as new transaction data becomes available, ensuring that the models remain current and responsive. One of the key strengths of adaptive machine learning models is their ability to process vast amounts of data in real time. By leveraging technologies such as neural networks and ensemble learning, these models can analyze complex datasets, identify subtle anomalies, and detect fraudulent activities with high precision. Real-time data processing capabilities enable immediate detection and response to suspicious transactions, significantly reducing the risk of financial losses. Adaptive models also incorporate anomaly detection techniques to identify deviations from normal transaction behavior. By constantly learning from the latest data, these models can recognize previously unseen fraud patterns, providing a robust defense against novel threats. Additionally, the integration of explainable AI (XAI) techniques ensures that the decision-making processes of these models are transparent and interpretable, fostering trust and compliance with regulatory requirements. Implementing adaptive machine learning models for real-time fraud prevention involves addressing challenges such as data quality, computational efficiency, and model interpretability. Financial institutions must ensure the availability of high-quality data and invest in robust computational infrastructure to support real-time processing. Furthermore, adopting explainable AI techniques enhances model transparency and regulatory compliance. In conclusion, adaptive machine learning models offer a dynamic and effective solution for real-time financial fraud prevention. By continuously learning and adapting to new data, these models provide a resilient defense against evolving fraud schemes, enhancing the security and integrity of financial transactions. This adaptive approach not only mitigates financial risks but also strengthens the overall trustworthiness of financial systems.

Leveraging advanced technologies in Supply Chain Risk Management (SCRM) to mitigate healthcare disruptions: A comprehensive review

The Healthcare sector is undeniably one of the most critical sectors in any given economy and disruptions in this industry have dire consequences that affect patient care, the efficiency of healthcare systems and by far public health. This publication seeks to analyze and discuss the vital role of supply chain risk management in addressing disruptions, becoming resilient and ensuring that healthcare services function effectively. The pandemic (COVID-19) brought to fore just how critical it is to have a robust and agile system for supply chain risk management in healthcare, the sudden and unprecedented surge in demand for essential medical equipment like personal protective equipment (PPE) left millions of people at risk thereby jeopardizing their safety and health. This crisis echoed the urgent need for efficient and effective supply chain risk management strategies to mitigate such risks and ensure the availability of vital medical supplies. This review paper will be sharing different strategies and actionable tips to develop contingency plans, identify, reduce potential disruptions and recommended practices for risk management in the healthcare supply chain. This will highlight key areas such as inventory management and stockpiling, which focuses on maintaining safety stock levels to buffer against sudden disruptions, using advanced analytics to forecast demand and manage inventory levels. It will also examine risk assessment and identification by pinpointing potential attacks, threats, and vulnerabilities within the healthcare supply chain through thorough risk assessments. Additionally, it will cover supplier base diversification, emphasizing the importance of engaging multiple suppliers to reduce dependency on a single source and enhance supply chain resilience by balancing global and local suppliers to mitigate geographic disruption risks. Furthermore, it will explore the integration of technology, leveraging innovative solutions like blockchain for enhanced transparency, security, and traceability of supply chain transactions.

Promoting financial inclusion for SMEs: Leveraging AI and data analytics in the banking sector

Financial inclusion is crucial for the growth and sustainability of small and medium-sized enterprises (SMEs), which are significant contributors to economic development and job creation. However, traditional banking models often fall short in serving the unique needs of SMEs due to perceived high risks and insufficient credit history. Leveraging artificial intelligence (AI) and data analytics, the banking sector can transform its approach to financial inclusion, offering tailored financial products and services that cater specifically to SMEs. AI and data analytics enable banks to analyze vast amounts of data from diverse sources, including transactional histories, social media activity, and market trends. This holistic view of an SME's financial health and business potential allows for more accurate risk assessment and credit scoring. Machine learning algorithms can identify patterns and predict creditworthiness, enabling banks to extend credit to SMEs that may have been overlooked by traditional models. Additionally, AI-driven insights facilitate the development of customized financial products, such as flexible loan terms and dynamic interest rates, that align with the cash flow cycles and operational realities of SMEs. Automated processes and AI-powered chatbots enhance customer service, providing SMEs with timely support and financial advice, thereby improving their banking experience. Data analytics also play a critical role in fraud detection and prevention, ensuring the security of transactions and building trust among SME clients. By continuously monitoring and analyzing transaction data, banks can quickly identify and mitigate fraudulent activities, protecting SMEs from financial losses. Moreover, AI and data analytics support the creation of financial literacy programs tailored to the specific needs of SMEs, empowering business owners with the knowledge and tools to make informed financial decisions. This educational aspect is vital in fostering a sustainable financial ecosystem for SMEs. In conclusion, the integration of AI and data analytics in the banking sector holds significant promise for promoting financial inclusion among SMEs. By providing more accessible, customized, and secure financial services, banks can support the growth and success of SMEs, ultimately contributing to broader economic development and financial stability.

Pengaruh Faktor Kepercayaan, Keamanan Transaksi dan Kualitas Pelayanan Terhadap Keputusan Pembelian Online pada Aplikasi Shopee Bagi Pengguna di Kota Depok

. One of the most popular types of e-commerce today is the marketplace which is widely used by people to run businesses and helps them shop online to meet their needs. Shopee is one of them because it is often visited via the website. This research uses a quantitative approach method by utilizing primary data as a data source obtained from the results of a questionnaire in the form of a Google form. The analysis techniques used are data quality tests (validity and reliability tests), classical assumption tests (normality test, heteroscedasticity, multicollinearity and multiple linear regression), hypothesis testing (partial t test). ) and f test (simultaneous) and coefficient of determination test (R2). Data processing in this research uses SPSS version 25. Based on research, it shows that trust has a partial effect on purchasing decisions with a sig. 0.008. Transaction security does not partially influence purchasing decisions with a sig value. 0.296. Service quality partially influences purchasing decisions with a sig value. 0,000. So trust, transaction security and service quality influence decisions with a sig value of 0.015.

A Novel Blockchain-based Responsible Recommendation System for Service Process Creation and Recommendation

Service composition platforms play a crucial role in creating personalized service processes. Challenges, including the risk of tampering with service data during service invocation and the potential single point of failure in centralized service registration centers, hinder the efficient and responsible creation of service processes. This paper presents a novel framework called Context-Aware Responsible Service Process Creation and Recommendation (SPCR-CA), which incorporates blockchain, Recurrent Neural Networks (RNNs), and a Skip-Gram model holistically to enhance the security, efficiency, and quality of service process creation and recommendation. Specifically, the blockchain establishes a trusted service provision environment, ensuring transparent and secure transactions between services and mitigating the risk of tampering. The RNN trains responsible service processes, contextualizing service components and producing coherent recommendations of linkage components. The Skip-Gram model trains responsible user-service process records, generating semantic vectors that facilitate the recommendation of similar service processes to users. Experiments using the Programmable-Web dataset demonstrate the superiority of the SPCR-CA framework to existing benchmarks in precision and recall. The proposed framework enhances the reliability, efficiency, and quality of service process creation and recommendation, enabling users to create responsible and tailored service processes. The SPCR-CA framework offers promising potential to provide users with secure and user-centric service creation and recommendation capabilities.

Fort-Trust: Safeguarding online transaction by machine learning

In the digital age, the safety of online transactions has become an important situation for both customers and groups. The increasing sophistication of cyber-attacks necessitates the development of robust and sensible protection mechanisms. "Citadel-believe: Safeguarding online Transactions through device gaining knowledge of" proposes a complete solution leveraging machine studying techniques to decorate the safety of online financial transactions. This method aims to stumble on and mitigate fraudulent activities in real time, presenting a further layer of safety past conventional techniques. e-commerce has completely changed the way people shop, it has also made people more susceptible to online transaction fraud. This problem is addressed by the innovative framework Fort-Trust, which uses the XGBoost algorithm for fraud detection. Fort-Trust incorporates feature correlation analysis to solve a prevalent problem in this field: imbalanced datasets. This strategy aims to maximize detection accuracy while minimizing false positives. The high precision rate that XGBoost delivers, according to the results, is essential for lowering financial losses and increasing user confidence. All things considered, Fort-Trust strengthens the security and dependability of online transactions by providing a strong and useful solution for real-world fraud detection

Application of Blockchain Technology in Real Estate Transactions Enhancing Security and Efficiency

Blockchain technology, an innovative distributed ledger technology, has gained widespread application across various industries in recent years due to its decentralization, transparency, immutability, and traceability. This paper explores the application of blockchain technology in real estate transactions by analyzing four representative cases: Propy, PropertyClub, Ubitquity, and RealBlocks. The analysis demonstrates the significant advantages of blockchain in enhancing transaction security, improving efficiency, and reducing costs. The study reveals that Propy's platform, leveraging smart contracts, has automated global real estate transactions, reducing average transaction time by 32.4% and costs by 27.8%. PropertyClub utilizes blockchain to record and verify transaction information, ensuring transparency and security while achieving a 21.3% cost reduction. Ubitquity's blockchain-based record management system reduces record management time by 45.6% and increases data accuracy to 98.9%. RealBlocks has digitized and securitized real estate investments, enhancing liquidity by 52.1% and reducing investment costs by 20.5%. Future trends indicate a deep integration of blockchain with smart contracts and IoT, as well as the digitization and securitization of real estate assets. However, the widespread application of blockchain technology faces multiple challenges, including technical bottlenecks, legal and regulatory issues, market acceptance, and data privacy concerns. This paper further elaborates on these trends and challenges, proposing potential solutions. In conclusion, while blockchain technology has a promising future in real estate transactions, its widespread adoption requires concerted efforts from all stakeholders.

Ethereum blockchain-based hierarchical cooperative power sharing in a group of microgrids

ABSTRACT Cooperative power-sharing (CPS) is one of the cutting-edge power management strategies the power system planners contemplate transforming from its traditional structure to a more decentralized framework as the globe prepares for a future with low carbon emissions. Cooperative microgrids are considered one of the future power system configurations where individual microgrids (MGs) can support each other through CPS. This paper introduces a decentralized energy market (DEM) to operate CPS hierarchically between MGs, groups of MGs, and the utility grid. The security of power-sharing transactions and the privacy of MGs pose challenges in terms of confidentiality and data integrity. To deal with these issues, a DEM framework based on distributed ledger technology (DLT) is proposed to ensure power transactions between cooperative MGs with a guarantee. A scheme is built on the Ethereum testnet to estimate the performance and validation of the proposed smart contracts. A case study with actual data for a group of four MGs is presented to validate the effectiveness of the proposed framework. The proposed blockchain approach results in money transactions resulting in MG1 receiving $485; while MG2 gets $525, MG3 and MG4 generates a revenue of $435 and $362.5 respectively. The numerical results demonstrate that MGs can actively share power with peers and achieve economic benefits.

Integrating Blockchain as a Service (BaaS) for BioEnerNet in the Internet of Things (IoT) landscape

The BioEnerNet project aims to revolutionize the bio energy sector by integrating blockchain technology through a Blockchain as a Service (BaaS) model. This paper explores the potential of implementing a BaaS framework in BioEnerNet to create a sustainable bio energy network. By leveraging blockchain technology, BioEnerNet can enhance transparency, security, and efficiency in energy transactions, paving the way for a more sustainable and decentralized bio energy ecosystem. This abstract sets the stage for a detailed examination of the BioEnerNet - BaaS framework and its implications for the future of bio energy networks.

Benefits of Artificial Intelligence and its Innovation in Organizations

This article reviews the advances, benefits and contributions of artificial intelligence to business organizations, without ignoring the need for skilled human labor and talent that companies have always required and which is essential for the economic development of a company. The implementation of legal frameworks and regulations that will in turn address issues such as organizational privacy, cybersecurity and liability are important aspects to be addressed in this article. Thus, this paper focuses on the compilation of scientific articles, books and other texts in which important topics were evidenced such as (i). - Innovation and invention, (ii). - Big Data, (iii). -Implementation of artificial intelligence in organizations, and (iv). -legality and responsibility of decisions, likewise, it examines the legal challenges of AI and highlights that smart contracts are a technological innovation that allows secure, fast and low-risk transactions using blockchain technology, this is why the literature review aims to explore how technology, artificial intelligence and communication tools can facilitate the work of the population, decreasing the time spent on some activities

Smart Tender Management System Using Blockchain in Python

-Blockchain technology has been adopted by a number of industries, including government agencies, financial services, healthcare, and agriculture, to improve their operations in the quickly changing modern world. But the industry for tenders hasn't yet completely realized all of its advantages. Due to problems including bias, bad record- keeping, lack of transparency, vulnerability to hackers, and data tampering, inadequate tender management procedures can cause large losses. We suggest using blockchain technology to ensure improved security and effectiveness in tender management as a solution to these problems. We can create a secure transaction management system that protects important documents and transactions related to the tendering process by utilizing encryption and the immutable nature of blockchain. By safely storing bid proposals, firm profiles, historical data, tender documents, and other information, this system fosters transparency and integrity. The Admin and Company modules are the two main components of the Smart Tender Management System. Administrators can oversee applications, handle tenders, and keep an eye on transaction logs with the help of the admin module. Companies can register, submit proposals, and monitor the status of their applications using the Company module. The solution improves security, lowers expenses, and simplifies tender processing by doing away with intermediaries and utilizing strong encryption techniques. This fosters an easy- to-use and effective experience for all parties involved KeyWords: Block chain Technology, Encryption, Smart Contracts, Tender Management.

Transforming Education with Blockchain Technology: Enhancing Security, Transparency, and Credential Verification

Blockchain technology has emerged as a powerful tool for enhancing transparency, security, efficiency, and inclusivity in record-keeping, credentialing, and educational transactions in the digital age. This paper aims to highlight the potential benefits and challenges of integrating blockchain technology into educational environments, emphasizing security, transparency, and credential verification as key areas of focus. Blockchain technology offers transformative potential in the field of education, particularly in credential and certification processes, providing secure, transparent, and globally accessible solutions for verifying academic achievements and professional skills. As blockchain technology continues to evolve, further advancements and case studies specific to enhancing privacy in educational processes are expected to emerge, addressing the unique challenges and regulatory requirements associated with handling sensitive educational data. Further R&D in blockchain technology within the education sector holds promise for addressing existing challenges, unlocking new opportunities, and advancing the capabilities of educational institutions worldwide.

Performability of Valuation Adjustment Mechanism: Value Judgements at the Normative Level

On the occasion of a valuation adjustment mechanism, the main conflict of profit occurs between the shareholders of the repurchasable shares and the creditors of the target company. Since the introduction of the Minutes of the national courts' civil and commercial trial work conference (Minutes), the academic debate on the valuation adjustment mechanism has centred on the enforceability of the share repurchase clause therein. If the dispute is dealt with according to the literal meaning of Article 5(2) of the Minutes, it is not only detrimental to the maintenance of transaction security, but also systematically in conflict with Article 579 of the Civil Code of the Peoples Republic of China (hereinafter referred to as Civil Code) systematically. Therefore, by means of systemic interpretation, Article 5(2) of the Minutes can be limited to the case of insufficient distributable profits in order to achieve the harmonisation of the normative system.

The Impact of Implementing Blockchain Technology in Learning on Data Security and Integrity

The application of blockchain technology in learning has great potential to improve data security and integrity. Because blockchain is a decentralized technology that uses block chains to record and secure transactions in a transparent and permanent manner, it offers an excellent solution to address security and data integrity issues. This research was conducted with the aim of improving the security and integrity of student data. Student data can be stored in a distributed manner with blockchain. No one can change or delete it without their permission. This goal also includes increasing transparency and accountability of the learning process to ensure that the data used in the learning process is accurate data. The method used in this research is a quantitative method. This method is a way of collecting numerical data that can be tested. Data was collected through distributing questionnaires addressed to students. Furthermore, the data that has been collected from the results of distributing the questionnaire will be accessible in Excel format which can then be processed using SPSS. From this research carried out, researchers can obtain research results that blockchain technology is used to protect educational data by locking the learning history of the learning management system (LMS), so that assessments can be carried out effectively and allow all parties involved to access and verify information safely while ensuring data integrity and validity. Based on the results of this research, it can be concluded that the application of blockchain technology can help improve the security and integrity of student data, such as academic, financial and personal data, in the learning context. By using blockchain, students have complete control over their data and ensure that bad people cannot misuse or access it. Apart from that, blockchain technology can also increase transparency and accountability in the classroom.

Cyber Security Challenges: Safeguarding Data in the Digital Era

The Internet is one of the fastest-growing areas of technical infrastructure development. In today’s business environment, disruptive technologies such as cloud computing, social computing, and next-generation mobile computing are fundamentally changing how organizations utilize information technology for sharing information and conducting commerce online. Today more than 80 percent of total commercial transactions are done online, so this field required a high quality of security for transparent and best transactions. The scope of Cyber Security extends not only to the security of IT systems within the enterprise, but also to the broader digital networks upon which they rely including cyber space itself and critical infrastructures. Cyber Security plays an important role in the development of information technology as well as Internet services. Our attention is usually drawn on “Cyber Security” when we hear about “Cyber Crimes”. Our first thought on “National Cyber Security” therefore starts on how good is our infrastructure for handling “Cyber Crimes”. This paper focus on cyber laws and security emerging trends, issues and challenges while adopting new technologies such as mobile computing, e-commerce, and social networking.

An Intelligent Financial Fraud Detection Model Using Knowledge Graph-Integrated Deep Neural Network

Financial fraud detection has been an urgent technical demand in cyberspace. It highly relies on clear extraction and deep representation toward complex relationships inside financial social networks. As consequence, this study combines both knowledge graph and deep learning to deal with such issue. Thus, an intelligent financial fraud detection model based on knowledge graph guidance and deep neural network is proposed in this paper. First, a new knowledge graph based on financially related systems is constructed, which includes multiple entities and relationships. Then, an adversarial learning-based neural network structure is formulated to extract financial attributes. Finally, the detection results can be output according to the extracted factors. Empirically, the proposal is implemented on a real-world dataset for performance evaluation. The experimental results show that it has higher accuracy and effectiveness compared to traditional fraud detection methods. The proposed detection model can not only identify known fraudulent behaviors, but also predict potential fraud patterns based on consumer habits, thereby improving the security and reliability of financial transactions. It can also update the knowledge graph in real-time, enabling it to cope with emerging fraud methods and variants.

Blockchain Technology's Impact on Mineral Resources Business Model Innovation

Mining corporations are rethinking blockchain because technology improves transparency, efficiency, and security in natural resource transactions. Blockchain and mineral resource transactions require further study. The effects of blockchain on mining value generation, delivery, and capture are examined. Mining management affects blockchain business model innovation.

Integrating fully homomorphic encryption to enhance the security of blockchain applications

Blockchain has been widely used for secure transactions among untrusted parties, but the current design of blockchain does not provide sufficient privacy and security for the data on the chain, limiting its application in sensitive information scenarios. To address this problem, we propose integrating fully homomorphic encryption (FHE) to enhance the security of blockchain applications, which can extend the application scope of blockchain and improve the privacy and security of blockchain by the features of FHE. Our scheme classifies FHE into those supporting polynomial and non-polynomial operations and introduces the concept of ciphertext computation conversion into Ethereum, enabling conversion between different ciphertext computation types. Moreover, we analyse the security and correctness to explain the feasibility and availability of the scheme. We carry out comparative experiments using different open-source libraries for fully homomorphic encryption and the time performance evaluation of the ciphertext computation conversion under different thread counts. The experiment results demonstrate the efficiency and usability of our scheme.