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\"Examines international cooperation in European security from a transaction cost economics perspective. This book addresses the puzzle of how to approach differing institutional preferences. It argues that the reduction and limitation of transaction costs was the primary determinant of security preferences\"-- Provided by publisher.

Aim/Purpose: This study identifies the elements that influence intentions to purchase from the most popular Arabic online classifieds platform, OpenSooq.com. Background: Online purchasing has become popular among consumers in the past two decades, with perceived risk and trust playing key roles in consumers’ intention to purchase online. Methodology: A questionnaire survey was conducted of Internet users from three Jordanian districts to investigate how they used the OpenSooq platform in their e-commerce activities. In total, 202 usable responses were collected, and the data were analyzed with PLS-SEM for hypothesis testing and model validation. Contribution: Though online trading is increasingly popular, the factors that impact the behavior of consumers when purchasing high-value products have not been adequately investigated. Therefore, this study examined the factors affecting perceived risk, and the potential impact of privacy concerns on the perceived risk of online smartphone buyers. The study framework can help explore online behavior in various situations to ascertain similarities and differences and probe other aspects of online buying. Findings: Perceived risk negatively correlates with online purchasing behavior and trust. However, privacy concern and perceived risk, transaction security and trust, and trust and online purchasing behavior exhibited positive correlations. Recommendations for Practitioners: Customers can complete and retain online purchases in a range of settings illuminated in this study’s methods and procedures. Moreover, businesses can manage their IT arrangements to make Internet shopping more convenient and build processes for online shopping that allow for engagement, training, and ease of use, thus improving their customers’ online purchasing behavior. Recommendation for Researchers: Given the insight into the understanding and integration of variables including perceived risk, privacy issues, trust, transaction security, and online purchasing behavior, academics can build on the groundwork of this research paradigm to investigate underdeveloped countries, particularly Jordan, further. Impact on Society: Understanding the characteristics that influence online purchasing behavior can help countries realize the full potential of online shopping, particularly the benefits of safe, fast, and low-cost financial transactions without the need for an intermediary. Future Research: Future research can examine the link between online purchase intent, perceived risk, privacy concerns, trust, and transaction security to see if the findings of this study in Jordan can be applied to a broader context in other countries.

Enterprise Resource Planning (ERP) software is extensively used for the management of business processes. ERP offers a system of integrated applications with a shared central database. Storing all business-critical information in a central place raises various issues such as data integrity assurance and a single point of failure, which makes the database vulnerable. This paper investigates database and Blockchain integration, where the Blockchain network works in synchronization with the database system, and offers a mechanism to validate the transactions and ensure data integrity. Limited research exists on Blockchain-based solutions for the single point of failure in ERP. We established in our study that for concurrent access control and monitoring of ERP, private permissioned Blockchain using Proof of Elapsed Time consensus is more suitable. The study also investigated the bottleneck issue of transaction processing rates (TPR) of Blockchain consensus, specifically ERP’s TPR. The paper presents system architecture that integrates Blockchain with an ERP system using an application interface.

Digital payment channels have expanded quickly, reshaping transaction flows while opening new avenues for fraud. Isolation Forest (IF) remains attractive for unsupervised screening, yet deployments that rely on a fixed anomaly-score threshold deteriorate when traffic shifts or is actively manipulated. The authors present a Temporal-Attention Isolation Forest with Dynamic Calibration (TA-IFDC) that treats threshold selection as an adaptive component rather than a static post-processing step. The method monitors the evolving distribution of IF scores in streaming mode and updates the decision boundary online, while a lightweight temporal-attention module encodes short-range dependencies across consecutive transactions. Together, these pieces allow the detector to adjust to drift without sacrificing precision during stable periods.

To address the challenges of insufficient accuracy in identifying complex risks in blockchain financial transactions and high detection latency in high-concurrency scenarios, this paper constructs a multi-feature fusion dynamic security detection algorithm (MF-BFDSA) and designs a security enhancement mechanism. This algorithm innovatively integrates three types of features: transaction data (amount, frequency, etc.), user behavior, and blockchain network (node response latency, etc.). It uses Q-learning to dynamically adjust weights and combines an improved LSTM (using a forget gate to introduce feature correlation coefficients) with an optimized XGBoost algorithm to construct a risk identification model. This mechanism utilizes a five-layer architecture encompassing data collection and algorithm detection, forming a dual barrier of \"algorithm pre-detection + blockchain strong verification.\" Experiments were conducted on the Hyperledger Fabric 2.4 platform, using Kaggle credit card fraud (284,000 records), IEEE-CIS financial fraud (550,000 records), and 1.05 million simulated transaction data. Comparing traditional algorithms with state-of-the-art methods, the results show that MF-BFDSA achieves an accuracy of 99.82% (surpassing state-of-the-art by 1.2-3.5 percentage points), an F1 score of 98.94% (surpassing state-of-the-art by 2.8-4.2 percentage points), and an anomaly detection latency of only 0.08 seconds. It also maintains a throughput of 1200 TPS and a latency of 0.15 seconds on mid-range hardware. A blockchain system integrating this algorithm achieves a throughput of 1800 TPS (50% higher than the original system) at 2000 TPS concurrency, a CPU utilization of 72% (reduced by 13 percentage points), and a double-spend success rate of less than 0.001%, validating the effectiveness of the algorithm and mechanism.

Blockchain technology is currently recognized as the most potential new key technology, it can bring earth shaking changes, it is expected to trigger a new round of technological innovation and industrial change, and cause market attention. The purpose of this paper is to study the security of digital financial transactions based on blockchain technology. Firstly, the security of sdte is analyzed, and the DoS attacks that each role may launch, the attacks that a single role may send, and the attacks that can be launched by multiple roles in collusion are analyzed. It shows that sdte can resist these attacks and has strong security. Then, the related environment of the system test is described. Then, the performance test and analysis are carried out from the key security transmission, the execution of smart contract in the trusted environment SGX and the total running time. The experimental results show that the extra time consumed by using the k-nearest neighbor (KNN) algorithm to process data is less than 0.45s, At the same time, the additional cost brought by the system is also acceptable.

The rapid digitization of financial transactions has increased efficiency but also exposed systems to sophisticated fraud attempts, posing significant challenges to ensuring transaction security. Traditional fraud detection approaches, including rule-based systems and conventional machine learning models, struggle to adapt to evolving fraud patterns, resulting in high false-positive rates and limited scalability. State-of-the-art methods, while leveraging deep learning, face limitations such as computational overhead, lack of transparency, and vulnerability to adversarial attacks. This study explores the integration of lightweight blockchain technology and deep learning for robust fraud detection in financial transactions. Lightweight blockchain ensures transaction immutability, transparency, and tamper-proof data sharing across nodes, addressing trust and security challenges. Meanwhile, deep learning provides dynamic and adaptive detection capabilities, employing neural networks to identify anomalous patterns in complex datasets. By reducing the computational and storage demands of traditional blockchain systems, the lightweight approach facilitates real-time fraud detection in resource-constrained environments, such as mobile and IoT devices. Our model combines these components, ensuring data integrity through blockchain while enabling efficient pattern recognition via deep learning, creating a system capable of addressing scalability, energy efficiency, and adaptability. Preliminary experiments demonstrate the model's effectiveness in reducing false positives, enhancing detection rates, and achieving scalability without compromising security or performance, marking a significant step toward secure and efficient financial ecosystems.

Credit/debit cards are a ubiquitous form of payment at present. They offer a number of advantages over cash, including convenience, security, and fraud protection. In contrast, the inherent vulnerabilities of credit/debit cards and transaction methods have led many payment institutions to focus on strengthening the security of these electronic payment methods. Also, the increasing number of electronic payment transactions around the world have led to a corresponding increase in the amount of money lost due to fraud and cybercrime. This loss of money has a significant impact on businesses and consumers, and it necessitates the development of rigid and robust security designs for securing underlying electronic transaction methods. In this regard, this research introduces a novel geolocation-based multi-factor authentication method for improving the security of electronic payment transactions, especially ATM transactions. The proposed method leverages geolocation to verify the user’s identity and prevent fraudulent transactions. In addition, this research also proposes a novel design approach for further controlling the ownership of transactions in a convenient way (e.g., allowing users to deactivate/reactivate authentication at any time, block the card in case it is stolen or lost, and set up a withdrawal limit). Overall, this approach does not require any major modifications to the existing banking infrastructure, which would be an ideal solution for securing ATM transactions around the world.

The rapid growth of Internet banking has necessitated advanced systems for secure, real-time decision making. This paper introduces BankNet, a predictive analytics framework integrating big data tools and a BiLSTM neural network to deliver high-accuracy transaction analysis. BankNet achieves exceptional predictive performance, with a Root Mean Squared Error of 0.0159 and fraud detection accuracy of 98.5%, while efficiently handling data rates up to 1000 Mbps with minimal latency. By addressing critical challenges in fraud detection and operational efficiency, BankNet establishes itself as a robust decision support system for modern Internet banking. Its scalability and precision make it a transformative tool for enhancing security and trust in financial services.

This study aims to examine the behavioral intentions for using fintech based applications from the lens of Information technology quality and trust-based model. Data were collected from 275 respondents through an online questionnaire by using the purposive sampling method. PLS-SEM was performed to test the impact of trust and risk on fintech continuance intention, and results showed that trust impacted continuance more than perceived risk. Moreover, information, service and system quality significantly impacted trust and perceived risk. The study also highlighted perceived risk and trust as mediators, and results showed that trust partially mediated the relationship between system, information, service quality and fintech continuance intention. Contrarily, perceived risk mediated the relationships between service and system quality and fintech continuance intention. This study enhances the theoretical depth and adds to the existing literature by presenting transaction security and trust as moderators. Results suggested that transaction security moderated the association between trust and fintech continuance intention. Additionally, trust showed to have a moderating impact on the relationship between perceived risk and fintech continuance intention. The study adds to the body of knowledge by emphasizing the role of trust and perceived risk as antecedents of behavioral intention to use fintech-based services. The study provides novel and meaningful insights and guidance for banks, fintech service providers, and policymakers to achieve a desirable position in the users' minds and design better experiences for customers by making the platform more innovative, reliable, and trustworthy.