Past Transactions Research Articles

Utilizing Blockchain Technology for Enhanced Cybersecurity in Financial Transactions

The blockchain technology has become a revolutionary new idea that has a lot of promise to make financial activities safer. This essay looks at how blockchain can be used to make financial systems safer by focusing on how it is independent and can't be changed. When it comes to standard financial transactions, monopoly comes with risks like single points of failure and being open to hacking. Blockchain solves these problems by spreading transaction records among a network of nodes, with each node keeping a log that is always up to date. This autonomous design makes sure that no one organization has access to all past transactions. This lowers the chance of scam or changing data without permission. Also, the fact that blockchain records can't be changed makes sure that financial deals are open and trustworthy. Once a transaction is recorded, it can't be changed after the fact without agreement from everyone in the network. This makes blockchain a strong tool for making financial processes auditable and accountable. Adding smart contracts to blockchain makes it even more useful for keeping financial activities safe. Smart contracts are deals that run themselves and have rules and conditions that are written into the blockchain network. These contracts handle tasks and make sure they are done correctly, which cuts down on the need for middlemen and the chance of mistakes made by people when transactions are settled. Blockchain's cryptographic methods also offer high-level security features like digital signatures and cryptographic hashing. These systems make sure that players are who they say they are and that data is correct throughout the entire transaction process. This makes the system more resistant to hacking and illegal access. This essay looks at examples and case studies of how blockchain has been used successfully in the financial sector. It shows how it can stop fraud, lower trade costs, and make operations run more smoothly.

MOVIEMATE: MOVIE RECOMMENDATION SYSTEM

Abstract— Nowadays, recommendation systems have revolutionized the way we search for things of interest. These systems employ an information filtering approach to anticipate user preferences. Commonly applied in areas such as books, news, articles, music, videos, and movies, recommendation systems play a pivotal role. In this study, we introduce MOVIEMATE, a movie recommendation system. Using content-based approach, MOVIEMATE examines information provided by users to recommend movies customized to their individual preferences. MOVIEMATE streamlines the movie selection process, leveraging the collective movie experiences of other users to efficiently deliver personalized recommendations. Developed in Python using Jupyter Notebook and Streamlit, this system harnesses various types of user data, item information, and past transactions stored in dedicated databases to generate recommendations. Navigating through these recommendations, users can effortlessly discover movies that align with their preferences. Keywords – recommendation system, recommender system, movies, Content-based filtering

Upselling through upgrading: The role of employee engagement

Although the literature on upselling has investigated different aspects of inducing upselling, there are limited discussions of the effect of past transactions on customers’ acceptance of upsells. Building on the expectation-confirmation and reference dependence theories, this study utilized mixed-effect regression analysis to explore the extent to which customers’ acceptance of upsells changes in response to their prior experience of unexpected gains via free upgrades. The results show that customers accept and spend more on upselling offers when they have experienced a free upgrade in a past transaction and that the effect of the free upgrade on upselling is positively moderated by the level of employee engagement.

Pelatihan Dan Pendampingan Laporan Keuangan: Upaya Mendorong UMKM Naik Kelas

The vision of the South Tangerang City Government is to make MSMEs a prima donna not only in South Tangerang City, experiencing problems including the limitations of MSMEs in making financial reports so that an overview of the financial performance and wealth of MSMEs is not obtained. An overview of performance through financial reports is a prerequisite for applying for MSME financing. Training and assistance in the preparation of financial reports is an effort by the South Tangerang City Government and PKN STAN so that MSMEs are promoted through an understanding of the financial performance of the business being managed and the opportunity to obtain financing. Training on preparing Financial Reports is carried out in masse for all MSMEs and intensively with division into groups. Meanwhile, assistance is provided intensively and continuously for two MSMEs, Idea Mulya and Cilok Djoedes. Obstacles such as the readiness of MSMEs to prepare financial transaction data still arise during mentoring. In addition, problems related to the SIAPIK application still arise, such as difficulties in recording past transactions, determining the final consignment balance value, and recording product sales in retail. However, the training and mentoring went smoothly and provided positive benefits for MSMEs in their efforts to realize MSMEs promotion to class.

Enhancing fraud detection in banking by integration of graph databases with machine learning

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions. This model uses advanced methods for anomalies, behaviors, and patterns to analyze past transactions and user behavior almost immediately. We provide an in-depth methodology for evaluating fraud detection systems based on parameters such as Accuracy Recall rate and False positive rate ROC curves. The findings can be used by financial institutions to develop and enhance fraud detection strategies as they demonstrate the effectiveness and reliability of the proposed approach. This study emphasizes the critical role that innovative technologies play in safeguarding the financial sector from the ever-changing strategies of fraudsters while also enhancing banking security.•This paper aims to implement the detection of fraudulent transactions using a state-of-the-art Graph Database approach.•The relational graph of features in the dataset used is modelled using Neo4J as a graph database.•Applying JSON features from the exported graph to various Machine Learning models, giving effective outcomes.

Fake Credit Transaction Detection Using Machine Learning

In today's digital age, detecting and preventing fraudulent credit card transactions is of paramount importance. As technology advances, criminal methods are also becoming more sophisticated. The use of machine learning in credit card fraud detection and mitigation has grown significantly. In this research study, a novel method for identifying fraudulent credit card transactions with machine learning algorithms is presented. The proposed system leverages past transaction data and various characteristics associated with each transaction, such as location, transaction amount, and time, to build a predictive model. These models are trained to recognize patterns that point to fraudulent activity using supervised learning methods like random forests and support vector machines. Several metrics, including accuracy, precision, recall, and F1 score, are used to assess the performance of the model. According to experimental data, the suggested method works better than conventional rule-based fraud detection systems and achieves high accuracy. The system can effectively detect fraudulent credit card transactions while minimizing false positives. Machine learning improves the security of credit card transactions by detecting fraud in real time. In summary, this study advances the field of credit card fraud detection by using machine learning algorithms to counteract the constantly changing tactics used by fraudsters.

Retail operations strategy for improved customer experience: a better response to crises such as COVID-19 pandemic

Purpose Discount grocery stores (DGSs) are attractive food supply chain (FSC) channels because many cost-conscious Indians use them for monthly needs. Despite capacity, DGSs must address customer concerns about store crowd densities and improve their COVID-19 preparedness. The purpose of this study is to learn how retail operations strategies can improve customer experience and how stores can benefit. Design/methodology/approach The study looked at a case study where retail operations are run more efficiently, and the customer experience is enhanced by standardizing and customizing customer transactions. The potential benefits that customers and retailers might anticipate are then statistically verified. Next, the potential benefits were examined to determine which ones from customers’ and retailers’ views should be prioritized to increase satisfaction. Findings The case situation analysis in the study demonstrates how DGSs can improve their retail operations to reduce customer wait times and provide greater convenience. The study also provides practitioners with potential benefits to pursue from the perspectives of retailers, customers and both retailers and customers. Research limitations/implications This study requires many past transactions and can be considered an extension of the current study, so it does not capture floor space and capacity improvements. Practical implications This research can help FSC retailers compete with upstream supply chain partners and customers in omnichannel retailing. By improving DGS retailer capacity and customer experience, this study can benefit all FSC stakeholders. Originality/value Although there are numerous potential benefits that practitioners can pursue, the current study suggests that practitioners focus on those that can improve retailer and customer satisfaction.

Insurtech in Europe: identifying the top investment priorities for driving innovation

The purpose of this study is to determine the essential indicators to improve insurtech systems and select the most critical alternative to increase insurtech-based investments in European countries. A novel fuzzy decision-making model is generated by integrating entropy and additive ratio assessment (ARAS) techniques with spherical fuzzy sets. First, the indicators are weighted using spherical fuzzy entropy. Then, the alternatives are ranked using spherical fuzzy ARAS. The alternatives are also ranked with the spherical fuzzy technique for order of preference by similarity to the ideal solution methodology. The main contribution of this study is that it would help investors to take the right actions to increase the performance of insurtech investments without incurring high costs. Another important novelty is that a new fuzzy decision-making model is proposed to solve this problem. The results of the two models are quite similar, proving the validity and coherency of the findings. It is found that pricing is the most critical factor that affects the performance of insurtech investments. Insurtech companies are required to make accurate pricing by conducting risk analyses to increase their profits and minimize their risks. Additionally, according to the ranking results, big data are the most appropriate way to improve the performance of insurtech investments in Europe. Big data analytics helps companies learn more about the behavior of their customers. By analyzing data about their customers’ past transactions, companies can provide more convenient services to them. This would increase customer satisfaction and enable companies to achieve long-term customer loyalty.

Credit Card Fraud Detection Using AI (Python)

Credit card fraud poses a formidable obstacle in the financial sector, resulting in considerable monetary damages for both individuals and institutions. The necessity for efficient fraud detection systems has become paramount due to the rising number of online transactions and the advancement of fraudulent methods. Machine learning methods have demonstrated potential in tackling this issue by utilizing past transaction data to detect fraudulent behavior. This study provides a thorough examination and evaluation of different machine learning techniques used in the detection of credit card fraud. The aim of this study is to assess and compare the efficacy of several machine learning algorithms in identifying fraudulent credit card transactions. The dataset utilized for experimentation is acquired from a prominent financial institution, including of both authentic and deceitful transactions. The dataset has been preprocessed to address missing values, outliers, and feature scaling. A variety of machine learning algorithms, such as “logistic regression, decision trees, random forests, support vector machines (SVM), and artificial neural networks (ANN),” are utilized and trained on the preprocessed information. The evaluation of each method is conducted using criteria like as “accuracy, precision, recall, and F1-score. In addition, several evaluation methods, such as k-fold cross-validation”, are used to assure the reliability of the findings. The empirical findings suggest that machine learning algorithms has the capability to accurately identify fraudulent credit card transactions. The algorithms demonstrate varying performance across different parameters, with certain algorithms displaying higher accuracy but worse precision or recall. The “Support Vector Machine (SVM)” algorithm gets the maximum accuracy rate of 98%, while the “Artificial Neural Network (ANN)” model displays the optimal balance between precision and recall..

A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities

Although the most widely studied datasets in fraud-detection systems belong to the banking sector, the aviation industry is susceptible to fraud activities that seriously harm airline companies. Therefore, big airline companies have started to purchase or develop their own fraud-detection systems in order to prevent their financial loss and prestige decline. Chronological order and temporal flow are intrinsically of high importance in fraud detection in the banking sector as well as in airline sale channels. Therefore, the transactions in the datasets used in fraud-detection systems should be evaluated not only according to the information they contain but also according to the past transactions they are linked to. One of the best ways to raise awareness about the connected past transactions to the fraud-detection system is to profile the data fields whose historical data is important and dynamically place these profiles on each transaction. In this study, we first draw the baseline, i.e., the first touch in this field, for fraud detection in aviation and then introduce a novel multi-modal profiling mechanism based on deep learning for the detection of fraudulent airline ticket activities. We achieved great success by feeding the new features obtained from those profiles into a deep neural network that is fine-tuned by adjusting the well-known hyperparameters regarding the aviation data. Thanks to the combination of profiling and deep learning, the F1 score of the proposed system reaches up to 89.3% and 93.2% in terms of quantity-based success and cost-based success, respectively.

A Trusted Reputation Management Scheme for Cross-Chain Transactions

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node’s reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.

A Reliable Energy Trading Strategy in Intelligent Microgrids Using Deep Reinforcement Learning

A microgrid is a system that incorporates various decentralized power sources and manages the distribution of electricity. In microgrid, prosumers play a significant role in trading electricity, and it is essential to establish an effective power trading mechanism to incentivize their active participation. This study proposes a power trading mechanism for prosumers in microgrids, which incorporates blockchain technology to protect their rights and interests. The mechanism employs reinforcement learning to optimize trading decisions and develops a reputation mechanism to evaluate prosumers' trustworthiness based on their past transactions. The proposed strategy encourages prosumers to conduct more transactions with honest peers by introducing reputation value rewards into the benefit function. The simulation results indicate that the proposed strategy outperforms traditional approaches and reputation value significantly impacts prosumers' utility. Overall, the proposed strategy aims to promote honest transactions and enhance prosumers' participation in microgrid power transactions.

Supply chain traceability and counterfeit detection of COVID-19 vaccines using novel blockchain-based Vacledger system

We propose a novel framework, Vacledger, for supply chain traceability and counterfeit detection of COVID-19 vaccines using a blockchain network. It includes four smart contracts on a private-permissioned blockchain network for supply chain traceability and counterfeit detection of COVID-19 vaccine, more specifically to (i) handle the rules and regulations of vaccine importing countries and provide authorization for cross the borders (regulatory compliance and border authorization smart contract), (ii) register new and imported vaccines in the Vacledger system (vaccine registration smart contract), (iii) find the number of stocks that have arrived in the Vacledger system (stock accumulation smart contract), and (iv) identify the exact location of the stock (location tracing update smart contract). Our results show that the proposed system keeps track of all activities, events, transactions, and all other past transactions, permanently stored in an immutable Vacledger connected to decentralized peer-to-peer file systems. We observe no algorithm complexity differences between the proposed Vacledger system and existing supply chain frameworks based on different blockchain types. In addition, based on four use cases, we estimate our model’s overall gasoline cost (transaction or gas price). The Vacledger system empowers distribution companies to manage their supply chain operations effectively and securely using an in-network, permissioned distributed network. This study employs the COVID-19 vaccine supply chain (the healthcare industry) to demonstrate how the proposed Vacledger system operates. Despite this, our proposed approach might be implemented in other supply chain industries, such as the food industry, energy trading, and commodity transactions.

거래량과 가격정보에 따른 모멘텀과 주식수익률의 관계에 관한 연구

According to behavioral finance, investors make investment decisions by recognizing past information as useful information. Investment decisions based on past information cause an overreaction to stock prices. According to previous studies on the Korean stock market, momentum based on trading volume and price information has a significant positive influence on stock returns. In this study, the Korean stock market is divided into the securities stock market and the KOSDAQ market to investigate which market the existing results are due to. Furthermore, an empirical analysis is conducted on the irrational behavior of investors based on transaction volume and price information. The research method uses univariate portfolio analysis and multivariate portfolio analysis and divides the decile portfolio and the conditional 5th quintile portfolio based on the momentum substitution considering the transaction volume. It is checked whether the momentum based on past transaction volume and price information lasts for the next six months. As a result, in the securities market, it is confirmed that the momentum according to transaction volume and price information has a significant positive effect on stock returns, and these results are maintained even after considering firm characteristics. On the other hand, it is stated that momentum according to transaction volume and price information does not occur in the KOSDAQ market. It shows that the existing results are due to the securities stock market rather than the KOSDAQ market.

Strategic rearrangement of retail shelf space allocations: Using data insights to encourage impulse buying

Brick-and-mortar retailers are struggling to compete with online retailers, who increasingly use customer-generated data to provide highly customized experiences for their patrons to drive impulse purchases. Although brick-and-mortar retailers cannot offer such customization, they must identify innovative ways to transform their own point-of-sale transaction data into increased customer impulse buying. We propose a shelf allocation-relocation scheme for rearranging storewide product allocations over time to maximize impulse buying behavior. The proposed method provides a roadmap for optimally allocating shelf space when routine rearrangements of a retail store take place per store policy. To the best of our knowledge, this represents one of the first periodic storewide rearrangement studies to use customer tendencies with the current shelf arrangement as the basis for the subsequent one. We also introduce a novel approach for incorporating past transaction data insights of cross-selling elasticity into the Shelf Space Allocation Problem (SSAP) to encourage long-term new business creation through impulse purchases. The method applies insights from association rule mining to group highly affine and profitable product pairs, optimize the assignment of departments to store aisles, and determine the optimal within-aisle space allocations for the products of each department. Our major finding is that, based on the potential of generating multiple forms of impulse purchasing (visibility-driven purchasing, present-shelf impulse, and past-aisle impulse), our strategic rearrangement technique consistently outperforms visually rearranging shelf space allocations and several existing allocation techniques that utilize multi-level association rule mining, while, in many instances, exceeding the profit potential of a more traditional unchanged (one-time optimal) shelf space arrangement, depending on the nature of a retailer’s target market. Our results also highlight the importance of a retailer selecting the most appropriate shelf space rearrangement strategy that fits the characteristics of its customers, especially their discretionary income level and their familiarity with the store layout.

ПРАВОВІ АСПЕКТИ ЦИФРОВІЗАЦІЇ МІЖНАРОДНОЇ ТОРГІВЛІ ШЛЯХОМ УПРОВАДЖЕННЯ ТЕХНОЛОГІЇ БЛОКЧЕЙН (ДОСВІД ДЛЯ УКРАЇНИ)

The article is devoted to the study of the peculiarities of digitalization of international trade through the introduction of blockchain technology and the identification of issues that require legal regulation for the implementation of the relevant process in Ukraine. It is emphasized that digitalization of international trade through the introduction of blockchain technology opens up opportunities for optimization of administrative trade procedures and stimulation of foreign trade activity, in particular, it allows modernizing and optimizing the work of “single windows” (including certification and customs clearance); cross-border data exchange between government agencies or authorities and economic entities; payment transfer system, etc. In turn, the use of smart contracts, built on the appropriate technology, can automate the compliance of stakeholders with various contractual obligations; and data (in the blockchain) — is a stream of reliable information about past transactions, as they remain unchanged after entry. This provides greater transparency and the ability to trace the movement of a product or document throughout the supply chain with a high level of security and immutability, as well as eliminates double spending (in particular, by using the same digital documents as collateral for financing, which is a common source of fraud in international trade. At the same time, blockchain technology is only a tool on the way to optimizing administrative trade procedures and stimulating foreign economic activity and can be used only if there is appropriate legislation. In particular, the issues of determining the legal status of the blockchain (in particular, the terminology — “blockchain”, “smart contracts”) and blockchain-based applications need to be settled. Analysis of international experience proves the lack of unified approaches in this area. This actualizes the need to intensify work at the global international level to address the above issues. It is also advisable for Ukraine to join this process. For the sake of end-to-end digitalization of trade and limiting the possibility of potentially conflicting individual approaches that could lead to further disconnection and barriers to trade, harmonization of domestic legislation in accordance with existing international documents (in particular, UNCITRAL model laws) is becoming important. Digital trade agreements (e.g., the upcoming Digital Trade Agreement between Ukraine and the United Kingdom) are one of the tools that will help to intensify actions in this direction. In addition, among the issues that require regulation prior to the introduction of blockchain technology in international trade are the following: (1) the procedure for entering data (in particular, ensuring their accuracy and completeness) to be transmitted and exchanged on the blockchain; (2) protection of the transmitted data, (3) responsibility for data entry and processing, as well as the legal algorithm for their correction in case of errors (in particular, it should be clearly defined whether it is possible to make changes to the code underlying the blockchain to correct errors and, if so, who has the right to do it), (4) dispute resolution procedure; (5) mutual recognition of documents/certificates issued (this will ensure that the algorithms used work accurately with the data entered and comply with specific (international and national) rules), etc., as well as recognition of electronic signatures and electronic documents (in particular, transfer documents, bills of lading, promissory notes, warehouse receipts, etc. On this basis, it is considered expedient not to adopt a separate legal act like the Illinois State Law “On the Implementation of Blockchain Technology”, but to modernize the existing laws of Ukraine: the Law of Ukraine “On Electronic Commerce”, the Law of Ukraine “On Electronic Documents and Electronic Document Management”, etc. These are the directions of further research. It is considered appropriate at the legislative level to allow the use of smart contracts, records and signatures protected by blockchain in the field of trade and to use the method of analogy of the law in order to extend the legal regime of electronic contracts and signatures to them, which is reflected in the law-making of certain foreign countries (in particular, the United States (New York State). It is proposed to develop and adopt legislation on the digitalization of economic policy in general and foreign economic policy in particular, taking into account the principles of functional equivalence and technological neutrality. This will protect domestic legislation from the need for constant amendments to take into account the emergence of new technologies.

Putting Credit Factor Investing into Practice

Implementing established corporate bond factors in real-world portfolios poses many challenges for investors. First, the investment universe is reduced by non-tradable assets. In this article, the authors avoid investing into these bonds by constructing a tradability measure based on past transaction data. A realistic strategy to implement should target bonds that are being traded in the market. Second, factor performance is typically measured against a benchmark. To allow for a fair comparison, the authors limit the deviation between the portfolio and the benchmark in key dimensions. Consequently, not only bonds with the best signals can be included in the portfolio. Overall, this reduced universe, along with other restrictions and higher transaction costs than on the equity side, leads to a substantial performance decrease. Nevertheless, the authors show that factor investing in credit is still a successful strategy if it is approached with realistic expectations and common pitfalls are avoided. Their enhanced approach translates signals into usable trading strategies, outperforming the benchmark with an information ratio of 1.1 (1.4) for investment grade (high yield).

Utilizing Prediction Intervals for Unsupervised Detection of Fraudulent Transactions: A Case Study

Money laundering operations have a high negative impact on the growth of a country’s national economy. As all financial sectors are increasingly being integrated, it is vital to implement effective technological measures to address these fraudulent operations. Machine learning methods are widely used to classify an incoming transaction as fraudulent or non-fraudulent by analyzing the behaviour of past transactions. Unsupervised machine learning methods do not require label information on past transactions, and a classification is made solely based on the distribution of the transaction. This research presents three unsupervised classification methods: ordinary least squares regression-based (OLS) fraud detection, random forest-based (RF) fraud detection and dropout neural network-based (DNN) fraud detection. For each method, the goal is to classify an incoming transaction amount as fraudulent or non-fraudulent. The novelty in the proposed approach is the application of prediction interval calculation for automatically validating incoming transactions. The three methods are applied to a real-world dataset of credit card transactions. The fraud labels available for the dataset are removed during the model training phase but are later used to evaluate the performance of the final predictions. The performance of the proposed methods is further compared with two other unsupervised state-of-the-art methods. Based on the experimental results, the OLS and RF methods show the best performance in predicting the correct label of a transaction, while the DNN method is the most robust method for detecting fraudulent transactions. This novel concept of calculating prediction intervals for validating an incoming transaction introduces a new direction for unsupervised fraud detection. Since fraud labels on past transactions are not required for training, the proposed methods can be applied in an online setting to different areas, such as detecting money laundering activities, telecommunication fraud and intrusion detection.

Privacy-preserving Collaborative Filtering by Distributed Mediation

Recommender systems have become very influential in our everyday decision making, e.g., helping us choose a movie from a content platform, or offering us suitable products on e-commerce websites. While most vendors who utilize recommender systems rely exclusively on training data consisting of past transactions that took place through them, it would be beneficial to base recommendations on the rating data of more than one vendor. However, enlarging the training data by means of sharing information between different vendors may jeopardize the privacy of users. We devise here secure multi-party protocols that enable the practice of Collaborative Filtering (CF) in a manner that preserves the privacy of the vendors and users. Shmueli and Tassa [ 38 ] introduced privacy-preserving protocols of CF that involved a mediator; namely, an external entity that assists in performing the computations. They demonstrated the significant advantages of mediation in that context. We take here the mediation approach into the next level by using several independent mediators. Such distributed mediation maintains all of the advantages that were identified by Shmueli and Tassa, and offers additional ones, in comparison with the single-mediator protocols: stronger security and dramatically shorter runtimes. In addition, while all prior art assumed limited and unrealistic settings, in which each user can purchase any given item through only one vendor, we consider here a general and more realistic setting, which encompasses all previously considered settings, where users can choose between different competing vendors. We demonstrate the appealing performance of our protocols through extensive experimentation.

DUEL: Dempster Uncertainty-Based Enhanced- Trust Level Scheme for VANET

This work presents an uncertainty-based Trust Model (TM). It uses Dempster Shafer Theory (DST) to cater to uncertainty arising due to information scarcity in Vehicular Adhoc Networks (VANETs). DST combines direct and indirect trust values of vehicles sending messages to build its new trust opinion. Malicious vehicles, albeit authenticated, may alter or drop messages. The solution is to establish trust. Trust establishment assumes that vehicle behaviour is predictable, and its past transactions are useful for anticipating its future response. The high vehicular mobility, dynamic topology, and low neighbourhood density in VANETs cause a lack of credible information for establishing trust. The work also presents the introduction of the penalty function, forgetting function, rewarding factor, and the uncertainty-based importance factor during the combination of direct and indirect trust values using DST. Apart from this, the forgiving factor deals with a situation when a distrusted vehicle starts behaving ideally. Additionally, we present trust contexts based on types of messages altered or dropped by them. Four different trust levels and uncertainty in DUEL aids the receiver make correct decisions during an attack. DUEL is applied and tested on the real-time data of vehicle trajectory. Results show that DUEL can capture both false message attacks and message suppression attacks even in rapidly changing neighbourhoods and performs well in terms of high values of TPR and low values of FPR.