Fake Transactions Research Articles

AHEAD: A Novel Technique Combining Anti-Adversarial Hierarchical Ensemble Learning with Multi-Layer Multi-Anomaly Detection for Blockchain Systems

Blockchain technology has impacted various sectors and is transforming them through its decentralized, immutable, transparent, smart contracts (automatically executing digital agreements) and traceable attributes. Due to the adoption of blockchain technology in versatile applications, millions of transactions take place globally. These transactions are no exception to adversarial attacks which include data tampering, double spending, data corruption, Sybil attacks, eclipse attacks, DDoS attacks, P2P network partitioning, delay attacks, selfish mining, bribery, fake transactions, fake wallets or phishing, false advertising, malicious smart contracts, and initial coin offering scams. These adversarial attacks result in operational, financial, and reputational losses. Although numerous studies have proposed different blockchain anomaly detection mechanisms, challenges persist. These include detecting anomalies in just a single layer instead of multiple layers, targeting a single anomaly instead of multiple, not encountering adversarial machine learning attacks (for example, poisoning, evasion, and model extraction attacks), and inadequate handling of complex transactional data. The proposed AHEAD model solves the above problems by providing the following: (i) data aggregation transformation to detect transactional and user anomalies at the data and network layers of the blockchain, respectively, (ii) a Three-Layer Hierarchical Ensemble Learning Model (HELM) incorporating stratified random sampling to add resilience against adversarial attacks, and (iii) an advanced preprocessing technique with hybrid feature selection to handle complex transactional data. The performance analysis of the proposed AHEAD model shows that it achieves higher anti-adversarial resistance and detects multiple anomalies at the data and network layers. A comparison of the proposed AHEAD model with other state-of-the-art models shows that it achieves 98.85% accuracy against anomaly detection on data and network layers targeting transaction and user anomalies, along with 95.97% accuracy against adversarial machine learning attacks, which surpassed other models.

Fraud Detection in Banking Data by Machine Learning Techniques

An important part of the study is looking at how to use machine learning to find scams in bank data. This is a very big problem in the banking world, where finding and stopping scams is very important. The study adds class weight-tuning hyper parameters to make scam detection better. These factors make it easier for the model to tell the difference between real and fake transactions, which makes the scam detection system more accurate. Cat Boost, Light GBM, and XG Boost are three well-known machine learning methods that are used in a smart way in this work. Each program is good at something different, and using them together should make the scam detection method work better overall.To fine-tune the hyper parameters, deep learning methods are used in the work. This combination improves the fraud detection system's speed and ability to change, making it better at finding new fraud schemes. Real-world data are used in the project to do thorough reviews. These tests show that using Light GBM and XG Boost together works better than other ways when looking at different factors. By this measure, the suggested method is better at finding fake actions than other approaches. It has a Stacking Classifier that combines results from the Random Forest and Light GBM classifiers with certain settings. Using a Gradient Boosting Classifier as the final predictor, this ensemble method improves the accuracy of predictions by using the best features of different models.

Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks

This paper presents an innovative incentive model that utilizes graph and game theories to address the issue of node incentives in decentralized blockchain networks such as EVM blockchains. The lack of incentives for nodes within EVM networks gives rise to potential weaknesses that might be used for various purposes, such as broadcasting fake transactions or withholding blocks. This affects the overall trust and integrity of the network. To address this issue, the current study offers a network model that incorporates the concepts of graph theory and utilizes a matrix representation for reward and trust optimization. Furthermore, this study presents a game-theoretic framework that encourages cooperative conduct and discourages malicious actions, ultimately producing a state of equilibrium according to the Nash equilibrium. The simulations validated the model’s efficacy in addressing fraudulent transactions and emphasized its scalability, security, and fairness benefits. This study makes a valuable contribution to the field of blockchain technology by presenting an incentive model that effectively encourages the development of secure and trusted decentralized systems.

Exploring Supervised Machine Learning Techniques for Detecting Credit Card Fraud: An Investigative Review

Given the current situation of the economy, credit card use has increased significantly. Users can make significant cash payments with these cards without carrying a lot of cash on them. They have simplified the process of conducting cashless transactions and enabled consumers to make payments of any kind with greater ease. While there are many benefits to using this electronic payment method, there are also some risks. In tandem with the expansion of the consumer base. A specific person’s credit card information may be unlawfully acquired and used in fraudulent purchases. To tackle this issue, certain machine learning methods may be applied to gather information. This research offers a comparative analysis of many supervised learning method for identifying real from fake transactions. In this article, we have covered a variety of techniques for spotting credit card fraud.

Generative Adversarial Networks for Cyber Threat Hunting in Ethereum Blockchain

Ethereum blockchain has shown great potential in providing the next generation of the decentralized platform beyond crypto payments. Recently, it has attracted researchers and industry players to experiment with developing various Web3 applications for the Internet of Things (IoT), Defi, Metaverse, and many more. Although Ethereum provides a secure platform for developing decentralized applications, it is not immune to security risks and has been a victim of numerous cyber attacks. Adversarial attacks are a new cyber threat to systems that have been rising. Adversarial attacks can disrupt and exploit decentralized applications running on the Ethereum platform by creating fake accounts and transactions. Detecting adversarial attacks is challenging because the fake materials (e.g., accounts and transactions) as malicious payloads are similar to benign data. This article proposes a model using Generative Adversarial Networks (GAN) and Deep Recurrent Neural Networks (RNN) for cyber threat hunting in the Ethereum blockchain. Firstly, we employ GAN to generate fake transactions using genuine Ethereum transactions as the first phase of the proposed model. Then in the second phase, we utilize bi-directional Long Short-Term Memory (LSTM) to identify adversarial transactions in a hunting exercise. The results of the first phase evaluation show that the GAN can generate transactions identical to the actual Ethereum transactions with an accuracy of 82.51%. Also, the results of the second phase show 99.98% accuracy in identifying adversarial transactions.

A Legal Analysis on Resolving Recently Growing Online Business Frauds in Bangladesh

In the recent time, the E-commerce sector has been facing serious challenges due to the fraudulent behaviors of a number of E-commerce businesses. Thousands of consumers have lost their money due to these fake transactions. The main purpose of this article is to help to mitigate online business frauds by reviewing the current legislation and regulations in Bangladesh. This research is a descriptive type of research and secondary data and information have been used for study purpose. The study has analyzed existing business laws and provisions, their applicability and deficiencies in reducing online business frauds and dangers. This study finds that there is no committed code or law establishing consumer rights in online businesses. The current laws have not been changed in such a way that online business matters can be handled properly. The study concludes that the legislature may amend existing laws and provisions and also they may consider some foreign countries provisions for taking actions against the fraudulent e-commerce as well as online business organizations. The outcomes of the present study obviously indicate that the risks of online business frauds directly affect consumer behavior when shopping online. Therefore, reducing these risks through undertaking just and proper laws and provisions can increase the trust of the online shopping consumers.

Enhancement in privacy preservation in cloud computing using apriori algorithm

Cloud <span>computing provides advantages, like flexibly of space, security, cost optimization, accessibility from any remote location. Because of this factor cloud computing is emerging as in primary data storage for individuals as well as organisations. At the same time, privacy preservation is an also a significant aspect of cloud computing. In regrades to privacy preservation, association rule mining was proposed by previous researches to protect the privacy of users. However, the algorithm involves creation of fake transaction and this algorithm also fails to maintain the privacy of data frequency. In this research an apriori algorithm is proposed to enhance the privacy of encrypted data. The proposed algorithm is integrated with elagmal cryptography and it does not require fake transactions. In this way, the proposed algorithm improves the data protection as well as query privacy and it hides data frequency. Result analysis shows that the proposed algorithm improves the privacy as compared to previously proposed association rule mining and the algorithm also shows 3% to 5% improvement in performance when compared to other existing algorithms. This performance analysis with varying number of the data and fake transactions shows that the proposed algorithm doesn’t require fake transactions, like data privacy association rule mining.</span>

DESIGN AND DEVELOPMENT OF ANTI-LOCK BRAKING SYSTEM FOR ELECTRIC VEHICLE

Abstract—Recently, privacy-preserving association rules mining algorithms have been proposed to support data privacy. However, the algorithms have an additional overhead to insert fake items (or fake transactions) and cannot hide data frequency. In this paper, we propose a privacy-preserving association rule mining algorithm for encrypted data in cloud computing. For association rule mining, we utilize Apriori algorithm by using the Elgamal cryptosystem, without additional fake transactions. Thus the proposed algorithm can guarantee both data privacy and query privacy, while concealing data frequency. We show that the proposed algorithm achieves about 3-5 times better performance than the existing algorithm, in terms of association rule mining time. Keywords- association rule mining; Apriori algorithm; encrypted data; cloud computing; Elgamal cryptosystem;

Fake transactions in Russian law: history and modernity

This article examines the phenomenon of a fake transaction. The author shows the origin of this institution in the decisions of the civil cassation Department of the Governing Senate in the Russian Empire. In particular, it is shown that initially such decisions were about circumventing the law, then the idea of fictitious (imaginary) transactions was formulated, and only then were the imaginary and fake transactions differentiated. The Soviet approach was initially based on the division of the imaginary and feigned transaction with the indication reactions a fake transactions: nullity, but with the ability to save to cover its legal. The author analyzes the current legislation, judicial practice. The aim of the study is the determination of aggravating circumstances sham transactions, based on the legislative model of sham transactions, including the value at which regulatory provisions it is understood in the legal positions of the Constitutional Court of the Russian Federation, of the Supreme Court of the Russian Federation, courts of arbitration and courts of general jurisdiction, given the domestic doctrine and the experience of legal regulation. Based on the results of the study, the author formulated the main elements that form the model of a fake transaction in the law of the Russian Federation, including the qualifying features of a fake transaction.

Blockchain Enabled Credibility Applications: Extant Issues, Frameworks and Cases

The credibility of information is known as a major cause of a wide range of issues, such as: altered product information in food supply chains; fake transactions on E-commerce platforms; lengthy claim settlement time in agricultural insurance; and costly borrowings in agricultural financing. For a more specific example in food supply chains, end customers want to check the product information, but either doubt the authenticity of information, or simply do not have access to the information. The reason is that upstream suppliers and downstream retailers are often reluctant to share data, fearing privacy loss or business secret leakage. The consequence is that regulatory departments may face enormous challenges to identify accurate contamination sources, if there is scarce information or falsely recorded information at any stage of the food supply chain. In this paper, we focus on four common scenarios demanding information credibility in the agricultural supply chain: product traceability, E-commerce platforms, agricultural insurance, and agricultural financing. We review some high-profile smart credibility applications with emphasis on how blockchain related technologies can provide the information credibility by examining extant issues and relevant frameworks.

Bit Incorporated Codon Positional Encoder (BICPE) Algorithm for Privacy Preservation in Vertically Partitioned Data in Cloud

HIGHLIGHTS Fake transactions are inserted into database to hide item frequency. The input binary bits are grouped into N sets with length as four for encryption. Each item in a data set is encoded with A,C,G,T DNA codons. Encrypted Association rules are built for frequent items in a transaction.

Differentiation of Labor and Civil Legal Relations

The authors analyze the points of view of scientists and practitioners on the differentiation of labor and civil legal relations. The conclusion is made: labour and civil legal relations, both in the XIX and in the XXI centuries, must be distinguished by objective criteria. In cases of fake transactions, for example, civil contracts or paid services, it is theoretically more reasonable to apply to the court with claims about the application of the consequences of the invalidity of fake transactions (paragraph 3 of Article 166 of the Civil Code of the Russian Federation) and the conclusion of an employment contract from the moment of the actual occurrence of labor relations (paragraph 5 of part 2 of Article 16 of the Labour Code of the Russian Federation), indicating the mandatory terms of the employment contract (part 2 of Article 57 of the Labour Code of the Russian Federation)

Legal Consequences of the Invalidity of Pretended Contracts

The purpose of this work is to identify and analyze the legal consequences of fake transactions. In the course of the study, it was concluded that fake transactions can be made both intentionally and inadvertently, as a result of a good-faith misconception of the parties regarding the chosen legal contractual structure. A fake transaction can cover not only another civil transaction, but also other actions of the parties. The legal consequence of making a fake transaction is its insignificance; restorative consequences are not applicable to fake transactions. The application of the legal regime of a hidden transaction is not a special legal consequence of the invalidity of the covering transaction.

A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach.

The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system’s efficacy and performance compared with other state-of-art models.

Understanding and application of crime of sabotaging production and operation in internet era

PurposeThe purpose of this study is to investigate the understanding and application of crime of sabotaging production and operation in internet era, and, at the same time, discuss the basic position for criminal law interpretation in cyberspace.Design/methodology/approachDoctrinal analysis and case study.FindingsAlong with the advent of the internet era, how to apply the traditional crime of sabotaging production and operation in virtual space has attracted people’s attention. The controversy caused by the conviction of malicious application of fake transactions is a typical example. The legal interest protected here includes not only the property value of the means of production itself, but also the expectation interest that can be obtained by normal production and operation activities. There is no reliable basis to believe that overlap of articles between special provision and general laws occurs in crime of sabotaging production and operation and crime of intentional damage of property. The production and operation activities carried out online can also be covered by crime of sabotaging production and operation, without doubt. Ejusdem Generis Rule should be fully respected, but crime of sabotaging production and operation has a dual structure of means behavior and purpose behavior, where the purpose behavior, sabotaging production and operation, is the key to the conviction. However, it is not necessarily premised on physical damage and violent characteristics. The understanding and application of traditional crimes should keep pace with the times in the internet era, and we should not stick to a completely rigid subjective interpretation.Originality/valueThis study demonstrates the possible application of crime of sabotaging production and operation in cyberspace, and clarifies many misunderstandings about this crime.

Fake transactions in Russian law: history and modernity

Fake transactions in Russian law: history and modernity

On some issues of civil law understanding of fake deals

The civil legislation reform and the complexity of the socioeconomic conditions in our country have increased the scientific and practical significance of research related to the clarification of the concept of fake transactions, their relationship with other (related) transactions, and the establishment of their legal consequences. These issues, which have no unity in civil science, are discussed in the proposed article. The article analyzes the concepts and attributes of fake transactions, their qualitative and distinctive features, the specifics of their invalidity (nullity), and the relationship with related invalid (nullity) agreements (transactions). This statement of the articles content determines its theoretical and practical significance as well as its relevance. The methodological basis of the research includes the well-known general and particular scientific research methods. The objectives of the proposed article are as follows: to clarify and form the authors concept of fake transactions, to establish their distinctive features, to define the place of fake contracts in the invalid transactions system, and to clarify the features of fake transaction invalidity.

Crypto Wash Trading

We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges. Regulated exchanges feature patterns consistently observed in financial markets and nature; abnormal first-significant-digit distributions, size rounding, and transaction tail distributions on unregulated exchanges reveal rampant manipulations unlikely driven by strategy or exchange heterogeneity. We quantify the wash trading on each unregulated exchange, which averaged over 70% of the reported volume. We further document how these fabricated volumes (trillions of dollars annually) improve exchange ranking, temporarily distort prices, and relate to exchange characteristics (e.g., age and userbase), market conditions, and regulation.

Mobile Based Multi-Factor Authentication Algorithm for Secure Online Transaction

In today’s world, online payment system is one of the essential requirements of people. Security in online transactions is still a major challenge for the researchers. This work is emphasizing on providing highly secure environment for online transactions to user. One time password is one of the mechanisms to reduce online fraud, still many cases of fake transactions are happening frequently. It requires more attention to establish the faith in E-banking services. The present research article is proposing a multi-factor based secure algorithm which enhances the security for the online transaction services. To increase the authenticity, the proposed algorithm is working on two strategies: a) One Time Password Based Authentication and b) Dedicated Hardware Based Authentication.

Synthetic Minority Oversampling and Smote Regularised Deep Autoencoders Neural Network Techniques for Fraud Prediction in Financial Payment Services

Frauds in Financial Payment Services are the most prevalent form of cybercrime. The increased growth in e-commerce and mobile payments in recent years is behind the rising incidence of fraud in financial payment services. According to "McKinsey, fraud losses throughout the world could be close to $44 billion by 2025." Every year, fraudulent card transactions causes billions of US Dollar of loss. To reduce these losses, designing effective fraud detection algorithms is essential, which depend on sophisticated machine learning methods to help investigators in fraud. For banks and financial institutions, therefore, fraud detection systems have gained excellent significance. Though the fake transactions are very low when compared to genuine transaction, care must be taken to predict it so that the financial institutions can maintain the customer integrity. As fraud is unlikely to occur compared to normal operations, we have the class imbalance problem. We applied Synthetic Minority Oversampling TEchnique (SMOTE) and the Ensemble of sampling methods(Balanced Random Forest Classifier, Balanced Bagging Classifier, Easy Ensemble Classifier, RUS Boost) to Ensemble machine learning algorithms Performance assessment using sensitivity, specificity, precision, ROC area. The purpose of this article is to analyze different predictive models to see how precise they are to detect whether a transaction is a standard payment or a fraud. Instead of misclassifying a real transaction as fraud, this model seeks to improve detection of fraud. We noted that the technique of Ensemble learning using Maximum voting detects the fraud better than other classifiers. Decision Tree Classifier, Logistic Regression, Balanced Bagging classifier is combined and the proposed algorithm is OptimizedEnsembleFD Algorithm. The sample size is increased and deep learning is applied .It is found that the proposed system Smote Regularised Deep Autoencoders (SRD Autoencoders) neural network performs better with good recall and accuracy for this large dataset.