Bitcoin is the most common cryptocurrency involved in cyber scams. Cybercriminals often utilize pseudonymity and privacy protection mechanism associated with Bitcoin transactions to make their scams virtually untraceable. The Ponzi scheme has attracted particularly significant attention among the Bitcoin fraudulent activities. This paper considers a multi-class classification problem to determine whether a transaction is involved in Ponzi schemes or other cyber scams, or is a non-scam transaction. We design a specifically designed crawler to collect data and propose a novel Attention-based Long Short-Term Memory (A-LSTM) method for the classification problem. The experimental results show that the proposed model has better efficiency and accuracy than existing approaches, including Random Forest, Extra Trees, Gradient Boosting, and classical LSTM. With correctly identified scam features, our proposed A-LSTM achieves an F1-score over 82% for the original data and outperforms the existing approaches.
Since the beginning of the insurance business, dishonest practices have plagued the sector. Each year, many crimes are not investigated, which costs the insurance industry billions of dollars. It is anticipated that by the year 2020, the Indian insurance business would have grown to a value of 21,715 billion dollars thanks to improvements in both knowledge and distribution methods. India comes up at number 10 for the amount of gross premiums collected by life insurance companies and number 15 for the total amount earned by non-life insurance companies. As a result, a blockchain technology-based architecture is developed to enable safe transactions and the data interchange between many agents make up the insurance network. The blockchain is a decentralized peer-to-peer technology that enables the secure, unchangeable, and open-source authentication of healthcare claims. Consider the ways in which blockchain technology and smart contracts could make corporate operations more effective. It will demonstrate how these technologies may be integrated to construct a system that makes use of machine learning to identify and prevent fraud in a variety of fields, including automobile, healthcare, and life insurance claims, among others.
Ethereum, a typical application of blockchain technology, has attracted extensive attention from all walks of life since its release. Owing to imperfections in existing supervision technology, illegal and criminal activities on blockchain platforms are becoming increasingly frequent. The most typical Ethereum fraud is the Ponzi scheme, which causes blockchain investors to lose millions of assets and severely impacts social development. Currently, Ponzi scheme detection primarily focuses on machine learning and data mining. However, existing detection methods still have two problems in data imbalance processing and feature extraction: (1) data enhancement using an oversampling algorithm produces noise and (2) feature redundancy existing in extracted feature data. The SMOTEENN algorithm is introduced to solve data imbalance. The PD-SECR method, the Convolutional Neural Network (CNN) feature extraction, and random forest (RF) classification models are used for detection, but the two models are independently trained. The results show that the detection method proposed in this study is more suitable for the Ethereum Ponzi scheme.
In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms-XGboost and random forest (RF)-used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.
Ali Aljofey, Abdur Rasool, Qingshan Jiang, Qiang Qu
Blockchain technology has allowed many abnormal schemes to hide behind smart contracts. This causes serious financial losses, which adversely affects the blockchain. Machine learning technology has mainly been utilized to enable automatic detection of abnormal contract accounts in recent years. In spite of this, previous machine learning methods have suffered from a number of disadvantages: first, it is extremely difficult to identify features that enable accurate detection of abnormal contracts, and based on these features, statistical analysis is also ineffective. Second, they ignore the imbalances and repeatability of smart contract accounts, which often results in overfitting of the model. In this paper, we propose a data-driven robust method for detecting abnormal contract accounts over the Ethereum Blockchain. This method comprises hybrid features set by integrating opcode n-grams, transaction features, and term frequency-inverse document frequency source code features to train an ensemble classifier. The extra-trees and gradient boosting algorithms based on weighted soft voting are used to create an ensemble classifier that balances the weaknesses of individual classifiers in a given dataset. The abnormal and normal contract data are collected by analyzing the open source etherscan.io, and the problem of the imbalanced dataset is solved by performing the adaptive synthetic sampling. The empirical results demonstrate that the proposed individual feature sets are useful for detecting abnormal contract accounts. Meanwhile, combining all the features enhances the detection of abnormal contracts with significant accuracy. The experimental and comparative results show that the proposed method can distinguish abnormal contract accounts for the data-driven security of blockchain Ethereum with satisfactory performance metrics.
Smart contract, one of the most successful applications of blockchain, is taking the world by storm, playing an essential role in the blockchain ecosystem. However, frequent smart contract security incidents not only result in tremendous economic losses but also destroy the blockchain-based credit system. The security and reliability of smart contracts thus gain extensive attention from researchers worldwide. In this survey, we first summarize the common types and typical cases of smart contract vulnerabilities from three levels, i.e., Solidity code layer, EVM execution layer, and Block dependency layer. Further, we review the research progress of smart contract vulnerability detection and classify existing counterparts into five categories, i.e., formal verification, symbolic execution, fuzzing detection, intermediate representation, and deep learning. Empirically, we take 300 real-world smart contracts deployed on Ethereum as the test samples and compare the representative methods in terms of accuracy, F1-Score, and average detection time. Finally, we discuss the challenges in the field of smart contract vulnerability detection and combine with the deep learning technology to look forward to future research directions.
Crypto crimes peaked in 2021, and recent years have shown that blockchain is not going to die but will stay with us. In addition, blockchain transaction throughputs are expected to rise, which will make the manual investigation of crimes next to impossible in the future. We need effective and automated solutions to monitor and detect fraudulent activities happening on blockchains. In this paper, we propose a system to detect the blacklisted addresses in the Ethereum blockchain. First, we collected Ethereum blockchain transaction data and blacklisted addresses. Then, we constructed the transaction graph of Ethereum and extracted features of addresses, including some global features like pagerank. Finally, we trained the models of standard machine learning algorithms and predicted the class of the addresses. Our results show that with more than 97% accuracy we can predict blacklisted addresses.
Anokye Acheampong Amponsah, Adebayo Felix Adekoya, Benjamin Asubam Weyori
Healthcare fraud is a global problem affecting both developing and developed countries. It is the deliberate attempt of the perpetrators to take undue advantage of the inefficiencies in current healthcare systems. Fraud tends to deny legitimate beneficiaries of universal health coverage, especially those under health insurance protection. In this work, we propose using machine learning techniques and blockchain technology to detect and prevent fraud in healthcare, especially in claims processing. A decision tree classification algorithm is adopted to classify the original claims dataset. The extracted knowledge is programmed in the Ethereum blockchain smart contract to detect and prevent healthcare fraud. The comparative experimental results show that the best performing tool achieves a classification accuracy of 97.96% and a sensitivity of 98.09%. This means that the proposed system enhances the blockchain smart contract’s ability to detect fraud with an accuracy of 97.96%.
Abstract: The interchange of electronic health data across healthcare facilities is made possible via the health information exchange program. There is a potential for data manipulation in this. This article primarily focuses on using "Blockchain," i.e. one of the greatest technologies, to secure medical health data. Blockchain has demonstrated its outstanding qualities in the field of cryptocurrencies like bitcoin and Ethereum. This study employs the Secure Hash Algorithm (SHA), Simple Mail Transfer Protocol (SMTP), and AES Rijndael Algorithm (SMTP). Additionally, using the Naïve Bayes method, we forecast many heart illnesses related to this work.
In view of the increasingly serious problem of money laundering crime in the blockchain industry, the existing solutions in this scenario can not be applied to reality, or there is a high false positive rate and false negative rate, a method based on weighted sampling neighborhood nodes is designed to find and analyze the implied interrelationship in the data between blockchain transaction features, and learn more effective aggregate input features in the local neighborhood of nodes through model training. Good results have been achieved on the public data set, which verifies the effectiveness of the model in the field of blockchain abnormal transaction detection. Moreover, it provides an idea of modelling transaction entity data by using graph neural network structure for the field of financial data transactions such as anti-money laundering monitoring.
M. Sumathi, M. Rajkamal, S. P. Raja, M. Venkatachalapathy · 5 authors
Nowadays, improving a crop yield ([Formula: see text]) is an emerging and essential task to reduce food scarcity. Factors impacting [Formula: see text] improvement include rising population, water shortage, fertilizer use, climate change and unprecedented insect attacks. To resolve these issues, a smart agriculture technique is proposed in this work. Internet of Things (IoT) sensor devices are used to collect data from farms, following which the fuzzy association rule-based classification technique classifies the data into two, valuable and nonvaluable. An improved artificial neural network (IANN) algorithm is applied to identify and analyze the factors involved in monitoring [Formula: see text]’s. Thereafter, all valuable data pertaining to the type of seed, fertilizer and crop cost is stored in blocks to secure data and communication between members of the farming community. Finally, an edge computing device is used to store the blocks and transfer information. The valuable data collected is classified using the fuzzy association rule and analyzed using the IANN technique, both of which facilitate a comparison with the historical data so as to enable better decision making in terms of seed and fertilizer selection. Similarly, crop price is predicted through a comparison of present and historical yields. To overcome breaches in security, a blockchain technique is employed in this work to secure communication between farmers, investors and merchants. The investor dispatches instructions on the selection of the seed and fertilizer, as well as the crop cost, through the blockchain to the farmer and the merchant. Such secure communication bypasses third-party involvement and inconsistencies in the data. When compared to the traditional method, the proposed technique offers better accuracy and profits, right from seed selection to trading. The proposed IANN technique produced a higher yield than the traditional method with a profit of 51%, 35% and 20% for rice, bananas and flowers, respectively. Similarly, the IANN technique provides 99.15% prediction accuracy in terms of a profit analysis. The blockchain and edge computing-based transactions improve security and reduce transactional latency. The proposed system ensures sustainability and traceability in agriculture.
Certificates are often falsified, such as fake diplomas and forged transcripts. As such, many schools and educational institutions have begun to issue diplomas online. Although diplomas can be issued conveniently anytime, anywhere, there are many cases wherein diplomas are forged through hacking and forgery. This paper deals with the required Blockchain diploma. In addition, we use an automatic translation system, which incorporates natural language processing, to perform verification work that does not require an existing public certificate. The hash algorithm is used to authenticate security. This paper also proposes the use of these security protocols to provide more secure data protection. In addition, each transaction history, whether a diploma is true or not, may be different in length if it is presented in text, but converting it into a hash function means that it is always more than a certain length of SHA-512 or higher. It is then verified using the time stamp values. These chaining codes are designed. This paper also provides the necessary experimental environment. At least 10 nodes are constructed. Blockchain platform development applies and references Blockchain standardization, and a platform test, measurement test, and performance measurement test are conducted to assess the smart contract development and performance measurement. A total of 500 nodes were obtained by averaging 200 times, and a Blockchain-based diploma file was agreed upon at the same time. It shows performance information of about 4100 TPS. In addition, the analysis of artificial intelligence distribution diagram was conducted using a four-point method, and the distribution chart was evenly distributed, confirming the diploma with the highest similarity. The verified values were then analyzed. This paper proposes these natural language processing-based Blockchain algorithms.
Zhenzhou Tian, Yaqian Huang, Jie Tian, Zhongmin Wang · 6 authors
Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and costefficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers.
Blockchain was formerly originated to prevent fraud in digital currency exchanges. Blockchain refers to a collective ledger that is decentralized and unaffected to any sort of tinkering. It gives the confirmed contributors access to store, view, and share the digital information in a situation rich in safety, which supports the development of trust, liability, and transparency in business relations. To capitalize on these specified assistances, companies have now started exploring how blockchain technology could prevent fraud in numerous industry verticals. Protection from identity theft and fraud is an endless challenge for everyone elaborate in buying and selling. Merchants, consumers, issuers, and acquirers know there are susceptibilities in how payments and data are secured. Hackers and fraudsters learn to outsmart the technology with each novelty in security technology and breach these networks. The first section of the chapter describes the structure of the blockchain, its framework, the pros and cons of the combination of these technologies, and the role and importance of machine and deep learning algorithms in fraud detection and prevention in the blockchain. The next section focuses on the reported work, highlighting different researchers’ work for fraud detection and prevention using blockchain technology. The final section of the chapter comprises comparative analysis based on various performance parameters such as accuracy, area under curve, confidence, true negative, false positive, and true positive for different types of fraud detection using blockchain technology.
Over the years, with the development of e-commerce, people are mostly making online transactions, and the risk of getting scammed has also increased. This in turn forces the financial institutions to improve continuously and upgrade their model. Machine Learning techniques were used to detect fraud in credit card transactions, but working with real-time data can be tough for machine learning to handle. Thus, implementation of blockchain techniques with machine learning to improve the efficiency and accuracy of the model. In the proposed model, Ethereum dataset has been used to check the fraudulent transaction and secure it with the help of machine learning algorithms. Out of all the classifiers XGBoost has attained the highest accuracy of 99.21% for the stated dataset.
Abstract Elliptic data—one of the largest Bitcoin transaction graphs—has admitted promising results in many studies using classical supervised learning and graph convolutional network models for anti-money laundering. Despite the promising results provided by these studies, only few have considered the temporal information of this dataset, wherein the results were not very satisfactory. Moreover, there is very sparse existing literature that applies active learning to this type of blockchain dataset. In this paper, we develop a classification model that combines long-short-term memory with GCN—referred to as temporal-GCN—that classifies the illicit transactions of Elliptic data using its transaction’s features only. Subsequently, we present an active learning framework applied to the large-scale Bitcoin transaction graph dataset, unlike previous studies on this dataset. Uncertainties for active learning are obtained using Monte-Carlo dropout (MC-dropout) and Monte-Carlo based adversarial attack (MC-AA) which are Bayesian approximations. Active learning frameworks with these methods are compared using various acquisition functions that appeared in the literature. To the best of our knowledge, MC-AA method is the first time to be examined in the context of active learning. Our main finding is that temporal-GCN model has attained significant success in comparison to the previous studies with the same experimental settings on the same dataset. Moreover, we evaluate the performance of the provided acquisition functions using MC-AA and MC-dropout and compare the result against the baseline random sampling model.
We are presenting a fast and innovative approach to performing approximate pattern-matching for plagiarism detection, using an NDFA-based approach that significantly enhances performance compared to other existing similarity measures. We outline the advantages of our approach in the context of blockchain-based non-fungible tokens (NFTs). We present, formalize, discuss and test our proposed approach in several real-world scenarios and with different similarity measures commonly used in plagiarism detection, and observe significant throughput enhancements throughout the entire spectrum of tests, with little to no compromises on the accuracy of the detection process overall. We conclude that our approach is suitable and adequate to perform approximate pattern-matching for plagiarism detection, and outline research directions for future improvements.
Blockchain applications go far beyond cryptocurrency. As an essential blockchain tool, smart contracts are executable programs that establish an agreement between two parties. Millions of dollars of transactions attract hackers at a hastened pace, and cyber-attacks have caused large economic losses in the past. Due to this, the industry is seeking robust and effective methods to detect vulnerabilities in smart contracts to ultimately provide a remedy. The industry has been utilizing static analysis tools to reveal security gaps, which requires an understanding and insight over all possible execution paths to identify known contract vulnerabilities. Yet, the computational complexity increases as the path gets deeper. Recently, researchers have been proposing ML-driven intelligent techniques aiming to improve the efficiency and detection rate. Such solutions can provide quicker and more robust detection options than the traditionally used static analysis tools. As of this publication date, there is currently no published survey paper on smart contract vulnerability detection mechanisms using ML models. In order to set the ground for further development of ML-driven solutions, in this survey paper, we extensively reviewed and summarized a wide variety of ML-driven intelligent detection mechanism from the following databases: Google Scholar, Engineering Village, Springer, Web of Science, Academic Search Premier, and Scholars Portal Journal. In conclusion, we provided our insights on common traits, limitations and advancement of ML-driven solutions proposed for this field.
S. Giribabu, Vikruthi. Sriharsha, Patan Hussain Basha, K. Suresh · 5 authors
A blockchain network's economics and user confidence can be seriously harmed by fraud. Consensus algorithms like proof of work and proof of stake can verify the legitimacy of a transaction but not the identity of the people who are conducting or verifying it. On a blockchain network, fraud can still occur, as a result of this. One approach to fighting fraud is to make use of machine learning techniques. There are two types of machine learning: supervised and unsupervised. We use a variety of supervised machine learning techniques in this study to distinguish between legitimate and fraudulent purchases. We also compare decision trees, Naive Bayes, logistic regression, multilayer perceptron, and other supervised machine learning techniques in detail for this challenge.
Blockchain presents a chance to address the security and privacy issues of the Internet of Things; however, blockchain itself has certain security issues. How to accurately identify smart contract vulnerabilities is one of the key issues at hand. Most existing methods require large-scale data support to avoid overfitting; machine learning (ML) models trained on small-scale vulnerability data are often difficult to produce satisfactory results in smart contract vulnerability prediction. However, in the real world, collecting contractual vulnerability data requires huge human and time costs. To alleviate these problems, this paper proposed an ensemble learning (EL)-based contract vulnerability prediction method, which is based on seven different neural networks using contract vulnerability data for contract-level vulnerability detection. Seven neural network (NN) models were first pretrained using an information graph (IG) consisting of source datasets, which then were integrated into an ensemble model called Smart Contract Vulnerability Detection method based on Information Graph and Ensemble Learning (SCVDIE). The effectiveness of the SCVDIE model was verified using a target dataset composed of IG, and then its performances were compared with static tools and seven independent data-driven methods. The verification and comparison results show that the proposed SCVDIE method has higher accuracy and robustness than other data-driven methods in the target task of predicting smart contract vulnerabilities.
In the context of the rapid development of blockchain technology, smart contracts have also been widely used in the Internet of Things, finance, healthcare, and other fields. There has been an explosion in the number of smart contracts, and at the same time, the security of smart contracts has received widespread attention because of the financial losses caused by smart contract vulnerabilities. Existing analysis tools can detect many smart contract security vulnerabilities, but because they rely too heavily on hard rules defined by experts when detecting smart contract vulnerabilities, the time to perform the detection increases significantly as the complexity of the smart contract increases. In the present study, we propose a novel hybrid deep learning model named CBGRU that strategically combines different word embedding (Word2Vec, FastText) with different deep learning methods (LSTM, GRU, BiLSTM, CNN, BiGRU). The model extracts features through different deep learning models and combine these features for smart contract vulnerability detection. On the currently publicly available dataset SmartBugs Dataset-Wild, we demonstrate that the CBGRU hybrid model has great smart contract vulnerability detection performance through a series of experiments. By comparing the performance of the proposed model with that of past studies, the CBGRU model has better smart contract vulnerability detection performance.