Blockchain technology has gained popularity due to its key features of decentralization, cryptographic verification, and immutability, which have proven extremely useful in various industries. However, despite their impressive security features, blockchain networks are not immune to cyber threats. In recent times, the blockchain system has been threatened by fraudulent attacks that require quick responses. Machine learning and deep learning models are increasingly leveraged to address these challenges. However, due to their black box nature, these models lack transparency, which is a major criticism. This study presents an approach to enhancing fraud detection mechanisms on Ethereum. This study presents an efficient and transparent fraud detection system on Ethereum known as IFS-TABPFN. An interpretable feature selection approach based on Shap values and optimized gradient boosting was introduced to develop five deep learning models built on neural networks. These models included Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Convolutional Neural Networks and Long Short-Term Memory (CLSTM) and Tabular Prior-Data Fitted Network (TabPFN). A comparative analysis of our results indicates that IFS-TABPFN achieves 99.2% accuracy in just a few seconds, outperforming other neural networks and existing systems. This study highlights the importance of explainable AI in understanding how features influence decisions, performance and contribute to artificial intelligence models' transparency and trust.
The diversity and scarcity of the medical information makes it difficult to create precise global classification approach for the healthcare applications.The main motive is the privacy issue that restricts the data exchanging scope between healthcare institutions.On the contrary, an information from single source is not adequate for developing the worldwide diagnosis approach.The Federated Learning (FL) is a promising solution for privacy and data multiplicity issues, an appropriate aggregation model for multi class and dissimilar medical information is still challenging task in the recognition.Moreover, the FL approaches does not effectively analyzes the each participant execution in the local model and secures the user data.In order to overcome this issue, the Zero-Knowledge Proof (ZKP) based FL approach is developed over blockchain (BC) for performing the COVID-19 classification.The global model of FL uses the two layer Long Short Term Memory (2LLSTM) with federated proximal term (FedProx) namely 2LLSTMFP while the Convolutional Neural Network (CNN) is used in the local model.The integration ZKP and BS is used to improve the data confidentiality while the immutability of BC helps to prevent unauthorized variations for the ledger.The developed FLBC-ZKP is analyzed with two datasets such as COVID-19 Radiography, and CXR images pneumonia and COVID-19.The FLBC-ZKP is evaluated using accuracy, recall, precision, specificity, F1-score, False Negative Rate (FNR) and False Positive Rate (FPR).The existing researches such as WMT, MCCF, 3SFDL and TOTL are used to compare the FLBC-ZKP method.The FLBC-ZKP achieves improved accuracy of 98.34 % for COVID-19 Radiography dataset that is better than the MCCF and 3SFDL.
Polaki Durga Prasad, Yelleti Vivek, Vadlamani Ravi
Federated Learning (FL) has garnered widespread attention in finance, banking, and healthcare due to its decentralized, distributed training and the ability to protect privacy while obtaining a global shared model. However, FL faces challenges such as communication overhead and limited resource capability. This motivated us to propose a first-of-its-kind, two-stage FL approach as follows: (i) During phase I, under non-federated settings, synthetic dataset is generated by employing two different probability distributions as noise to the vanilla conditional tabular generative adversarial neural network (CTGAN) resulting in modified CTGAN. We also employed standard metrics to assess the quality of synthetic datasets. (ii) In phase II, the Federated Probabilistic Neural Network (FedPNN) is developed for building globally shared classification model. Despite PNN being a one-pass learning classifier, its complexity depends on the training data size. Therefore, we employed a modified evolving clustering method (ECM), another one-pass algorithm, to cluster the training data, in between the input and pattern layers of the FedPNN. The effectiveness of our approach is validated on credit card fraud detection and Polish bankruptcy prediction datasets.
Udayveer Singh Virk, Devansh Verma, Gagandeep Singh, Prof. Sheetal Laroiya Prof. Sheetal Laroiya
Abstract—This project aims to develop a web3 platform that stores user credentials on the blockchain, providing high levels of security and privacy. Using a range of tools and technologies, including Metamask, RemixIDE, Ganache, Node.js, Solidity for smart contracts, HTML, and CSS, the platform offers a user-friendly interface that enhances the user experience. Smart contracts are used to ensure that user credentials are only visible to the individual user, providing a high level of security and privacy. This platform has the ability to revolutionize how users interact with online services and manage their digital identities, reducing costs, increasing trust, and improving expandability. The implementation of this project has demonstrated the overall benefits of blockchain and smart contracts in virtual identity management, including increased security, improved privacy, and enhanced user experience. The platform has the potential for further development and expansion, including the integration of biometric authentication, artificial intelligence and machine learning algorithms, and the expansion to include a range of online services. Overall, this project has demonstrated the significant potential of blockchain technology and smart contracts in digital identity management and has the ability to shift the way users communicate with online services, offering a one-stop-shop for their online needs. Keywords—Block chain, metamask, ganache, remix ide, solidity
Mohamed Ibrahim Ragab, Rawan Osama Bakr, Heba K. Aslan
Deceptive phishing attacks greatly endanger blockchain security, tricking miners into adding harmful blocks to the chain. Current methods of detection and agreement protocols are frequently not enough, especially if authorized miners accidentally include these blocks. Despite the potential for improving detection capabilities, the adoption of zero-trust policies is still restricted. This paper explores different machine learning techniques, like k-Nearest Neighbors (k-NN), Decision Trees (DT), Random Forest (RF), and XGBoost, to predict phishing attacks. It also evaluates feature selection methods such as Principal Component Analysis (PCA) and Decision Trees, ultimately recommending the Random Forest (RF) model as the most effective for phishing detection. The RF model, assessed using metrics such as accuracy, precision, recall, and evaluation time, demonstrates superior performance, achieving up to 99% accuracy. Consequently, the RF model emerges as the optimal choice for accurately and efficiently identifying phishing threats, thereby enhancing the security of blockchain networks.
There is a huge demand to ensure the compliance of smart contracts listed on blockchain platforms to safety and economic standards described in natural languages. Today, manual efforts in the form of auditing are commonly used to achieve this goal. ML-based automated techniques have the promise to alleviate human efforts and the resulting monetary costs. However, unlike other domains where ML techniques have had huge successes, no systematic ML techniques have been proposed or applied to smart contract auditing. We present SC-Bench, the first dataset for automated smart-contract auditing research. SC-Bench consists of 5,377 real-world smart contracts running on Ethereum, a widely used blockchain platform, and 15,975 violations of standards on Ehereum called ERCs. Out of these violations, 139 are real violations programmers made. The remaining are errors systematically injected by us to reflect the violations of different ERC rules. We evaluate SC-Bench using GPT-4 by prompting it with both the contracts and ERC rules. In addition, we manually identify each violated rule and the corresponding code site (i.e., oracle) and prompt GPT-4 with the information asking for a True-or-False question. Our results show that without the oracle, GPT-4 can only detect 0.9% violations, and with the oracle, it detects 22.9% violations. These results show the potential room for improvement in ML-based techniques for smart-contract auditing.
Delegated Proof of Stake (DPoS) is a widely utilized consensus protocol in blockchain-based Internet of Things (IoT) systems. We propose a heuristic algorithm-based accounting rights allocation method, also referred to as the witness election method, which aims to address the challenges in DPoS. The challenges associated with selected witness nodes that do not reflecting majority stakeholder preferences and susceptibility to manipulation of the vote. This method employs the Kendall’s rank correlation as the fitness function to optimize the arrangement of the top-k producers, thereby maximizing stakeholder preferences. We propose a novel heuristic algorithm, termed SP-DEWOA, which combines the differential evolution algorithm and whale optimization with piecewise chaotic mapping to maximize permutation similarity, i.e., stakeholder preferences. To further improve the efficiency of SP-DEWOA, we parallelize SP-DEWOA based on the Spark-based parallelization design. Experimental results demonstrate that the witness nodes selected through SP-DEWOA are consistent with the preferences of the majority of stakeholders. Furthermore, SP-DEWOA has been proven to have high scalability and resilience against vote manipulation.
In recent years, Non-Fungible Token (NFT) has gradually become the key application of blockchain technology. Static NFT is the most common type of NFT. Once static NFT is minted on the blockchain, its additional metadata is immutable. However, some NFTs that mark real assets, games, sports, and other types must dynamically update the metadata. Therefore, a dynamic NFT with changeable features is needed. The emergence of dynamic NFT has greatly expanded the application innovation scene, and promoted the rapid development of community ecology, but also brought new problems and challenges to anti-fraud and supervision. This paper aims to realize the classification and detection of dynamic NFT. First, define and classify dynamic NFTs from both dynamic and static perspectives. Second, a complete dataset of dynamic NFT smart contract codes on Ethereum was constructed for the first time, and analyzed from multiple perspectives. Third, a smart contract feature model of dynamic NFT is proposed, and machine learning methods are used for recognition and classification. After experimental verification, the method proposed in this article can be effectively used to detect and identify dynamic NFTs, helping NFT holders avoid risks.
Like any other useful technology, cryptocurrencies are sometimes used for criminal activities. While transactions are recorded on the blockchain, there exists a need for a more rapid and scalable method to detect addresses associated with fraudulent activities. We present RiskSEA, a scalable risk scoring system capable of effectively handling the dynamic nature of large-scale blockchain transaction graphs. The risk scoring system, which we implement for Ethereum, consists of 1. a scalable approach to generating node2vec embedding for entire set of addresses to capture the graph topology 2. transaction-based features to capture the transactional behavioral pattern of an address 3. a classifier model to generate risk score for addresses that combines the node2vec embedding and behavioral features. Efficiently generating node2vec embedding for large scale and dynamically evolving blockchain transaction graphs is challenging, we present two novel approaches for generating node2vec embeddings and effectively scaling it to the entire set of blockchain addresses: 1. node2vec embedding propagation and 2. dynamic node2vec embedding. We present a comprehensive analysis of the proposed approaches. Our experiments show that combining both behavioral and node2vec features boosts the classification performance significantly, and that the dynamic node2vec embeddings perform better than the node2vec propagated embeddings.
Dongcheng Li, W. Eric Wong, Xiaodan Wang, Sean Pan · 5 authors
This paper introduces a method for detecting vulnerabilities in smart contracts using static analysis and a multi-objective optimization algorithm. We focus on four types of vulnerabilities: reentrancy, call stack overflow, integer overflow, and timestamp dependencies. Initially, smart contracts are compiled into an abstract syntax tree to analyze relationships between contracts and functions, including calls, inheritance, and data flow. These analyses are transformed into static evaluations and intermediate representations that reveal internal relations. Based on these representations, we examine contract's functions, variables, and data dependencies to detect the specified vulnerabilities. To enhance detection accuracy and coverage, we apply a multi-objective optimization algorithm to the static analysis process. This involves assigning initial numeric values to input data and monitoring changes in statement coverage and detection accuracy. Using coverage and accuracy as fitness values, we calculate Pareto front and crowding distance values to select the best individuals for the new parent population, iterating until optimization criteria are met. We validate our approach using an open-source dataset collected from Etherscan, containing 6,693 smart contracts. Experimental results show that our method outperforms state-of-the-art tools in terms of coverage, accuracy, efficiency, and effectiveness in detecting the targeted vulnerabilities.
Smart contracts, as a form of digital protocol, are computer programs designed for the automatic execution, control, and recording of contractual terms. They permit transactions to be conducted without the need for an intermediary. However, the economic property of smart contracts makes their vulnerabilities susceptible to hacking attacks, leading to significant losses. In this paper, we introduce a smart contract timestamp vulnerability detection technique HomoDec based on code homogeneity. The core idea of this technique involves comparing the homogeneity between the code of the test smart contract and the existing smart contract vulnerability codes in the database to determine whether the tested code has a timestamp vulnerability. Specifically, HomoDec first explores how to vectorize smart contracts reasonably and efficiently, representing smart contract code as a high-dimensional vector containing features of code vulnerabilities. Subsequently, it investigates methods to determine the homogeneity between the test codes and the ones in vulnerability code base, enabling the detection of potential timestamp vulnerabilities in smart contract code.
Nicolas Huber, Ralf Küsters, Julian Liedtke, Daniel Rausch
Abstract Electronic voting (e-voting) systems have become more prevalent in recent years, but security concerns have also increased, especially regarding the privacy and verifiability of votes. As an essential ingredient for constructing secure e-voting systems, designers often employ zero-knowledge proofs (ZKPs), allowing voters to prove their votes are valid without revealing them. Invalid votes can then be discarded to protect verifiability without compromising the privacy of valid votes. General purpose zero-knowledge proofs (GPZKPs) such as ZK-SNARKs can be used to prove arbitrary statements, including ballot validity. While a specialized ZKP that is constructed only for a specific election type/voting method, ballot format, and encryption/commitment scheme can be more efficient than a GPZKP, the flexibility offered by GPZKPs would allow for quickly constructing e-voting systems for new voting methods and new ballot formats. So far, however, the viability of GPZKPs for showing ballot validity for various ballot formats, in particular, whether and in how far they are practical for voters to compute, has only recently been investigated for ballots that are computed as Pedersen vector commitments in an ACM CCS 2022 paper by Huber et al. Here, we continue this line of research by performing a feasibility study of GPZKPs for the more common case of ballots encrypted via Exponential ElGamal encryption. Specifically, building on the work by Huber et al., we describe how the Groth16 ZK-SNARK can be instantiated to show ballot validity for arbitrary election types and ballot formats encrypted via Exponential ElGamal. As our main contribution, we implement, benchmark, and compare several such instances for a wide range of voting methods and ballot formats. Our benchmarks not only establish a basis for protocol designers to make an educated choice for or against such a GPZKP, but also show that GPZKPs are actually viable for showing ballot validity in voting systems using Exponential ElGamal.
Bitcoin is a sort of money that is entirely virtual and is frequently referred to as a cryptocurrency, digital cash or virtual currency. It resembles a virtual currency although some countries have openly banned it, At first, not many stores used to accept Bitcoin as a mode of payment for goods and services. cryptocurrencies were mainly preferred for illicit activity on darknets, but nowadays it is widely used in mainstream applications. The amount of people who are becoming interested in cryptocurrencies has skyrocketed in recent years, and as a result, daily interactions on the distributed ledgers that underpin them have become more intense. The real Bitcoins you see in pictures are just for show. Without the private codes printed inside them, they would be worthless. In this paper we examine academic works to determine bitcoin address behaviour and pattern analysis. A thorough research of the literature is used to report on the pattern analysis of bitcoin transaction. The paper is based on primary information from published works and secondary information from publicly available case studies that are pertinent. In contrast to previous currencies, Bitcoin seems to have encountered many difficulties and, with its wide range of practical uses, posed particular difficulties for the user community. When Bitcoin first appeared, it appeared to represent optimism for a brighter future, but it is difficult to forecast how bitcoin will develop.
Ethereum smart contracts, the new way of transactions and a popular name in the world of cryptocurrencies, have gathered a huge base of research and scientific attention. They are so helpful that they allow us to eliminate the need for a separate third-party library to allow unknown parties to see contract details on a computer. But since we can all see that online commerce is growing day by day and will continue to grow, it can never be fully free from scams and unethical operations. So, to correctly detect all such unethical and malicious transactions, this paper used a deep learning model. And to further enhance the model, this study used metaheuristic optimization as well. It employs an algorithm called Genetic Algorithm and to provide better optimization in the explorations phase of Cuckoo Search (CS) to achieve its goal of detecting fraudulent transactions. The algorithm covers the loopholes in the CS strategy. Furthermore, to provide strong grounds for research in our paper, this proposed model was compared with various types of approaches such as Light Gradient Boosting Machine, Support Vector Classification, Multi-Layer Perceptron, XGBoost, Logistic Regression, and Random Forest. The proposed model outperforms other significant models like SVC, KNN, LGBM Classifier, RF etc. with the accuracy of $\mathbf{9 8. 6 \%}$.
Ethereum faces growing fraud threats. Current fraud detection methods, whether employing graph neural networks or sequence models, fail to consider the semantic information and similarity patterns within transactions. Moreover, these approaches do not leverage the potential synergistic benefits of combining both types of models. To address these challenges, we propose TLMG4Eth that combines a transaction language model with graph-based methods to capture semantic, similarity, and structural features of transaction data in Ethereum. We first propose a transaction language model that converts numerical transaction data into meaningful transaction sentences, enabling the model to learn explicit transaction semantics. Then, we propose a transaction attribute similarity graph to learn transaction similarity information, enabling us to capture intuitive insights into transaction anomalies. Additionally, we construct an account interaction graph to capture the structural information of the account transaction network. We employ a deep multi-head attention network to fuse transaction semantic and similarity embeddings, and ultimately propose a joint training approach for the multi-head attention network and the account interaction graph to obtain the synergistic benefits of both.
Related party transactions (RPTs) can serve as channels for the spread of credit risk events among blockchain firms. However, current credit risk-assessment models typically only consider a firm’s individual characteristics, overlooking the impact of related parties in the blockchain. We suggest incorporating RPT network analysis to improve credit risk evaluation. Our approach begins by representing an RPT network using a weighted adjacency matrix. We then apply DANE, a deep network embedding algorithm, to generate condensed vector representations of the firms within the network. These representations are subsequently used as inputs for credit risk-evaluation models to predict the default distance. Following this, we employ SHAP (Shapley Additive Explanations) to analyze how the network information contributes to the prediction. Lastly, this study demonstrates the enhancing effect of using DANE-based integrated features in credit risk assessment.
This study explored the use of machine learning models to detect fraudulent transactions made over Ethereum, a type of cryptocurrency. The dataset included data from 9840 transactions which included both fraudulent and legitimate transactions and included various parameters such as average, minimum and maximum amounts received and sent, the number of unique addresses to and from which ether was sent and received, the time duration between the transactions and the remaining balance after the transactions. The transaction amount lies in a range of 12000.0 to 52000.0. Four machine learning models, namely Decision Tree, Random Forest Classifier, Catboost Classifier and XGBoost Classifier, are evaluated for fraud transaction detection. XGBoost has emerged as the most promising model, achieving near perfect accuracy (99.65%) and high precision, recall and F1-scores. Notably the robust performance of XGBoost suggests it’s potential for accurately detecting fraudulent transactions in Ethereum. However, comparatively weaker performance is observed in models like the Decision Tree and Random Forest. This study underscores the significance of machine learning in enhancing fraud detection in Ethereum transactions and advancement of the field of cybersecurity in blockchain technologies by providing a useful and scalable fraud-detecting instrument.
Ruichao Liang, Jing Chen, Cong Wu, Kun He · 9 authors
Smart contracts, the cornerstone of decentralized applications, have become increasingly prominent in revolutionizing the digital landscape. However, vulnerabilities in smart contracts pose great risks to user assets and undermine overall trust in decentralized systems. Fuzzing, a prominent security testing technique, is extensively explored to detect vulnerabilities. But current smart contract fuzzers fall short of expectations in testing efficiency for two primary reasons. Firstly, smart contracts are stateful programs, and existing approaches, primarily coverage-guided, lack effective feedback from the contract state. Consequently, they struggle to effectively explore the contract state space. Secondly, coverage-guided fuzzers, aiming for comprehensive program coverage, may lead to a wastage of testing resources on benign code areas. This wastage worsens in smart contract testing, as the mix of code and state spaces further complicates comprehensive testing. To address these challenges, we propose Vulseye, a stateful directed graybox fuzzer for smart contracts guided by vulnerabilities. Different from prior works, Vulseyeachieves stateful directed fuzzing by prioritizing testing resources to code areas and contract states that are more prone to vulnerabilities. We introduceCode TargetsandState Targetsinto fuzzing loops as the testing targets of Vulseye. We use static analysis and pattern matching to pinpointCode Targets, and propose a scalable backward analysis algorithm to specifyState Targets. We design a novel fitness metric that leverages feedback from both the contract code space and state space, directing fuzzing toward these targets. With the guidance of code and state targets, Vulseyealleviates the wastage of testing resources on benign code areas and achieves effective stateful fuzzing. In comparison with state-of-the-art fuzzers, Vulseyedemonstrated superior effectiveness and efficiency. Notably, it uncovered 4,845 vulnerabilities in 42,738 real-world smart contracts, outperforming existing approaches by up to$9.7\times $, and identified 11 previously unknown vulnerabilities within the top 50 Ethereum DApps, involving approximately 2,500,000 USD.
In the dynamic and rapidly evolving landscape of blockchain technology, traditional fraud detection methods, which often rely on labeled data, face limitations due to the diverse and adaptive nature of fraud. This study introduces a novel framework that employs the K-Means clustering algorithm, a technique celebrated for its unsupervised learning capabilities, to detect anomalous transaction patterns indicative of potential fraud, such as unusually high transaction volumes or rapid transfers between wallets. By circumventing the need for pre-labeled examples of fraudulent activity, our approach significantly enhances adaptability and applicability across various blockchain contexts. We apply this framework to a comprehensive dataset encompassing multiple cryptocurrencies, including Bitcoin, Ethereum, Doge Coins, and Tether, analyzing attributes such as closing prices, volatility, and market volume. The results demonstrate the framework’s effectiveness in isolating outliers and identifying transactions that bear hallmarks of suspicious activity, thereby contributing a powerful tool for proactive fraud detection. This research not only paves the way for future advancements in blockchain security but also reinforces the trustworthiness and integrity of blockchain systems by providing a robust mechanism for identifying and mitigating fraudulent activities without the constraints of traditional, supervised methods.
This paper explores the vulnerability of machine learning models, specifically Random Forest, Decision Tree, and K-Nearest Neighbors, to very simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance metrics, such as accuracy, precision, recall, and F1-score. Our findings, highlighting how prone those techniques are to simple attacks, are alarming, and the inconsistency in the attacks' effect on different algorithms promises ways for attack mitigation. We examine the effectiveness of different mitigation strategies, including adversarial training and enhanced feature selection, in enhancing model robustness.
The rampant fraudulent activities on Ethereum hinder the healthy development of the blockchain ecosystem, necessitating the reinforcement of regulations. However, multiple imbalances involving account interaction frequencies and interaction types in the Ethereum transaction environment pose significant challenges to data mining-based fraud detection research. To address this, we first propose the concept of meta-interactions to refine interaction behaviors in Ethereum, and based on this, we present a dual self-supervision enhanced Ethereum fraud detection framework, named Meta-IFD. This framework initially introduces a generative self-supervision mechanism to augment the interaction features of accounts, followed by a contrastive self-supervision mechanism to differentiate various behavior patterns, and ultimately characterizes the behavioral representations of accounts and mines potential fraud risks through multi-view interaction feature learning. Extensive experiments on real Ethereum datasets demonstrate the effectiveness and superiority of our framework in detecting common Ethereum fraud behaviors such as Ponzi schemes and phishing scams. Additionally, the generative module can effectively alleviate the interaction distribution imbalance in Ethereum data, while the contrastive module significantly enhances the framework's ability to distinguish different behavior patterns. The source code will be available in https://github.com/GISec-Team/Meta-IFD.
Gang Tian, Guangxin Zhao, Rui Wang, Jiachang Wang · 5 authors
Abstract How to quickly and accurately retrieve relevant smart contracts from a huge amount of smart contracts has become an urgent need for users. The classification of smart contracts offers a solution by narrowing down the search space. Existing smart contract classification methods suffer from incomplete semantic feature extraction and a lack of consideration of the existence of rich semantics in existing smart contracts of the same class. To address the above problems, we propose a contrast learning and semantic feature embedding approach to enhance K-Nearest Neighbor (CL-SFE-IKNN). Our method fuses local features, global features, and account transaction features of the smart contract source code to perfect the semantics of the contract. Our method adopts KNN to retrieve multiple instances of contracts in the same class and assigns weights to the model output based on their labels. Meanwhile, we introduce contrastive learning and semantic feature embedding to enhance KNN retrieval to high-quality nearest neighbors of the same class. Experimental results show that by combining a KNN classifier with a traditional linear classifier, our model achieves the best performance compared with other baseline models.