The role and significance of the cryptocurrency phenomenon is defined. The directions of criminal use of cryptocurrencies are outlined. Algorithms for the use of cryptocurrencies and illegal crypto markets by Russian criminals have been revealed. The prerequisites and features of the use of cryptocurrency mixers and tumblers for the purpose of concealing criminal cryptocurrency operations are disclosed. The decentralized service “Tornado Cash” and the directions of its criminal use are characterized. Modern ways of circumventing sanctions and avoiding sanctions pressure during the purchase of cryptocurrencies by Russian war criminals and hackers have been identified. The features of the functioning of centralized and decentralized cryptocurrency exchanges in the context of existing and probable restrictions on cross-border cryptocurrency payments and p2p transfers by Russians are detailed. The basic provisions of the EU law on AML were considered in order to introduce restrictions on the implementation of anonymous cryptocurrency transactions. The positive experience of Israel in combating the financing of terrorism with the help of cryptocurrencies is highlighted. The further directions of improvement of the mechanisms to prevent the use of cryptocurrencies for the purpose of supporting war criminals and financing terrorism have been identified, including within the framework of regulatory settlement.
Crypto malware has become a major threat to the security of cryptocurrency holders and exchanges. As the popularity of cryptocurrency continues to rise, so too does the number and sophistication of crypto malware attacks. This paper leverages machine learning techniques to understand the evolution, impact, and detection of cryptocurrency-related threats. We analyse the different types of crypto malware, including ransomware, crypto jacking, and supply chain attacks, and explore the use of machine learning algorithms for detecting and preventing these threats. Our research highlights the importance of using machine learning for detecting crypto malware and compares the effectiveness of traditional methods with deep learning techniques. Through this analysis, we aim to provide insights into the growing threat of crypto malware and the potential benefits of using machine learning in combating these attacks.
The Ponzi scheme, an old-fashioned fraud, is now popular on the Ethereum blockchain, causing considerable financial losses to many crypto investors. A few Ponzi detection methods have been proposed in the literature, most of which detect a Ponzi scheme based on its smart contract source code. This contract-code-based approach, while achieving very high accuracy, is not robust because a Ponzi developer can fool a detection model by obfuscating the opcode or inventing a new profit distribution logic that cannot be detected. On the contrary, a transaction-based approach could improve the robustness of detection because transactions, unlike smart contracts, are harder to be manipulated. However, the current transaction-based detection models achieve fairly low accuracy. In this paper, we aim to improve the accuracy of the transaction-based models by employing time-series features, which turn out to be crucial in capturing the life-time behaviour a Ponzi application but were completely overlooked in previous works. We propose a new set of 85 features (22 known account-based and 63 new time-series features), which allows off-the-shelf machine learning algorithms to achieve up to 30% higher F1-scores compared to existing works.
Tran Viet Khoa, Do Hai Son, Chi-Hieu Nguyen, Dinh Thai Hoang · 11 authors
With the escalating prevalence of malicious activities exploiting vulnerabilities in blockchain systems, there is an urgent requirement for robust attack detection mechanisms. To address this challenge, this paper presents a novel collaborative learning framework designed to detect attacks in blockchain transactions and smart contracts by analyzing transaction features. Our framework exhibits the capability to classify various types of blockchain attacks, including intricate attacks at the machine code level (e.g., injecting malicious codes to withdraw coins from users unlawfully), which typically necessitate significant time and security expertise to detect. To achieve that, the proposed framework incorporates a unique tool that transforms transaction features into visual representations, facilitating efficient analysis and classification of low-level machine codes. Furthermore, we propose an advanced collaborative learning model to enable real-time detection of diverse attack types at distributed mining nodes. Our model can efficiently detect attacks in smart contracts and transactions for blockchain systems without the need to gather all data from mining nodes into a centralized server. In order to evaluate the performance of our proposed framework, we deploy a pilot system based on a private Ethereum network and conduct multiple attack scenarios to generate a novel dataset. To the best of our knowledge, our dataset is the most comprehensive and diverse collection of transactions and smart contracts synthesized in a laboratory for cyberattack detection in blockchain systems. Our framework achieves a detection accuracy of approximately 94% through extensive simulations and 91% in real-time experiments with a throughput of over 2,150 transactions per second.
Abstract Despite the rapid growth of the cyber insurance market in recent years, insurance companies in this area face several challenges, such as a lack of data, a shortage of automated tasks, increased fraudulent claims from legal policyholders, attackers masquerading as legal policyholders, and insurance companies becoming targets of cybersecurity attacks due to the abundance of data they store. On top of that, there is a lack of Know Your Customer procedures. To address these challenges, in this article, we present , an innovative architecture that utilizes Blockchain technology to provide data transparency and traceability. The backbone of the architecture is complemented by Smart Contracts, which automate cyber insurance processes, and Self-Sovereign Identity for robust identification. The effectiveness of ’s architecture is compared with the literature against the challenges the cyber insurance industry faces. In a nutshell, our approach presents a significant advancement in the field of cyber insurance, as it effectively combats the issue of fraudulent claims and ensures proper customer identification and authentication. Overall, this research demonstrates a novel and effective solution to the complex problem of managing cyber insurance, providing a solid foundation for future developments in the field.
The cyberspace is a convenient platform for creative, intellectual, and accessible works that provide a medium for expression and communication. Malware, phishing, ransomware, and distributed denial-of-service attacks pose a threat to individuals and organisations. To detect and predict cyber threats effectively and accurately, an intelligent system must be developed. Cybercriminals can exploit Internet of Things devices and endpoints because they are not intelligent and have limited resources. A hybrid decision tree method (HIDT) is proposed in this article that integrates machine learning with blockchain concepts for anomaly detection. In all datasets, the proposed system (HIDT) predicts attacks in the shortest amount of time and has the highest attack detection accuracy (99.95% for the KD99 dataset and 99.72% for the UNBS-NB 15 dataset). To ensure validity, the binary classification test results are compared to those of earlier studies. The HIDT’s confusion matrix contrasts with previous models by having low FP/FN rates and high TP/TN rates. By detecting malicious nodes instantly, the proposed system reduces routing overhead and has a lower end-to-end delay. Malicious nodes are detected instantly in the network within a short period. Increasing the number of nodes leads to a higher throughput, with the highest throughput measured at 50 nodes. The proposed system performed well in terms of the packet delivery ratio, end-to-end delay, robustness, and scalability, demonstrating the effectiveness of the proposed system. Data can be protected from malicious threats with this system, which can be used by governments and businesses to improve security and resilience.
Weichu Deng, Huanchun Wei, Teng Huang, Cong Cao · 6 authors
With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities.
Danielle Alves Batista, Ana Mangeth, Isabella Frajhof, Paulo Henrique Alves · 8 authors
Blockchain technology, initially known for its applications in the financial industry, has emerged as a promising solution for various other domains. One prominent area for the use of blockchain-based solutions is forensics, specifically the chain of custody maintenance and control. While there have been numerous research projects exploring the use of blockchain technology in digital forensics, limited attention has been given to its application in controlling of the physical evidence chain of custody. In this research, we aim to explore the literature on the use of blockchain technology to solve problems related to the physical evidence chain of custody. Through a systematic literature review (SLR), we analyzed 26 resources discussing blockchain-based solutions for evidence chain of custody issues, based on requirements that could be applied to both physical and digital evidence. The results showed that there is a lack of studies involving the use of blockchain technology to solve problems related to the physical evidence chain of custody, and future research should focus on solving the issue.
Purpose This study aims to, firstly, develop a red flag checklist for cryptocurrency Ponzi schemes and, secondly, to test this red flag checklist against publicly available marketing material for Mirror Trading International (MTI). The red flag checklist test seeks to establish if MTI’s marketing material posted on YouTube ® (in the form of a live video presentation) exhibits any of the red flags from the checklist. Design/methodology/approach The study uses a structured literature review and qualitative analysis of red flags for Ponzi and cryptocurrency Ponzi schemes. Findings A research lacuna was discovered with regard to cryptocurrency Ponzi scheme red flags. By means of a structured literature review, journal papers were identified that listed and discussed Ponzi scheme red flags. The red flags from the identified journal papers were subsequently used in a qualitative analysis. The analyses and syntheses resulted in the development of a red flag checklist for cryptocurrency Ponzi schemes, with five red flag categories, containing 18 associated red flags. The red flag checklist was then tested against MTI’s marketing material (a transcription of a live YouTube presentation). The test resulted in MTI’s marketing material exhibiting 88% of the red flags contained within the checklist. Research limitations/implications The inherent limitations in the design of using a structured literature review and the lack of research regarding the cryptocurrency Ponzi scheme red flags. Practical implications The study provides a red flag checklist for cryptocurrency Ponzi schemes. The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags. Social implications The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags. Originality/value The study provides a red flag checklist for cryptocurrency Ponzi schemes.
Mohammed A. Mohammed, Manel Boujelben, Mohamed Abid
Recently, the advent of blockchain (BC) has sparked a digital revolution in different fields, such as finance, healthcare, and supply chain. It is used by smart healthcare systems to provide transparency and control for personal medical records. However, BC and healthcare integration still face many challenges, such as storing patient data and privacy and security issues. In the context of security, new attacks target different parts of the BC network, such as nodes, consensus algorithms, Smart Contracts (SC), and wallets. Fraudulent data insertion can have serious consequences on the integrity and reliability of the BC, as it can compromise the trustworthiness of the information stored on it and lead to incorrect or misleading transactions. Detecting and preventing fraudulent data insertion is crucial for maintaining the credibility of the BC as a secure and transparent system for recording and verifying transactions. SCs control the transfer of assets, which is why they may be subject to several adverbial attacks. Therefore, many efforts have been proposed to detect vulnerabilities and attacks in the SCs, such as utilizing programming tools. However, their proposals are inadequate against the newly emerging vulnerabilities and attacks. Artificial Intelligence technology is robust in analyzing and detecting new attacks in every part of the BC network. Therefore, this article proposes a system architecture for detecting fraudulent transactions and attacks in the BC network based on Machine Learning (ML). It is composed of two stages: (1) Using ML to check medical data from sensors and block abnormal data from entering the blockchain network. (2) Using the same ML to check transactions in the blockchain, storing normal transactions, and marking abnormal ones as novel attacks in the attacks database. To build our system, we utilized two datasets and six machine learning algorithms (Logistic Regression, Decision Tree, KNN, Naive Bayes, SVM, and Random Forest). The results demonstrate that the Random Forest algorithm outperformed others by achieving the highest accuracy, execution time, and scalability. Thereby, it was considered the best solution among the rest of the algorithms for tackling the research problem. Moreover, the security analysis of the proposed system proves its robustness against several attacks which threaten the functioning of the blockchain-based healthcare application.
The IoT (Internet of Things) encompasses numerous networks and connected devices. One of the primary concerns surrounding IoT, according to researchers and security experts, is the potential risks to privacy and cybersecurity. Deep learning offers significant capabilities for self-adjustment, self-organization, and generalization. Recognizing this, advanced deep learning algorithms are employed in this research to address the privacy and security issues plaguing the IoT landscape. To address these concerns, a novel model called BC-Trans Network is proposed, leveraging the strengths of both Blockchain technology and a transformer component. The transformer plays a vital role in identifying abnormal data, enabling the system to take proactive measures against potential threats. In addition Hash-2 is introduced for the verification of IoT users, adding an extra layer of security to the authentication process. The Blockchain model is utilized to securely store user passwords and details, ensuring a robust and tamper-proof authentication mechanism. To validate the proposed model, a publicly available dataset CSE-CIC-IDS2018 is employed. Pre-processing techniques, including feature selection using the chi-square method, are applied to refine the dataset. The transformer module then classifies the data as normal or abnormal, allowing for accurate identification of potential security breaches. To further safeguard the data and protect the privacy of users, a Fully Homomorphic Encryption (FHE) method is employed. This advanced encryption enables the encryption of categorized normal data, ensuring its confidentiality even during transmission and storage. The study's findings support IoT-cloud server security and privacy by demonstrating the effectiveness of the suggested paradigm in identifying and thwarting network threats. With detection times of 225.3 seconds, an accuracy of 99.25%, a precision of 99.53%, a recall of 99.32%, and an F1 score of 99.59%, the proposed system exhibits impressive performance. Furthermore, as the output numbers increase, the system's metrics improve, suggesting its scalability and flexibility.
Abstract This work considers a combinatorial optimization problem in graphs, the nilcatenation problem, and investigates its potential application for detecting money laundering activities in cryptocurrency networks. The nilcatenation problem consists of finding a set of arcs that can be removed from an arc‐weighted directed graph without changing the balance of any vertex. The balance of a vertex is defined as the difference between the sum of the weights of outgoing and incoming arcs. We propose a 0/1 integer linear programming formulation and a local branching algorithm. The approaches are computationally evaluated and compared using three sets of test instances, two of them generated from Bitcoin's testnet and mainnet networks. An experiment on the testnet showed that it is possible to retrieve a nilcatenation artificially introduced with fake bitcoin transactions. Experiments on the mainnet showed that it is possible to find large nilcatenations, possibly indicating money laundering activities.
Abstract The rise of Non-Fungible Tokens (NFTs) is beginning to revolutionize the digital world thanks to the unique property of these tokens. Indeed, they can represent the ownership of physical or digital assets. They are implemented using smart contracts, therefore if the code of the smart contract contains bugs, an attacker can exploit its vulnerabilities to perform an attack called sleepminting. Sleepminting consists of transferring NFTs owned by an address, without the owner’s consent. In this paper, we provide a detailed analysis of the sleepminting attack and, thanks to the insights gained, we propose a prevention system to reduce the number of sleepminting attacks. Our prevention system is based on analysing the transactions included in new blocks, detecting those that are related to sleepminting attacks and keeping track of the addresses that are involved in these transactions. A dictionary-like data structure can be used to keep track of the addresses involved, where the key is the address and the value acts as a counter for the number of times the address is involved in sleepminting. With this information, block-creating nodes can add another verification step before adding a transaction to a block, which consists of blocking transactions when the addresses involved appear in sleepminting attacks a number of times greater than a threshold. The evaluation shows that sleepminting is a relevant phenomenon, and now it involves NFT transfers rather than NFT minting. Our proposed prevention system is able to block up to 87% of attacks.
With the popularity of Non-Fungible Tokens (NFTs), NFTs have become a new target of phishing attacks, posing a significant threat to the NFT trading ecosystem. There has been growing anecdotal evidence that new means of NFT phishing attacks have emerged in Ethereum ecosystem. Most of the existing research focus on detecting phishing scam accounts for native cryptocurrency on the blockchain, but there is a lack of research in the area of phishing attacks of emerging NFTs. Although a few studies have recently started to focus on the analysis and detection of NFT phishing attacks, NFT phishing attack means are diverse and little has been done to understand these various types of NFT phishing attacks. To the best of our knowledge, we are the first to conduct case retrospective analysis and measurement study of real-world historical NFT phishing attacks on Ethereum. By manually analyzing the existing scams reported by Chainabuse, we classify NFT phishing attacks into four patterns. For each pattern, we further investigate the tricks and working principles of them. Based on 469 NFT phishing accounts collected up until October 2022 from multiple channels, we perform a measurement study of on-chain transaction data crawled from Etherscan to characterizing NFT phishing scams by analyzing the modus operandi and preferences of NFT phishing scammers, as well as economic impacts and whereabouts of stolen NFTs. We classify NFT phishing transactions into one of the four patterns by log parsing and transaction record parsing. We find these phishing accounts stole 19,514 NFTs for a total profit of 8,858.431 ETH (around 18.57 million dollars). We also observe that scammers remain highly active in the last two years and favor certain categories and series of NFTs, accompanied with signs of gang theft.
Xueyan Tang, Yuying Du, Alan Lai, Ze Zhang · 5 authors
<title>Abstract</title> This paper aims to explore the application of deep learning in smart contract vulnerabilities detection. Smart contracts are an essential part of blockchain technology and are crucial for developing decentralized applications. However, smart contract vulnerabilities can cause financial losses and system crashes. Static analysis tools are frequently used to detect vulnerabilities in smart contracts, but they often result in false positives and false negatives because of their high reliance on predefined rules and lack of semantic analysis capabilities. Furthermore, these predefined rules quickly become obsolete and fail to adapt or generalize to new data. In contrast, deep learning methods do not require predefined detection rules and can learn the features of vulnerabilities during the training process.In this paper, we introduce a solution called Lighting Cat which is based on deep learning techniques. We trained three deep learning models for detecting vulnerabilities in smart contract: Optimized-CodeBERT, Optimized-LSTM, and Optimized-CNN. To precisely extract vulnerability features, we acquired segments of vulnerable code functions to retain critical vulnerability features. Using the CodeBERT pre-training model for data preprocessing, we could capture the syntax and semantics of the code more accurately, thereby enhancing the performance of vulnerabilities detection. This is particularly significant in the inspection of Solidity Code.To demonstrate the feasibility of our proposed solution, we evaluated its performance using the SolidiFI-benchmark dataset, which consists of 9369 vulnerable contracts injected with vulnerabilities from seven different types. Experimental results showed that, among the Lighting Cat we proposed, Optimized-CodeBERT model surpassed other methods, achieving an f1-score of 93.53%.
This paper introduces the Time-Aware LSTM (T-LSTM) model to identify criminal activities involving USDT on wallet addresses within the blockchain ecosystem. The model utilizes a time-aware LSTM architecture to learn the continuous variations in node address features over different transaction time intervals. Additionally, a gating mechanism filters the influence intensity of neighboring transaction node addresses on the central node. The gating mechanism accounts for the transactional correlation strength between node addresses. Finally, a self-attention mechanism is employed to integrate node address features across various transaction timestamps, producing a comprehensive feature representation for the addresses. Experimental results demonstrate that the T-LSTM model effectively captures the dynamic feature changes of node addresses over irregular transaction intervals, outperforming traditional detection models regarding precision, recall, and F1 score on the test set.
This work compares machine learning methods using supervised, semi-supervised and unsupervised learning, to classify advertisements for cryptocurrency related investment scams found in the online forum Bitcointalk, and the social media platform Reddit. We extract more than 24.2 million posts from Bitcointalk and use Reddit’s API to collect 2,108 submissions. We train and compare several multiclass text classification approaches and use the models with highest accuracy and F-measure to identify cryptocurrency investment scam advertisements found on both platforms. We discover around five percent of all posts collected on both sites are potential scams. We then use another text classifier to identify the scam actors involved in these investment scam advertisements. We also discover the lures used within these fraudulent adverts and find the main differences in luring techniques used between Bitcointalk and Reddit. We identify that the most prevalent lure type uses the financial principle, followed by the distraction principle in Bitcointalk, and by the authority principle in Reddit. Finally, we use subreddits as communities’ proxies and compare scam advertisements within them to identify whether pensioners are being specifically targeted by cryptocurrency scam adverts. Our results show that retirement subreddits do not contain a higher number of cryptocurrency investment scam adverts compared to other investment focused subreddits.
Aiming at the problem of insufficient technology for detecting smart contract vulnerabilities and the lack of improvement of certain detection tools, a method for the expansion and improvement of the internal module of the Mythril method is proposed. Since the technology of detecting vulnerabilities is not yet mature and there are vulnerabilities in smart contracts, vulnerability detection is particularly important. Since the Mythril tool covers the most types of vulnerabilities, its accuracy rate is also the highest. In order to ensure the effect of smart contract vulnerability detection, this paper proposes the expansion and improvement of the internal module of Mythril, which realizes the operation of automatic vulnerability analysis when performing vulnerability detection by improving the operation efficiency and simplifying the operation of smart contract opcodes while analyzing them. The comparison of experimental results shows that the proposed method is more suitable for smart contract vulnerability detection, and the detection accuracy and efficiency are improved, with an average accuracy rate of 94.07%. It performs better in vulnerability detection and provides an effective guarantee for the security and reliability of smart contracts, which has important application value and research significance.
Adi Darmawansyah, Djunaedi Djunaedi, Kristiawanto Kristiawanto
The medium of exchange can be any object that can be accepted by everyone in society in the process of exchanging goods and services. Long before knowing money, humans had made transactions using barter practices, that is, the exchange of goods and/or services for the desired goods and/or services. In the preparation of this research, a normative juridical approach where approach is carried out based on the main legal material by examining theories, concepts, legal principles, and laws and regulations related to this research. Cryptocurrency assets don't just impact people who mine or trade crypto. It turns out that anonymous platforms that run crypto are also increasingly associated with cybercrime. A recent study from Interisle Consulting Group revealed that phishing attempts related to cryptocurrencies grew 257 percent compared to last year (compared to a 61 percent increase in phishing attacks overall), especially for attacks on wallets and exchanges. The rapid development of information and communication technology makes the journey of the development of crime in the virtual and digital world (cybercrime) sophisticated and complex.
cryptocurrency is a digital or virtual currency that uses ryptography for security and operates independently of a entral bank. Its decentralized nature allows for secure and transparent transactions, making it an appealing alternative to traditional fiat currencies. Cryptocurrencies uses block chain technology, which is a distributed ledger that records all transactions on a network of computers. Bit coin was the first cryptocurrency to gain widespread attention, but today there are thousands of different crypto currencies with varying degrees of popularity and acceptance. Despite their potential benefits, cryptocurrencies are subject to volatility, regulatory uncertainty, and security risks, which have led to debates about their future role in the global economy. In this paper we are going to discuss different fraud prospects in cryptocurrency investment faces by users. Here we are listed possible scams happened in past and possibilities in future with cryptocurrency fund. Even we tried to discuss recent available detection & prevention methods for such scams. With the help of our future research perspective we are planning to provide technique to prevent such different scams and saving our valuable money.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Firuz Kamalov, Mehdi Gheisari, Yang Liu, Mohammad Reza Feylizadeh · 5 authors
The Internet of Things (IoT) has been considered in various fields in the last decade. With the increasing number of IoT devices in the community, secure, accessible, and reliable infrastructure for processing and storing computed data has become necessary. Since traditional security protocols are unsuitable for IoT devices, IoT implementation is fraught with privacy and security challenges. Thus, blockchain technology has become an effective solution to the problems of IoT security. Blockchain is an empirical data distribution and storage model involving point-to-point transmission, consensus mechanism, asymmetric encryption, smart contract, and other computer technologies. Security and privacy are becoming increasingly important in using the IoT. Therefore, this study provides a comprehensive framework for classifying security criteria based on blockchain technology. Another goal of the present study is to identify causal relationship factors for the security issue using the Fuzzy Decision-Making Trial-and-Evaluation Laboratory (FDEMATEL) approach. In order to deal with uncertainty in human judgment, fuzzy logic is considered an effective tool. The present study’s results show the proposed approach’s efficiency. Authentication (CR6), intrusion detection (CR4), and availability (CR5) were also introduced as the most effective and essential criteria, respectively.
Abstract With the widespread use of blockchain, more and more smart contracts are being deployed, and their internal logic is getting more and more sophisticated. Due to the large false positive rate and low detection accuracy of most current detection methods, which heavily rely on already established detection criteria, certain smart contracts additionally call for human secondary detection, resulting in low detection efficiency. In this study, we propose HGAT, a hierarchical graph attention network-based detection model, in order to address the aforementioned issues as well as the shortcomings of current smart contract vulnerability detection approaches. First, using Abstract Syntax Tree (AST) and Control Flow Graph, the functions in the smart contract are abstracted into code graphs (CFG). Then abstract each node in the code subgraph, extract the node features, utilize the graph attention mechanism GAT, splice the obtained vectors to form the features of each line of statements and use these features to detect smart contracts. To create test data and assess HGAT, we leverage the open-source smart contract vulnerability sample dataset. The findings of the experiment indicate that this method can identify smart contract vulnerabilities more quickly and precisely than other detection techniques.
This paper presents a comprehensive analysis of the cryptocurrency free giveaway scam disseminated in a new distribution channel, Twitter lists. To collect and detect the scam in this channel, unlike existing scam detection systems that rely on manual effort, this paper develops a fully automated scam detection system, \textit{GiveawayScamHunter}, to continuously collect lists from Twitter and utilize a Nature-Language-Processing (NLP) model to automatically detect the free giveaway scam and extract the scam cryptocurrency address. By running \textit{GiveawayScamHunter} from June 2022 to June 2023, we detected 95,111 free giveaway scam lists on Twitter that were created by thousands of Twitter accounts. Through analyzing the list creator accounts, our work reveals that scammers have combined different strategies to spread the scam, including compromising popular accounts and creating spam accounts on Twitter. Our analysis result shows that 43.9\% of spam accounts still remain active as of this writing. Furthermore, we collected 327 free giveaway domains and 121 new scam cryptocurrency addresses. By tracking the transactions of the scam cryptocurrency addresses, this work uncovers that over 365 victims have been attacked by the scam, resulting in an estimated financial loss of 872K USD. Overall, this work sheds light on the tactics, scale, and impact of free giveaway scams disseminated on Twitter lists, emphasizing the urgent need for effective detection and prevention mechanisms to protect social media users from such fraudulent activity.