Ponzi schemes, a form of scam, have been discovered in Ethereum smart contracts in recent years, causing massive financial losses. Rule-based detection approaches rely on pre-defined rules with limited capabilities and domain knowledge dependency. Additionally, using static information like opcodes and transactions for machine learning models fails to effectively characterize the Ponzi contracts, resulting in poor reliability and interpretability.
Abstract Due to the anonymous and contract transfer nature of blockchain cryptocurrencies, they are susceptible to fraudulent incidents such as phishing. This poses a threat to the property security of users and hinders the healthy development of the entire blockchain community. While numerous studies have been conducted on identifying cryptocurrency phishing users, there is a lack of research that integrates class imbalance and transaction time characteristics. This paper introduces a novel graph neural network-based account identification model called CT-GCN+, which utilizes blockchain cryptocurrency phishing data. It incorporates an imbalanced data processing module for graphs to consider cryptocurrency transaction time. The model initially extracts time characteristics from the transaction graph using LSTM and Attention mechanisms. These time characteristics are then fused with underlying features, which are subsequently inputted into a combined SMOTE and GCN model for phishing user classification. Experimental results demonstrate that the CT-GCN+ model achieves a phishing user identification accuracy of 97.22% and a phishing user identification area under the curve of 96.67%. This paper presents a valuable approach to phishing detection research within the blockchain and cryptocurrency ecosystems.
Abstract Sybil attacks are a very serious challenge in social networks including, the Social Internet of Things (SIoT). This paper introduces the SybilPSIoT method, in which a hybrid prevention and detection decentralized approach is proposed in SIoT based on smart contracts. The owner adds his objects to the smart contract. However, hostile owners can create Sybil things. This paper formally presents a model that uses a signed SIoT network with objects and identifiers as network nodes and information about the type of nodes (acknowledgers). Assuming the relationship between the edge marks between nodes and the node type, the proposed method uses trust paths between verification and desired nodes using a Bayesian inference model and structural balance patterns to judge the target node in these paths. It also uses game theory to control access owners to prevent Sybil from creating new things based on a cost‐benefit function. Based on the analysis method, a validating effect proportional to the path length on the target object was presented. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks.
Elohim Fonseca dos Reis, Alexander Teytelboym, Abeer ElBahrawy, Ignacio De Loizaga · 5 authors
Dark web marketplaces have been a significant outlet for illicit trade, serving millions of users worldwide for over a decade. However, not all users are the same. This paper aims to identify the key players in Bitcoin transaction networks linked to dark markets and assess their role by analysing a dataset of 40 million Bitcoin transactions involving the 31 major markets in the period 2011-2021. First, we propose an algorithm that categorizes users either as buyers or sellers, and show that a large fraction of the trading volume is concentrated in a small group of elite market participants. We find that the dominance of markets is reflected in trading properties of buyers and sellers. Then, we investigate both market star-graphs and user-to-user networks, and highlight the importance of a new class of users, namely 'multihomers', who operate on multiple marketplaces concurrently. Specifically, we show how the networks of multihomers and seller-to-seller interactions can shed light on the resilience of the dark market ecosystem against external shocks. Our findings suggest that understanding the behavior of key players in dark web marketplaces is critical to effectively disrupting illegal activities.
The implementation of blockchain technology in the creation of secure and decentralized Web 3.0 applications has grown in significance. Blockchain, an industry-spanning distributed ledger technology, has facilitated substantial advancements in information and communication technology, among others. Regarding Web 3.0, this study examines how the implementation of blockchain technology can enhance decentralization and security. By conducting a literature review, this study examines how the implementation of blockchain technology in the development of Web 3.0 applications significantly improves data security. Through the implementation of robust cryptographic features and distributed security principles, the outcomes demonstrate that blockchain can effectively safeguard data while it is being transmitted and stored via Web 3.0 applications. This is a crucial step in the direction of resolving the security issues that are frequently encountered in the digital environment of today. Furthermore, blockchain technology facilitates enhanced decentralization within Web 3.0 applications. Blockchain applications reduce their reliance on a central authority, thereby enhancing their resilience against single-system malfunctions and monopoly control. Furthermore, it facilitates the development of platforms that are more equitable and transparent, granting users greater authority over their data and interactions.
Recent years, the successful application of blockchain in cryptocurrency has attracted a lot of attention, but it has also led to a rapid growth of illegal and criminal activities. Phishing scams have become the most serious type of crime in Ethereum. Some existing methods for phishing scams detection have limitations, such as high complexity, poor scalability, and high latency. In this article, we propose a novel framework named phishing detection on Ethereum via augmentation ego-graph based on graph neural network (PEAE-GNN). First, we obtain account labels and transaction records from authoritative websites and extract ego-graphs centered on labeled accounts. Then we propose a feature augmentation strategy based on structure features, transaction features and interaction intensity to augment the node features, so that these features of each ego-graph can be learned. Finally, we present a new graph-level representation, sorting the updated node features in descending order and then taking the mean value of the top n to obtain the graph representation, which can retain key information and reduce the introduction of noise. Extensive experimental results show that PEAE-GNN achieves the best performance on phishing detection tasks. At the same time, our framework has the advantages of lower complexity, better scalability, and higher efficiency, which detects phishing accounts at early stage.
Bhupendra Acharya, Muhammad Saad, Antonio Emanuele Ciná, Lea Schönherr · 8 authors
The mainstream adoption of cryptocurrencies has led to a surge in wallet-related issues reported by ordinary users on social media platforms. In parallel, there is an increase in an emerging fraud trend called cryptocurrency-based technical support scam, in which fraudsters offer fake wallet recovery services and target users experiencing wallet-related issues.In this paper, we perform a comprehensive study of cryptocurrency-based technical support scams. We present an analysis apparatus called HoneyTweet to analyze this kind of scam. Through HoneyTweet, we lure over 9K scammers by posting 25K fake wallet support tweets (so-called honey tweets). We then deploy automated systems to interact with scammers to analyze their modus operandi. In our experiments, we observe that scammers use Twitter as a starting point for the scam, after which they pivot to other communication channels (e.g., email, Instagram, or Telegram) to complete the fraud activity. We track scammers across those communication channels and bait them into revealing their payment methods. Based on the modes of payment, we uncover two categories of scammers that either request secret key phrase submissions from their victims or direct payments to their digital wallets. Furthermore, we obtain scam confirmation by deploying honey wallet addresses and validating private key theft. We also collaborate with the prominent payment service provider by sharing scammer data collections. The payment service provider feedback was consistent with our findings, thereby supporting our methodology and results. By consolidating our analysis across various vantage points, we provide an end-to-end scam lifecycle analysis and propose recommendations for scam mitigation.
Haojie Sun, Zhaowei Liu, Shenqiang Wang, Haiyang Wang
With Ethereum blockchain advancement, the Ethereum platform gathers numerous users. In this context, traditional phishing appears new fraud methods, resulting in significant losses. Currently, network embedding methods are considered effective solutions in the field of phishing detection. However, investigating existing Ethereum phishing node detection algorithms finds they are not optimal and still face two issues. Firstly, the Ethereum network's topology is unsatisfactory, with nodes exhibiting a long-tail distribution in their degree. Current technologies typically allow high-degree nodes to acquire high-quality embeddings, while low-degree nodes, constrained by limited structure, obtain embeddings of lower quality, significantly impacting the detection accuracy of downstream tasks. Secondly, different features of nodes will suffer losses during the fusion process, resulting in the final learned feature embedding being suboptimal. This paper presents an attention-based graphical representation learning approach (ABGRL) to address these problems. ABGRL extracts different feature information by means of multiple channels, and fuses the different feature information using adaptive attention convolution to select the feature information that has the greatest impact on the downstream task. Then the tail node feature information is enhanced by a self-supervised regression model with robust tail node embedding. Finally, the effectiveness of the proposed model was validated through extensive experiments.
There is a distinct lack of criminological research examining victimisation experiences in emerging cryptocurrency frauds. At the same time, online cryptocurrency communities have become a key part of the social milieu of the cryptocurrency ecosystem where scams are commonplace. Using Reddit forum data from the subreddit r/ CryptoCurrency, this exploratory qualitative study investigates how users in an online cryptocurrency community share knowledge and experiences of cryptocurrency scams. Thematic analysis revealed how online cryptocurrency communities discuss scams by (1) arming the community (e.g. newcomer guides, personal disclosures of scam victimisation, and reflections on the technological affordances in scams); and (2) establishing community norms in response to cryptocurrency scams (e.g. protecting the community, ‘scambaiting’ practices, normalising scams as an outcome of ‘decentralisation’). Gaining a deeper understanding of cryptocurrency scam experiences provides timely insights into the intersections between victims/offenders in digital environments, how we can respond to the recent growth in cryptocurrency scams, and the variegated ways that victims seek assistance following experiences.
Pengcheng Xia, Yu Zhou, Kailong Wang, Kai Ma · 9 authors
The dark web has emerged as the state-of-the-art solution for enhanced anonymity. Just like a double-edged sword, it also inadvertently becomes the safety net and breeding ground for illicit activities. Among them, cryptocurrencies have been prevalently abused to receive illicit income while evading regulations. Despite the continuing efforts to combat illicit activities, there is still a lack of an in-depth understanding regarding the characteristics and dynamics of cryptocurrency abuses on the dark web. In this work, we conduct a multi-dimensional and systematic study to track cryptocurrency-related illicit activities and campaigns on the dark web. We first harvest a dataset of 4,923 cryptocurrency-related onion sites with over 130K pages. Then, we detect and extract the illicit blockchain transactions to characterize the cryptocurrency abuses, targeting features from single/clustered addresses and illicit campaigns. Throughout our study, we have identified 2,564 illicit sites with 1,189 illicit blockchain addresses, which account for 90.8 BTC in revenue. Based on their inner connections, we further identify 66 campaigns behind them. Our exploration suggests that illicit activities on the dark web have strong correlations, which can guide us to identify new illicit blockchain addresses and onions, and raise alarms at the early stage of their deployment.
Cryptocurrency relies on the underlying P2P networking to deliver the up-to-date block and transaction information to synchronize and agree on the processed financial transactions. To maintain healthy connectivity, Bitcoin has implemented a negative reputation scheme based on ban score, which tracks the peer node behavior and results in dropping the peer connection if exceeding a threshold. Previous research in cryptocurrency security showed that such ban score can be both ineffective (easy to bypass to un-ban itself) and vulnerable (against spoofing-based defamation). We design and build a novel positive reputation scheme that addresses such deficiencies of ban score by having the peer behavior only positively impact our score and by disincentivizing switching the network ID for a new reputation score. We provide a general framework for the positive reputation scheme to enable the use of different networking sensing and measurements as well as different aggregate weights for scoring. Then, we provide a concrete instance and build of the positive reputation score scheme focusing on the peer's behavior in relaying unique/new blocks and transactions. We implement, experiment, and analyze our scheme on an active Bitcoin node connected to the Mainnet to show how our scheme provides the reputation and informs the beneficialness of each of its peer connections (delivering new block and transaction information).
Yuan Su, Yuheng Wang, Jiliang Li, Zhou Su · 6 authors
The Public Key Infrastructure (PKI) system is the cornerstone of today's security communications. All users in the service domain covered by the same PKI system are able to authenticate each other before exchanging messages. However, there is identity isolation in different domains, making the identity of users in different domains cannot be recognized by PKI systems in other domains. To achieve cross-domain authentication, the consortium blockchain system is leveraged in the existing schemes. Unfortunately, the consortium blockchain-based authentication schemes have the following challenges: high cost, privacy concerns, scalability and economic unsustainability. To solve these challenges, we propose a scalable and privacy-preserving cross-domain authentication scheme called Bifrost-Auth. Firstly, Bifrost-Auth is designed to use a decentralized oracle to directly interact with blockchains in different domains instead of maintaining a consortium blockchain and enables mutual authentication for users lying in different domains. Secondly, users can succinctly authenticate their membership of the domain by the accumulator technique, where the membership proof is turned into zero knowledge to protect users' privacy. Finally, Bifrost-Auth is proven to be secure against various attacks, and thorough experiments are carried out and demonstrate the security and efficiency of Bifrost-Auth.
From the last few years, in product manufacturing industries, counterfeit products have played an important role that would affect the companv's fame. sales. and profit. To detect fake products and identify real products, a secure system can be developed using blockchain technology, Blockchain technology is a distributed decentralized, digital ledger that is stored across multiple databases, storing all transactions as blocks connected by a chain. Due to having a digital ledger, a block cannot be changed or be hacked. By employing blockchain technology, consumers or users may certify the safety of a product without depending on other users. In this study, Quick Response (QR) codes and bar codes have been used to provide robust techniques to detect counterfeit products. where QR code of the product is associated as a block to a blockchain. The proposed system stores product details and generate a unique QR code associated with that product as a block in the database to detect the counterfeit product. The unique QR codes have been created throughout the procedure and it is matched with the entries stored in the blockchain database. If the generated unique QR code is matched against entries in the blockchain database, it will give a notification to the user otherwise fake product notification will be given to the user. The proposed system outperforms the existing systems in terms of security as well as computational time.
This study presents a series of structured and adaptive processes aimed at tracking and verifying transactions recorded on the blockchain. Permissioned blockchains are employed across diverse enterprises for various purposes, including data recording, management, the utilization of blockchain services, and authentication. However, the processes of data tracking and transactions incur substantial resource and time expenditure. Furthermore, there is potential for information asymmetry within the blockchain ledger due to data breach attacks. Consequently, we propose a contract structured as a hash chain to mitigate resource and time consumption in the tracking and verification processes by organizing transaction hash values and content in a hash chain format based on cryptography. We generate a hash chain for the recorded transactions along the process line and expedite the tracking and verification process by navigating the relevant hash chain. This approach achieves faster and more accurate tracking procedures compared to conventional transaction tracking processes, simultaneously maintaining data symmetry within the blockchain ledger. We conduct a comparative analysis of a contract-based hash-chain-employing structure and two contracts related to tracking in terms of tracking time, CPU usage, and network traffic, among other metrics. The findings suggest that structuring transaction data in the form of a hash chain significantly enhances the efficiency and integrity of the data-tracking and verification processes. Consequently, in this study, we advocate for the adoption of contracts based on the hash chain format when leveraging the blockchain for tracking and verification purposes across various institutions.
Recently, most people have been looking for smart services that allow them to do their daily services directly without having to visit local businesses and providers [1]. As observed in recent times, citizens can now apply to open their own commercial file or obtain a passport without having to visit the country's economic institution or the Immigration and Passports headquarters. Additionally, applying or opening accounts in virtual banks also added a lot of services and facilities for users and transactions between countries that can be rapidly provisioned with minimal effort [2]. All these transactions require the user's authentication and identity verification to avoid any personal fraud and save the user's rights Blockchain technology stands out as one of the most secure technologies available enabling secure transactions without the need for a central authority. Starting in 2009 1, with Bitcoin leveraging blockchain technology, there has been an increasing number of blockchain technology-based solutions. The significance of this work compared to its predecessors is that it uses an existing product and technology to prove the solutions offered by Blockchain and find a solution for authorization and authentication.
Vinay Rishiwal, Udit Agarwal, Mano Yadav, Aziz Alotaibi · 6 authors
The gaming industry, which predominantly depends on centralized platforms, faces growing challenges in protecting digital assets, ensuring gameplay fairness, and addressing online fraud. Conventional gaming systems store player data on centralised servers, including sensitive financial and personal information, making them particularly vulnerable to cyberattacks and security breaches. With gaming transactions frequently occurring on unsecured mobile and desktop platforms, player accounts are often at risk of theft, hacking, and fraud. These security vulnerabilities result in significant financial losses for players and developers, underscoring the urgent need for a secure, transparent, decentralized solution. In light of these challenges, adopting blockchain technology in gaming platforms has emerged as a promising and transformative solution. Blockchain’s decentralized and immutable ledger offers enhanced security, making it nearly impossible for malicious actors to manipulate transaction records or steal digital assets. Research and practical implementations have shown that blockchain can effectively reduce online fraud by securely storing in-game assets and transactions on decentralized networks, ensuring their integrity and resistance to tampering. This paper makes several significant contributions to understanding how blockchain technology can revolutionize the gaming industry. First, it provides a comprehensive analysis of blockchain’s security advantages, emphasizing its capacity to reduce online fraud and enhance the protection of digital assets. Second, the paper explores how blockchain enhances player autonomy by establishing secure digital identities and tokenising in-game assets. By enabling players to own, trade, and exchange their assets directly, blockchain fosters a decentralized gaming economy that grants players greater control over their virtual possessions. Finally, the paper identifies emerging trends and potential research avenues in blockchain-enabled gaming, offering valuable insights into the challenges and opportunities in this rapidly evolving field.
This paper minimizes fake news, which has been a hot topic recently, using blockchain and artificial intelligence technology, and verifies it with blockchain. Also, using Artificial Intelligence technology, we want to create an algorithm that predicts how fake news will spread in the future. You can see various attempts at a news media platform based on Blockchain technology. However, the Blockchain news media platform is still not getting the market response we expected. It is questionable whether the reason is simply because it is a new technology, so it takes a long time to gain trust from consumers, whether consumers are not yet expecting an innovative news media platform, or whether the explosive growth of the Blockchain news media platform is difficult for other reasons. Research to answer this or direct research between Blockchain and media platforms is still lacking. In addition, the method of verifying fake news using artificial intelligence was verified, ANN, CBR, and MDA were changed, and the experiment was verified for progress. In addition, the use of 5-fold cross-validation as a comparative method was added as described above to more closely examine the possibility of its usefulness even in general situations. Also, through various fields of artificial intelligence and blockchain, verification work was done with blockchain, and fake news prediction was made using artificial intelligence. Various experiments were conducted and performance tests were performed, while the performance of about 5,000 TTPS was recorded through the third experiment. In the future, we think it is necessary to combine Artificial Intelligence and blockchain technology.
In today's world of internet marketing product review plays a crucial role. Often E-Commerce companies are accused of manipulating with the data and it suffers from biasing. Biasing leads to wrong customer's view creation thereby prediction for a top rating product fails. This study shows how crowd annotation prevents this biasness and thereby design a smart contract which ultimately will help to eliminate the data manipulation. The smart contract is deployed and tested to see if data can be saved in the run time storage memory regions of the Ethereum blockchain, and the test results are positive. In future this will help to get the unbiased data and better machine learning results, which will ultimately be beneficial for the economy.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques