In recent years, new payment methods have emerged, aimed at improving convenience for users. Cryptocurrencies, in principle, are no different. In this study, we seek to analyze the general population's attitudes towards the adoption of cryptocurrencies as a payment method. To achieve this, we have developed a descriptive survey that targets both current cryptocurrency users and non-users, recognizing that differences in perception may exist. Additionally, we have conducted a sentiment analysis of open-ended questions to understand respondents' views on the future of the cryptocurrency market and its potential as a payment tool, utilizing different lexicons in the English language. Our findings indicate that most cryptocurrency users prefer to invest in these digital assets, often choosing coins based on their popularity rather than other intrinsic features. E-commerce payments are the most attractive activity, followed by international transactions when using cryptocurrencies as a payment method. However, high volatility and a lack of ease of use are the most common difficulties reported by users. Our study also highlights the importance of regulation in a time when users are increasingly demanding higher levels of oversight, in contrast to the past. While users are concerned about the instability and volatility of cryptocurrencies, they also value the anonymity these transactions offer. Our analysis showcases an innovative approach to analyzing interviews and qualitative questionnaires that can be applied in other research fields.
Unknown vulnerabilities, also known as zero-day vulnerabilities, are vulnerabilities in software, systems, or networks that have not yet been publicly disclosed or fixed. If these vulnerabilities are ever discovered by hackers, intentionally or unintentionally, they pose a major threat to network security. This is particularly true in the blockchain field, as smart contracts hold a lot of money, and if they are discovered and exploited by hackers, the financial losses to users will be even greater. However, the current research on smart contract vulnerabilities mainly focuses on known vulnerabilities, and the research on unknown vulnerabilities has been limited. Based on this, we introduce a machine learning-based method for detecting unknown vulnerabilities in smart contracts. First, the method obtains the opcode sequences executed by smart contract transactions in the EVM by instrumenting Geth and replaying the Ethereum transactions. Next, we employ an n-gram model and a vector weight penalty mechanism to extract the opcode sequence features. We then use machine learning algorithms to detect unknown vulnerabilities based on the similarity principle. Finally, we test the effectiveness of our method with four machine learning models: the K-Nearest Neighbor algorithm (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). The SVM model performs best at detecting unknown vulnerabilities, with an accuracy of 96%, a precision of 91%, a recall of 100%, and an F1-score of 95%. We also discuss the benefits of the method: timely detection of attacks due to unknown vulnerabilities, thus reducing user losses.
Abstract: This paper investigates the integration of an Intrusion Detection System (IDS) within the context of blockchain technology. The objective is to enhance the security posture of blockchain networks by detecting and mitigating potential intrusions. Through a meticulous examination of the current threat landscape and the unique challenges posed by blockchain systems, this research proposes a robust IDS framework tailored to the specific requirements of decentralized and distributed ledger environments. The study employs [specific methodology/approach] to assess the effectiveness of the proposed IDS, presenting conclusive findings that contribute to the ongoing discourse on securing blockchain ecosystems. The implications of this research extend to bolstering the resilience of blockchain networks against emerging threat.
Alexandr Kuznetsov, Alex Rusnak, Anton Yezhov, Kateryna Kuznetsova · 6 authors
In the rapidly evolving landscape of blockchain technology, ensuring the integrity and security of data is paramount. This study delves into the security aspects of Merkle Trees, a fundamental component in blockchain architectures, such as Ethereum. We critically examine the susceptibility of Merkle Trees to hash collisions, a potential vulnerability that poses significant risks to data security within blockchain systems. Despite their widespread application, the collision resistance of Merkle Trees and their robustness against preimage attacks have not been thoroughly investigated, leading to a notable gap in the comprehensive understanding of blockchain security mechanisms. Our research endeavors to bridge this gap through a meticulous blend of theoretical analysis and empirical validation. We scrutinize the probability of root collisions in Merkle Trees, considering various factors such as hash length and path length within the tree. Our findings reveal a direct correlation between the increase in path length and the heightened probability of root collisions, thereby underscoring potential security vulnerabilities. Conversely, we observe that an increase in hash length significantly reduces the likelihood of collisions, highlighting its critical role in fortifying security. The insights garnered from our research offer valuable guidance for blockchain developers and researchers, aiming to bolster the security and operational efficacy of blockchain-based systems.
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.
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.
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.
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
Cryptocurrency tracker is an online platform that provides a userfriendly experience. Users get a simple and userfriendly experien ce through the user interface. Users can sign into their account with Gmail or a mobile number for easy access to their account. U sers can track prices of different cryptocurrencies and view currency charts. Using this user interface, users can find prices and ot her relevant information about cryptocurrencies. The app helps users to create watchlists and we can track prices. We can set alerts for cryptocurrency prices. We can customize notifications and help understand new cryptocurrency trends. Users can easily find various cryptocurrencies and track future crypt currency trends. It helps users invest in new popular cryptocurrencies that will be more useful to them in the future. Overall, the Cryptocurrency Tracker web app is a valuable tool for anyone looking to invest, trade, or just keep an eye on the cryptocurrency market. It provides realtime data and insights that can help users make informed investment decisions and stay abrea st of the latest industry trends and developments.
Addressing the shortcomings of traditional voting systems, such as fraud, lack of transparency, and inefficiency, this paper proposes a decentralized voting model based on the Polygon blockchain. The model leverages the inherent characteristics of blockchain, including anti-tampering, transparency, and security, to establish a secure and trustworthy voting mechanism. By utilizing smart contract, the model automates voting, enforces rules, records ballots in real-time, and prevents double voting, thereby significantly reduces human errors and election fraud. It also ensures privacy, eliminates coercion, and realizes on-chain tallying using the Paillier homomorphic encryption. The model is analyzed in terms of security and deployed on Polygon, a sidechain of Ethereum. Comparisons with other schemes demonstrate that this model is also optimized for reducing gas cost.
In recent years, the convenience and potential use of crypto-assets such as Bitcoin and Ethereum have attracted increasing attention. On the other hand, there have been reports of attacks on the blockchain networks that support crypto-assets in an attempt to steal other users’ assets. In the past, research on attack observation against blockchains has used techniques such as holding real crypto-assets to lure attackers into honeypots or falsifying balances to attackers. However, these methods risk losing crypto-assets to attackers or being exposed as honeypots to attackers. To solve these problems, we propose a new RPC (Remote Procedure Call) honeypot method that returns the wallet address of another partya. holding a high balance in response to an attacker’s request, thereby luring the attacker without having the real crypto-assets. Our experimental evaluation shows that this method can attract more attackers than the method with zero-balance wallets and can observe more sophisticated attacks. Furthermore, we proposed a risk reduction strategy for crypto-asset theft by applying the idea of our method. In the log analysis process, we devised a new clustering method using the number of times an attacker executes a specific method as a feature. By applying this method, we successfully classified attackers based on their objectives, demonstrating the efficient analysis of vast amounts of log data.