B. N. Das, Babul Chandra Sarker, Amit Saha, Kanchon Kumar Bishnu · 9 authors
The exponential growth of cryptocurrency implementation in the USA has brought with it a surge in correlated risks, particularly in the form of scams that exploit the relative novelty and complexity of digital currencies. The primary objective of this study was to develop machine algorithms for identifying fraud trends in cryptocurrency transactions. By employing complex analysis, this research project attempted to identify certain trends and behaviors that fall under a variety of scams, providing a platform for effective detection and counter-strategies. This study will have a definite objective in terms of Bitcoin, Ethereum, and other high-profile cryptocurrencies in America when it comes to scam analysis. The scam-related transaction dataset comprised in-depth information regarding suspicious fraud activity in the cryptocurrency environment, such as a specific ID for a transaction, timestamps, values for transactions, and labels distinguishing between suspicious and legitimate activity. A variety of proven models were selected such as Logistic Regression, Random as well Multinomial Naive Bayes, where each model had its respective weaknesses and strengths. The Random Forest algorithm attained the highest accuracy, nearing perfection which underscores its robustness and reliability in classifying both legitimate and fraudulent reports. To effectively counter fraud in cryptocurrencies, U.S. policies must be strengthened with a merger of machine intelligence in them. Regulatory agencies have to work towards developing a system that encourages exchanges to utilize complex analysis for fraud detection, perhaps in terms of reduced compliance burden for entities with effective anti-fraud controls in position. Leveraging AI insights can go a long way in supporting investigations into scams in cryptocurrencies conducted by governments. By utilizing machine algorithms trained with datasets of past scams, governments can monitor and follow illicit fund flows through the blockchain with ease.
Ethereum, the first blockchain platform to support smart contracts, has become a target for various cybercrimes, particularly financial frauds like Ponzi schemes. Ponzi schemes on Ethereum are known as Smart Ponzi Schemes (or Ponzi Contracts) and have caused huge financial losses. Current Ponzi contract detection models face three main challenges: simple opcode sequence processing does not effectively distinguish Ponzi from non-Ponzi contracts, single-feature-based models lack accuracy, and reliance on transaction records hinders early detection. To address these issues, this paper proposes a Multi-Feature Ponzi Scheme Detection Model (MFDPonzi). MFDPonzi tracks the changes in stack, memory, and storage parameters during the execution of smart contracts, reconstructing opcode sequences and extracting diverse features, including semantic and developer features. Finally, a multi-feature fusion algorithm is used to enhance model stability. Additionally, MFDPonzi can identify Ponzi contracts at the early stage of smart contract creation without relying on transaction data. Experimental results show that MFDPonzi achieves an 85.9% recall and an 88.7% F-score on Ethereum smart contracts, outperforming baselines in both performance and robustness.
Detecting Ethereum phishing scams is extremely urgent. In this paper, we propose a novel Hybrid Attention Model for Ethereum phishing scams detection called HATTM to solve the problem of irregular transaction series in Ethereum, fully extract account features and then improve detection performance. Specifically, we take a novel perspective by regarding each transaction of an account as a separate amount-time point to handle irregular data. In the hybrid attention model, we capture intra-account and inter-account trading features through intra-account attention of EPS-FORMER and inter-account attention of EPSGAT, respectively. We further extract Intra-account and Inter-account statistical features to enrich the account representation. The complete representation of accounts is composed of the above four types of features to detect phishing accounts. Experimental results on the real-world Ethereum dataset show that HATTM outperforms existing models and is far ahead in the recall, which indicates that our model can effectively detect Ethereum phishing scams.
The integration of blockchain with Information-Centric Networking (ICN) enhances content distribution efficiency in areas such as the Internet of Things (IoT) and 5G/6G communications. This integration implies that the network state information of ICN can significantly impact consensus efficiency. However, the Ethereum Casper FFG consensus algorithm overlooks the network heterogeneity among consensus nodes, leading to a potential bottleneck in consensus efficiency, especially when nodes with inferior network quality participate. To address this issue, this paper proposes a multidimensional reputation model based on an ICN-enabled blockchain architecture. The model combines on-chain stake and network contributions to evaluate the reputation of ICN consensus nodes. Furthermore, a reputation-based hybrid consensus mechanism, RepuICN, is introduced, which enhances the network layer of the Casper FFG algorithm. This mechanism selects higher-reputation ICN consensus nodes as proposers for checkpoint blocks, mitigating the impact of network latency fluctuations on block propagation. Additionally, RepuICN improves block propagation efficiency through ICN multicast and caching techniques. Simulation results show that, under identical conditions with a network of 5000 nodes and 2% ICN nodes, RepuICN reduces broadcast latency by 17% for regular blocks and 61.4% for checkpoint blocks and achieves 3.4 times higher transaction throughput than Casper FFG.
In recent years, the proliferation of malware has reached unprecedented levels, leading to escalating cybercrime costs. Signatures extracted by static analysis of files have been widely adopted for malware detection: vendors maintain databases of known malware signatures that are shared with registered users. The recent literature has proposed the use of private and consortium (thus, permissioned) blockchains for spreading signatures among blockchain users. These approaches require controlled access to enhance trust and accountability but restrict the widespread sharing of up-to-date signatures because users must be registered. In this paper, we present a novel technique that leverages a public blockchain to enable the massive dissemination of malware signatures among any users since a public blockchain is permissionless. On the other hand, the use of a public blockchain introduces new challenges related to security and data privacy, which our solution solves. The main benefit and outcome of our solution is that any users can securely access and verify malware signatures facilitating real-time detection of malicious files. We implemented our solution in Ethereum and exploited a smart contract written in Solidity to demonstrate that our approach is highly cost-effective.
Krishna Nivash. J Krishna Nivash. J, Dr. V. Vijayakumar Dr. V. Vijayakumar
Blockchain Based Cross Network calling is an innovative decentralized peer-to-peer (P2P) communication system leveraging WebRTC and blockchain technology. This paper explores the system architecture, scalability mechanisms, and potential future enhancements. We present an in-depth analysis of its hybrid decentralized architecture, discuss the scalability challenges in P2P networks, and propose future enhancements including Layer-2 blockchain solutions, decentralized signaling, and AI integration. The findings suggest that decentralized audio communication can be significantly improved through advanced architectural designs and optimized scalability solutions. This paper provides a comprehensive exploration of Cross Network-based decentralized communication, emphasizing its implications for privacy, security, and future enhancements in Web3 applications.
Phishing is a serious threat to cryptocurrency networks; Bitcoin and Ethereum are prime targets for these attacks. This paper discusses some aspects of phishing attacks on these platforms. While the simpler architecture of Bitcoin leads to more direct phishing attempts, the more complex ecosystem in Ethereum introduces a wide range of attack vectors through dApps and smart contracts. A comparative analysis of phishing attacks in both blockchains shows that while both have their fair share of attacks, Bitcoin seems to bear the brunt of phishing attacks. Current defense strategies, like 2FA and anti-phishing tools, as well as recommendations for increasing network security against phishing are discussed in this paper. Understanding these phishing mechanisms is crucial in strengthening the security of blockchain platforms and mitigating future attacks.
The Ethereum blockchain operates as a decentralized platform, utilizing blockchain technology to distribute smart contracts across a global network. It enables currency and digital value exchange without centralized control. However, the exponential growth of online commerce has created a fertile ground for a surge in fraudulent activities such as money laundering and phishing, thereby exacerbating significant security vulnerabilities. To combat this, our article introduces an ensemble learning approach to accurately detect fraudulent Ethereum blockchain transactions. Our goal is to integrate a decision-making tool into the decentralized validation process of Ethereum, allowing blockchain miners to identify and flag fraudulent transactions. Additionally, our system can assist governmental organizations in overseeing the blockchain network and identifying fraudulent activities. Our framework incorporates various data pre-processing techniques and evaluates multiple machine learning algorithms, including logistic regression, Isolation Forest, support vector machine, Random Forest, XGBoost, and recurrent neural network. These models are fine-tuned using grid search to enhance their performance. The proposed approach utilizes an ensemble of three distinct models (Random Forest, extreme gradient boosting (XGBoost), and support vector machine) to further improve classification performance. It achieves high scores of over 98% across key classification metrics like accuracy, precision, recall, and F1-score. Moreover, the approach is suitable for real-world usage, with an inference time of 0.13 s.
S V Bhaskar, C M Harshith, Santhosh Krishna B V, K. Ashok · 5 authors
This paper discusses the architecture, implementation, and evaluation of a blockchain-based chat application that relies on Ethereum's decentralized framework. This project's main objective is ensuring that message transactions are safe and transparent, without involving a central authority, increasing data privacy and lowering the risk of data breaches. The application uses Solidity for the smart contracts and Web3.js to connect the front end, marking a move toward decentralized applications (DApps). Implementation details, security considerations, and testing outcomes are discussed to position blockchain as a viable platform for privacy-focused communication solutions.
This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development. High-quality watermarks adhering to the detectability-fidelity-robustness tri-objective are limited due to codes' low-entropy nature. Watermark verification, however, often needs to reveal the signature and requires re-encoding new ones for code reuse, which potentially compromising the system's usability. To overcome these challenges, RoSeMary obtains high-quality watermarks by training the watermark insertion and extraction modules end-to-end to ensure (i) unaltered watermarked code functionality and (ii) enhanced detectability and robustness leveraging pre-trained CodeT5 as the insertion backbone to enlarge the code syntactic and variable rename transformation search space. In the deployment, RoSeMary uses zero-knowledge proofs for secure verification without revealing the underlying signatures. Extensive evaluations demonstrated RoSeMary achieves high detection accuracy while preserving the code functionality. RoSeMary is also robust against attacks and provides efficient secure watermark verification.
A. Bendada, Abdelaziz Amara Korba, Mouhamed Amine Bouchiha, Yacine Ghamri‐Doudane
The rapid growth of decentralized finance (DeFi) has led to the widespread use of automated agents, or bots, within blockchain ecosystems like Ethereum, Binance Smart Chain, and Solana. While these bots enhance market efficiency and liquidity, they also raise concerns due to exploitative behaviors that threaten network integrity and user trust. This paper presents a decentralized federated learning (DFL) approach for detecting financial bots within Ethereum Virtual Machine (EVM)-based blockchains. The proposed framework leverages federated learning, orchestrated through smart contracts, to detect malicious bot behavior while preserving data privacy and aligning with the decentralized nature of blockchain networks. Addressing the limitations of both centralized and rule-based approaches, our system enables each participating node to train local models on transaction history and smart contract interaction data, followed by on-chain aggregation of model updates through a permissioned consensus mechanism. This design allows the model to capture complex and evolving bot behaviors without requiring direct data sharing between nodes. Experimental results demonstrate that our DFL framework achieves high detection accuracy while maintaining scalability and robustness, providing an effective solution for bot detection across distributed blockchain networks.
The aim of this research is to raise awareness regarding cryptocurrency fraud. In this context, the study focuses on cryptocurrency investment frauds and provides an evaluation from the perspective of new media literacy. Throughout the research process, a total of 969 complaints were analyzed under the categories of "Cryptocurrency Investment Fraud" and "Cryptocurrency and Victim Complaints" on the Şikayetvar platform. Adopting an exploratory approach, the complaints were coded under various themes using content and thematic analysis methods. The analysis process was conducted using MAXQDA 24, a qualitative data analysis software. The findings reveal that the theme with the highest frequency among types of fraud is "Fake Coin/Token" (337), illustrating the strategies employed by cryptocurrency fraudsters to deceive investors through fraudulent projects and assets. Additionally, the theme "Withdrawal and Transaction Request Rejection" (159) reflects the difficulties faced by users in conducting transactions and withdrawing their funds on legitimate platforms, showcasing how fraudulent platforms delay their victims. Furthermore, scams conducted through Telegram channels (173) have garnered attention, highlighting the significant role social media platforms play in fraudulent activities. Cryptocurrency frauds underscore the deficiencies in users' new media literacy and emphasize the importance of financial literacy and new media literacy education in an environment where fraud is prevalent.
Abstract— Remote electronic voting promises increased accessibility but remains constrained by persistent challenges related to coercion in unsupervised environments, credential compromise, and the difficulty of sustaining long-term voter trust. While coercion-resistant approaches commonly rely on revoting, most existing systems treat credential loss or recovery as an administrative exception, often reintroducing identity linkage or trusted intermediaries and offering limited means for voters or observers to verify that an election unfolded as intended. This paper presents Arcaunt, a remote voting architecture that elevates anonymous credential recovery to a first-class security property and integrates it directly into the voting lifecycle. The architecture introduces an Anonymous Recovery Channel (ARC), enabling voters to revoke and replace compromised credentials without identity disclosure or reliance on administrator discretion. Recovery is logically and operationally decoupled from ballot casting. This preserves ballot secrecy and prevents temporary compromise of credentials, devices, or voter autonomy from becoming a permanent loss of voting control. Arcaunt builds on established cryptographic mechanisms, including publicly verifiable bulletin boards, commitment-based ballots, and unlinkable bearer credentials. These components provide ballot integrity and verifiability without exposing voter identities and form the foundation on which revoting, recovery, and auditability are composed. Individual assurance is provided through deferred, non-transferable verification mechanisms: voters receive a receipt at ballot submission, while verification becomes possible only after election closure, preventing real-time feedback that could enable coercion while still allowing voters to confirm that their final valid ballot was recorded and included. At the system level, integrity is enforced through an append-only, publicly auditable ledger and deterministic “last valid vote” counting rules, ensuring that administrative database access cannot alter election outcomes without detection. The architecture explicitly bounds its threat model, acknowledging limits against global traffic analysis and continuous coercion while constraining failures to be temporary and non-scalable. We analyze the security properties of the proposed system under realistic adversarial assumptions and evaluate a prototype implementation, demonstrating that anonymous recovery, coercion-resistant revoting, individual verification, and public auditability can be combined efficiently without reliance on trusted administrators or specialized hardware. Keywords—e-voting, arcaunt, anonymous recovery channel (arc), coercion resistance, sha-3, digital democracy, govtech, zero-knowledge proofs.
Zubaida Rehman, Mark Gregory, Iqbal Gondal, Hai Dong · 5 authors
This paper presents a comprehensive study on eclipse attacks in blockchain networks by describing how eclipse attacks work, their effects, detection, and prevention. In this context, understanding and controlling network-level attacks, such as eclipse attacks, is an essential task in relation to assurance and reliability for decentralized systems that utilize blockchain technology. An eclipse attack is a sequence of network-layer attacks that monopolize the connections to a target node to isolate it from the rest of the network. Eclipse attacks that focus on node discovery manipulation, can have a substantial impact on a blockchain network, by increasing transaction computation cost, transaction censorship, and consensus disruption. We studied eclipse attacks on a blockchain network. The attack vectors were associated with node discovery manipulation, network partitioning, and information flow exploitation. This paper also reviews state-of-the-art detection methods and prevention strategies, shedding light on their effectiveness and limitations. Awareness of eclipse attacks and their effect provides the motivation for further research in developing practical and resilient security measures for blockchain networks.
Iori Suzuki, Yin Minn Pa Pa, Anh Thi Van Nguyen, Katsunari Yoshioka
Decentralized Finance (DeFi) token scams have become one of the most prevalent forms of fraud in Web-3 technology, generating approximately $241.6 million in illicit revenue in 2023 [1].Detecting these scams requires analyzing both on-chain data, such as transaction records on the blockchain, and off-chain data, such as websites related to the DeFi token project and associated social media accounts.Relying solely on one type of data may fail to capture the full context of fraudulent activities.While on-chain data is publicly accessible due to the transparency inherent in blockchain technology, off-chain data often disappears alongside DeFi scam campaigns, making it difficult for the security community to study these scams.To address this challenge, we propose a dataset comprising more than 550 thousand archived web and social media data as offchain data, in addition to on-chain data related to 32,144 DeFi tokens deployed on Ethereum blockchain from September 24, 2024 to January 14, 2025.This dataset aims to support the security community in studying and detecting DeFi token scams.To illustrate its utility, our case studies demonstrated the potential of the dataset in identifying patterns and behaviors associated with scam tokens.These findings highlight the dataset's capability to provide insights into fraudulent activities and support further research in developing effective detection mechanisms.
In recent years, blockchain technology, as an innovative information technology, has received widespread attention in academia and industry. However, its limitations in mechanism design and the completeness of supporting infrastructure, combined with the immaturity of security concepts, have exposed blockchain systems to severe security threats and challenges. This study aims to address critical security issues in blockchain technology by proposing a blockchain security architecture based on Web attack principles. The architecture adopts a negotiated consensus mechanism and integrates real-time protection techniques from the field of cybersecurity, designing an innovative framework capable of identifying and restricting malicious nodes. With dynamic isolation as its core strategy, the architecture detects abnormal behaviors and temporarily isolates malicious nodes, preventing further damage to the blockchain network. The results demonstrate that this architecture successfully addresses the bottlenecks of inadequate targeted defense in existing blockchain systems and significantly improves operational efficiency and security. Experimental validation indicates that the architecture exhibits substantial practical value in scenarios such as decentralized finance (DeFi) and supply chain management, laying a solid foundation for the widespread application of blockchain technology in real-world settings.