The decentralized, open-source architecture of blockchain technology, exemplified by the Ethereum platform, has transformed online transactions by enabling secure and transparent exchanges. However, this architecture also exposes the network to various security threats that cyber attackers can exploit. Detecting suspicious behaviors in account on the Ethereum blockchain can help mitigate attacks, including phishing, Ponzi schemes, eclipse attacks, Sybil attacks, and distributed denial of service (DDoS) incidents. The proposed system introduces an ensemble stacking model combining Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and a neural network (NN) to detect potential threats within the Ethereum platform. The ensemble model is fine-tuned using Bayesian optimization to enhance predictive accuracy, while explainable artificial intelligence (XAI) tools-SHAP, LIME, and ELI5-provide interpretable feature insights, improving transparency in model predictions. The dataset used comprises 9,841 Ethereum transactions across 52 initial fields (reduced to 17 relevant features), encompassing both legitimate and fraudulent records. The experimental findings demonstrate that the proposed model achieves a superior accuracy of 99.6%, outperforming that of other cutting-edge methods. These findings demonstrate that the XAI-enabled ensemble stacking model offers a highly effective, interpretable solution for blockchain security, strengthening trust and reliability within the Ethereum ecosystem.
We introduce a zero-knowledge cryptocurrency mixer framework that allows groups of users to set up a mixing pool with configurable governance conditions, configurable deposit delays, and the ability to refund or confiscate deposits if it is suspected that funds originate from crime. Using a consensus process, group participants can monitor inputs to the mixer and determine whether the inputs satisfy the mixer conditions. If a deposit is accepted by the group, it will enter the mixer and become untraceable. If it is not accepted, the verifiers can freeze the deposit and collectively vote to either refund the deposit back to the user, or confiscate the deposit and send it to a different user. This behaviour can be used to examine deposits, determine if they originate from a legitimate source, and if not, return deposits to victims of crime.
We show that a replicated state machine (such as a blockchain protocol) can retain liveness in a strategic setting even while facing substantial ambiguity over certain events. This is implemented by a complementary protocol called "Machine II", which generates a non-ergodic value within chosen intervals such that no limiting frequency can be observed. We show how to implement this machine algorithmically and how it might be applied strategically as a mechanism for "veiling" actions. We demonstrate that welfare-enhancing applications for veiling exist for users belonging to a wide class of ambiguity attitudes, e.g. Binmore (2016), Gul and Pesendorfer (2014). Our approach is illustrated with applications to forking disputes in blockchain oracles and to Constant Function Market Makers, allowing the protocol to retain liveness without exposing their users to sure-loss.
Mohammad H. Amin, Jack Raymond, Daniel Kinn, Gunnar Miller · 10 authors
We propose a blockchain architecture in which mining requires a quantum computer. The consensus mechanism is based on proof of quantum work, a quantum-enhanced alternative to traditional proof of work that leverages quantum supremacy to make mining intractable for classical computers. We have refined the blockchain framework to incorporate the probabilistic nature of quantum mechanics, ensuring stability against sampling errors and hardware inaccuracies. To validate our approach, we implemented a prototype blockchain on four D-Wave(TM) quantum annealing processors geographically distributed within North America, demonstrating stable operation across hundreds of thousands of quantum hashing operations. Our experimental protocol follows the same approach used in the recent demonstration of quantum supremacy [King et al. Science 2025], ensuring that classical computers cannot efficiently perform the same computation task. By replacing classical machines with quantum systems for mining, it is possible to significantly reduce the energy consumption and environmental impact traditionally associated with blockchain mining while providing a quantum-safe layer of security. Beyond serving as a proof of concept for a meaningful application of quantum computing, this work highlights the potential for other near-term quantum computing applications using existing technology.
Juan Beccuti, Thunj Chantramonklasri, Matthias Hafner, Nicolas Oderbolz
This paper examines how various categories of Ethereum stakers respond to changes in the consensus issuance schedule, and the potential impact of such changes on the composition of the staking market. To this end, we have develop and calibrate a game-theoretic model of the Ethereum staking market, incorporating strategic interactions between various staking agents. Our findings suggest that solo stakers may be more sensitive to variations in staking rewards than ETH holders using centralized exchanges or liquid staking providers. This increased sensitivity is driven not only by the cost structure of solo staking, but also by the competitive dynamics between different staking solutions in the market. When faced with a downward-sloping issuance schedule, staking agents compete for limited staking yields, and their choice of staking supply affects the revenues of other stakers. Therefore, the presence of other staking methods with access to MEV revenues and other DeFi yield sources when staking, as well as inattentive stakers, puts competitive pressure on solo stakers. Consequently, our model predicts that a reduction in issuance is likely to crowd out solo stakers. We present preliminary empirical evidence to support this result, using an instrumental variable estimation approach to estimate the yield elasticity of staking supply for different staking categories.
Airdrops issued by platforms are to distribute tokens, drive user adoption, and promote decentralized services. The distributions attract airdrop hunters (attackers), who exploit the system by employing Sybil attacks, i.e., using multiple identities to manipulate token allocations to meet eligibility criteria. While debates around airdrop hunting question the potential benefits to the ecosystem, exploitative behaviors like Sybil attacks clearly undermine the system's integrity, eroding trust and credibility. Despite the increasing prevalence of these tactics, a gap persists in the literature regarding systematic modeling of airdrop hunters' costs and returns, alongside the theoretical models capturing the interactions among all roles for airdrop mechanism design. Our study first conducts an empirical analysis of transaction data from the Hop Protocol and LayerZero, identifying prevalent attack patterns and estimating hunters' expected profits. Furthermore, we develop a game-theory model that simulates the interactions between attackers, organizers, and bounty hunters, proposing optimal incentive structures that enhance detection while minimizing organizational costs.
Federated Learning (FL) has emerged as a promising paradigm in distributed machine learning, enabling collaborative model training while preserving data privacy. However, despite its many advantages, FL still contends with significant challenges -- most notably regarding security and trust. Zero-Knowledge Proofs (ZKPs) offer a potential solution by establishing trust and enhancing system integrity throughout the FL process. Although several studies have explored ZKP-based FL (ZK-FL), a systematic framework and comprehensive analysis are still lacking. This article makes two key contributions. First, we propose a structured ZK-FL framework that categorizes and analyzes the technical roles of ZKPs across various FL stages and tasks. Second, we introduce a novel algorithm, Verifiable Client Selection FL (Veri-CS-FL), which employs ZKPs to refine the client selection process. In Veri-CS-FL, participating clients generate verifiable proofs for the performance metrics of their local models and submit these concise proofs to the server for efficient verification. The server then selects clients with high-quality local models for uploading, subsequently aggregating the contributions from these selected clients. By integrating ZKPs, Veri-CS-FL not only ensures the accuracy of performance metrics but also fortifies trust among participants while enhancing the overall efficiency and security of FL systems.
This study aims to assess the validity and precision of employing a multivariate LSTM model compared to traditional models and stock analysis techniques for predicting the price of the cryptocurrency BTC. The research incorporates a feature elimination technique to optimize price predictions across various time intervals by removing non-essential and redundant features, including economic factors. In the case of BTC, with a finite total supply of 21 million coins, an increase in popularity generally leads to a surge in price. To gauge BTC’s popularity, tweet frequency and Google search trends were considered as input factors. Additionally, traditional indicators like USD, Gold and the Volatility Index (VIX) were used to measure the stock market atmosphere. The LSTM model’s performance was benchmarked against other models such as RNNs, ANN, SVR and ARIMA. The LSTM model exhibiting superior learning in multivariate data, achieving an RMSE score of 268.83.
It is not uncommon for customers who intend to buy a used product in the secondary market to end up with a counterfeit because they have imperfect information about product authenticity . Blockchain is being piloted as a cutting-edge solution to this challenge. We use a two-period game to study the impact of utilizing blockchain to combat counterfeit products in the secondary market. We show that, even when the cost of implementing blockchain is negligible, the manufacturer can be better off incurring reputation damage than adopting blockchain. Further, the used goods reseller can be worse off from blockchain, even though that seller is not responsible for the implementation cost and benefits from blockchain’s signaling capability. We also demonstrate that the counterfeiter can benefit as a result of blockchain. When the quality of a fake product is sufficiently low, blockchain lowers consumer surplus . The winning situation of blockchain between the manufacturer, reseller, and customers is achieved only when the fake product is of intermediate quality. Blockchain can be powerful in situations when used products have a low perceived quality; otherwise, blockchain may not be ideal.
Blockchain technology has transformed information management through decentralization, security and immutability. However, a gap persists in its application for the issuance and verification of professional qualifications in education. This study presents a prototype developed in Python and Docker, designed to guarantee the authenticity and traceability of academic credentials through a hybrid blockchain network with six Docker nodes. The prototype includes processes such as initial data registration, node configuration, credential generation with QR codes and associative signature based on Byzantine consensus. During the signing stage, previously stored records are validated and authenticated, ensuring integrity before final credentials are generated. The peer-to-peer network ensures synchronization, decentralized storage and immutability of records. On average, initial title registration on the blockchain took 2.97 s, with block replication taking 0.02 s. Record signing had a latency of 0.96 s, with replication in 0.79 s, and Byzantine consensus took 0.12 s, all with moderate resource consumption. The generated titles, verifiable via QR codes, reinforce trust and reduce academic fraud. This model stands out for its practical and scalable approach, with potential for adaptation to other sectors. Future work should address the scalability and robustness of the system for more complex applications.
As cryptocurrency transactions continue to grow, detecting scams within transaction records remains a critical challenge. These transactions can be represented as dynamic graphs, where Neural Network Convolution (NNConv) models are widely used for detection. However, NNConv models suffer from model decay due to evolving transaction patterns, the introduction of new users, and the emergence of adversarial techniques designed to evade detection. To address this issue, we propose an automated, periodic hyperparameter optimization method based on proximal policy optimization (PPO), a reinforcement learning algorithm designed for dynamic environments. By leveraging PPO’s stable policy updates and efficient exploration strategies, our approach continuously refines hyperparameters to sustain model performance without frequent retraining. We evaluate the proposed method on a large-scale cryptocurrency transaction dataset containing 2,973,489 nodes and 13,551,303 edges. The results demonstrate that our method achieves an F1 score of 0.9478, outperforming existing graph-based approaches. These findings validate the effectiveness of PPO-based optimization in mitigating model decay and ensuring robust cryptocurrency scam detection.
This paper presents an advanced framework for analyzing cryptocurrency market microstructure through the integration of deep learning techniques and social media sentiment analysis. The proposed approach combines BERT-based sentiment analysis with market microstructure indicators to capture complex market dynamics. The framework processes multi-source data streams, including social media content and order book information, to generate comprehensive market insights. Experimental evaluation conducted on cryptocurrency market data from January 2022 to December 2023 demonstrates superior performance compared to traditional approaches. The model achieves 91.2% prediction accuracy and maintains a Sharpe ratio of 2.34 in trading simulations. The attention mechanism effectively identifies relevant market signals with 92.3% precision, while the temporal feature extraction module captures multi-scale market patterns. The applications have been successful with the capability of the ability to below 100 milliseconds, fit for high applications. The studies made for fields by creating the processing system for market microstructure focuses for commercial and investigators. The framework's performance stability across different market conditions validates its practical applicability in cryptocurrency trading and market analysis.
We investigate the impact of anonymity and privacy-preservation on cryptocurrency use. We find that privacy coins, which deploy advanced privacy-preserving technologies to enhance trader anonymity, experience a relative increase in usage compared to non-privacy coins following regulatory interventions aimed at countering illegal activities in cryptocurrency trading and use. However, the adoption of privacy coins decreases relative to non-privacy coins after the introduction of regulations restricting the use of privacy-preserving protocols. These findings underscore the significance of privacy as a driving factor in cryptocurrency adoption.
It is quite challenging to properly address the issues of digital assets and online identities by conventional estate rules in the era of digital technologies. Rising social media platforms, cryptocurrencies, non-fungible tokens (NFTs), and other virtual assets have made digital legacy complex. Current research highlights the constraints of existing estate laws for the administration of digital assets after death and the legal obstacles resulting from digital platform contractual limitations. The key challenges identified are assets classification, protection of privacy rights, and enforcement of policies on a wider scale. By comparing the global legal approaches and evolving trends in digital inheritance, a comprehensive framework including digital assets into estate planning has been proposed. A balanced legal framework ensuring fair distribution, protecting heirs' rights and building trust in the digital economy is the solution.
Inzamam Ul Haq, Muhammad Abubakr Naeem, Chunhui Huo, Walid Bakry
This study examines the interlinkages among diverse cryptocurrency classes and their multiscale relationship with media climate change concerns to examine how cryptocurrency returns respond to rising climate change concerns. The analysis includes 11 cryptocurrencies classified as dirty, gold-backed, energy, and sustainable and their behavior regarding media climate change concerns, including transition and physical risks. Using squared wavelet coherence and partial wavelet coherence (PWC) on daily data from January 1, 2014 to June 29, 2024, this study shows time-frequency-dependent market integration among cryptocurrency pairs. During rising climate change concerns, returns decrease for some cryptocurrencies while increasing for XRP, implying higher investors' trust in sustainable cryptocurrencies. PWC analysis reveals significant influence of climate change concerns on pairwise returns connectedness among various cryptocurrency classes. This study highlights the need for cryptocurrency traders to incorporate media climate change information into their investment decisions, contributing insights into using diverse crypto-assets for risk management. • We find high market integration after 2018 cryptocurrency crash. • PLG and gold-backed cryptos show weak dependence with respective cryptocurrencies. • Rising climate change concerns significantly increase PLG and XRP returns across time-frequency. • We find that transition risks predict cryptocurrency returns more than physical risks. • We find that climate change concerns drive cryptocurrency co-movements.
Basem Mohamed Elomda, Taher Abouzaid Abdelaty Abdelbary, Hesham Hassan, Kamal S. Hamza · 5 authors
The Multi-Layer Blockchain Security Model (MLBSM) proposed in 2024 was designed to safeguard Internet of Things (IoT) networks, as well as similar network architectures, against transaction privacy leakage in public blockchain systems. MLBSM also addresses critical issues like latency, ensuring faster transaction speeds through clustering and parallel processing. This paper presents a new extension to the Multi-Layer Blockchain Security Model (MLBSM). The proposed model is called the Enhanced Multi-Layer Blockchain Security Model (EMLBSM). The proposed EMLBSM will solve latency issues by compressing and reducing the layers of the MLBSM through merging layer2 and layer3 in the MLBSM. This paper describes the required enhanced solution for latency and scalability problems that were found in the MLBSM.
Bitcoin burn addresses are addresses where bitcoins can be sent but never retrieved, resulting in the permanent loss of those coins. Given Bitcoin's fixed supply of 21 million coins, understanding the usage and the amount of bitcoins lost in burn addresses is crucial for evaluating their economic impact. However, identifying burn addresses is challenging due to the lack of standardized format or convention. In this paper, we propose a novel methodology for the automatic detection of burn addresses using a multi-layer perceptron model trained on a manually classified dataset of 196,088 regular addresses and 2,082 burn addresses. Our model identified 7,905 true burn addresses from a pool of 1,283,997,050 addresses with only 1,767 false positive. We determined that 3,197.61 bitcoins have been permanently lost, representing only 0.016% of the total supply, yet 295 million USD on November 2024. More than 99% of the lost bitcoins are concentrated in just three addresses. This skewness highlights diverse uses of burn addresses, including token creation via proof-of-burn, storage of plain text messages, or storage of images using the OLGA Stamps protocol.
Adi Wolfson, Gerard Khaladjan, Yotam Lurie, Shlomo Mark
Cryptocurrencies are decentralized digital financial services that do not physically exist in the world of tangible products and goods, and therefore purportedly offer some positive environmental sustainability features. However, since they are based on blockchain technology, which requires a relatively large input of energy, their climatic impact is not benign. Furthermore, they are very volatile and characterized by low levels of transparency and control, thus creating some negative economic and social sustainability effects. Stablecoins, which are a pegged type of cryptocurrency, exhibit much less volatility and have higher levels of management and interoperability. This raises the following question: are stablecoins more sustainable compared to other cryptocurrencies? To explore this, a sustainability assessment was conducted, comparing cryptocurrencies and stablecoins across environmental, social, and economic dimensions while identifying the key characteristics of sustainability. It was found that stablecoins can mitigate the economic and social risks associated with cryptocurrencies and thus increase their overall sustainability. Moreover, since stablecoins are managed and governed to a greater extent, a key consideration in their development is the selection and implementation of more appropriate mechanisms that can reduce energy use and enhance sustainability. Finally, stablecoins offer more effective—and not just more efficient—solutions, based on value co-creation between several providers and a customer.
Mamoon M. Saeed, Rashid A. Saeed, Mohammad Kamrul Hasan, Elmustafa Sayed Ali · 8 authors
After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations.
ABSTRACT While non‐fungible tokens (NFTs) have emerged as a significant blockchain application, research has largely focused on market dynamics rather than consumer behavior. Through in‐depth interviews with 21 NFT consumers and a netnographic analysis of Discord interactions (109,517 words), this study develops a comprehensive framework explaining the evolution from initial purchase to sustained or discontinued interest in NFTs. The findings reveal that while profit expectations drive initial purchases, strong community bonds and social identity formation are crucial for maintaining engagement. Specifically, active community participation, both before and after purchases, creates a self‐reinforcing cycle where engagement directly influences NFT valuation. However, unfulfilled profit expectations and perceived community abandonment by project leaders often lead to disillusionment. The study extends the Need‐to‐Belong and Social Identity Theory to the digital asset context, demonstrating how NFT communities serve as platforms for identity expression and emotional support, transcending purely financial motivations. For practitioners, the findings suggest that sustainable NFT projects should prioritize community building and transparent leadership over short‐term speculation. This research provides the first longitudinal analysis of NFT consumer behavior, offering insights into how digital assets can create enduring value through social engagement rather than merely speculative trading.
Brittany Hagedorn, Jeremy Cooper, Benjamin Loevinsohn, Valentina Martufi
BACKGROUND: To improve service delivery of Nigeria's primary health care (PHC) system, the government tested two approaches for facility-level financing: performance-based financing (PBF) and decentralized facility financing (DFF). Facilities also had increased autonomy, supervision, and community oversight. We examine how the intervention approach and funding level affected breadth of services and structural quality. METHODS: We use health facility surveys previously collected in 2014 and 2017, covering three years of implementation, in which districts were randomly assigned PBF or DFF and compared to matched districts in control states. We use log-linear regressions and non-parametric statistics to estimate the effect size of the financing approach and level of funding per capita. RESULTS: Service availability was highest in PBF facilities, while DFF also outperformed control on most measures. Results showed that structural readiness and service offerings both increased with more funding, especially under DFF. DFF and PBF facilities were better equipped to provide services that they claimed to offer, which was not the case for controls. Overall, PBF outperformed DFF, partially explained by funding levels. The rate of offering complimentary services followed a pattern of easiest-to-hardest to deliver. CONCLUSION: PBF and DFF both improved the breadth and structural quality of services, although DFF performance was more sensitive to funding levels. Improvements were observed at relatively low levels of funding, but larger investments were associated with better performance. Most DFF facilities exceeded the performance of higher-funded controls, implying that funding was more valuable in the context of autonomy, increased supervision, and community oversight.
Anne Broadbent, Alex B. Grilo, Nagisa Hara, Arthur Mehta
In a proof of knowledge (PoK), a verifier becomes convinced that a prover possesses privileged information. In combination with zero-knowledge proof systems, PoKs play an important role in security protocols such as in digital signatures and authentication schemes, as they enable a prover to demonstrate possession of certain information (such as a private key or a credential), without revealing it. A PoK is formally defined via the existence of an extractor, which is capable of reconstructing the key information that makes a verifier accept, given oracle access to any accepting prover. We extend this concept to the setting of a single classical verifier and multiple quantum provers and present the first statistical zero-knowledge (ZK) PoK proof system for problems in QMA. To achieve this, we establish the PoK property for the ZK protocol of Broadbent, Mehta, and Zhao (TQC 2024), which applies to the local Hamiltonian problem. More specifically, we construct an extractor which, given oracle access to a provers' strategy that leads to high acceptance probability, is able to reconstruct the ground state of a local Hamiltonian. Our result can be seen as a new form of self-testing, where, in addition to certifying a pre-shared entangled state, the verifier also certifies that a prover has access to a quantum system, in particular, a ground state; this indicates a new level of verification for a proof of quantumness.
André Augusto, André Vasconcelos, Miguel Correia, Luyao Zhang
The number of blockchain interoperability protocols for transferring data and assets between blockchains has grown significantly. However, no open dataset of cross-chain transactions exists to study interoperability protocols in operation. There is also no tool to generate such datasets and make them available to the community. This paper proposes XChainDataGen, a tool to extract cross-chain data from blockchains and generate datasets of cross-chain transactions (cctxs). Using XChainDataGen, we extracted over 35 GB of data from five cross-chain protocols deployed on 11 blockchains in the last seven months of 2024, identifying 11,285,753 cctxs that moved over 28 billion USD in cross-chain token transfers. Using the data collected, we compare protocols and provide insights into their security, cost, and performance trade-offs. As examples, we highlight differences between protocols that require full finality on the source blockchain and those that only demand soft finality (\textit{security}). We compare user costs, fee models, and the impact of variables such as the Ethereum gas price on protocol fees (\textit{cost}). Finally, we produce the first analysis of the implications of EIP-7683 for cross-chain intents, which are increasingly popular and greatly improve the speed with which cctxs are processed (\textit{performance}), thereby enhancing the user experience. The availability of XChainDataGen and this dataset allows various analyses, including trends in cross-chain activity, security assessments of interoperability protocols, and financial research on decentralized finance (DeFi) protocols.