This thesis examines the feasibility of implementing a retail Central Bank Digital Currency (CBDC) in Qatar using an Ethereum-based blockchain architecture. The research is motivated by Qatar's strategic objectives, including improving payment infrastructure, enforcing monetary policy, fintech innovation and infrastructure modernization. A literature review examined global CBDC initiatives, blockchain architectures, consensus mechanisms, and programmability features, focusing on governance models, privacy frameworks and interoperability.The findings of this literature review informed the design of a modular, four-layered system aligned with Qatarâs institutional structure and regulatory requirements. The proposed system leverages Hyperledger Besu with QBFT consensus for deterministic finality and Tessera for transaction-level privacy. A suite of smart contracts was developed to manage compliance (KYC/AML), role-based access, cash compatibility and cross-border transactions. Implementation and deployment were conducted using a private network of 4 validator nodes, configured via Docker, with supporting infrastructure built on a dedicated virtual machine.Functional and empirical testing confirmed the network's ability to maintain consensus under fault scenarios, enforce compliance through smart contracts and achieve limited confidentiality among privacy groups. Scalability testing revealed throughput of up to 127 transactions per second with up to 8 validators and block intervals of 1 second; however, performance degraded with 12 validators, identifying current limits in scalability and resource efficiency. Similarly, privacy enforcement via Tessera was effective in basic cases but encountered limitations with standard ERC-20 contract compatibility.In conclusion, the study confirms that an Ethereum-based blockchain is viable for controlled deployment in Qatar. This is primarily due to its programmability, which ensures regulatory compliance and fosters the development of innovative financial products. However, limitations in privacy and scalability necessitate further research into scaling techniques and alternative privacy solutions to meet the requirements of nationwide retail CBDC implementations. Alternatively, dedicated blockchain frameworks and ERC standards for CBDCs can offer a more sustainable option that natively supports the scalability and privacy requirements of central banking.
<p><strong>This paper contributes to the literature by</strong> presenting a reproducible framework for designing and testing fair on-chain governance systems. It introduces <em>CryptoKen</em>, an Ethereum-based token using quadratic voting to reduce plutocracy and enhance participation in decentralized organizations, achieving high usability (SUS 82.5) and 92% verified test coverage.</p>
Modern demand for blockchain scaling demanded the developmentof sharding as a viable solution that facilitates parallel processingwhile supporting cross-shard communications. The implementation ofsharding provides excellent scalability to decentralized systems, but itentails enormous complexities in maintaining integrity of smart contracts across different shards. This study analyzes pivotal deploymentslike Ethereum 2.0, NEAR protocol, and Polkadot. This innovationidentifies vulnerabilities on atomicity, consistency, and validator security when executing decentralized applications (dApps). This researchreviews contemporary literature and emerging technologies to identifykey security risks such as replay attacks, shard takeover, and data unavailability events. Various methodologies for reducing vulnerabilities,including atomic commit protocols, dynamic validator assignment,zk-SNARKs, and SP-Chain architecture, are assessed in this report.This study evaluates the implementation of an Escrow smart contract that utilizes Practical Byzantine Fault Tolerance (PBFT) and Proofof-Stake (PoS) coordination protocols through evaluation tests. Thisresearch work analyzes code using both static and dynamic methods to detect security weaknesses in contracts and then recommendssolutions that enhance the robustness of these contracts.The intelligent contract is tested for performance in single-shardmode as well as cross-shard operations. It experiences quick responsetimes and effective data processing with single-shard execution butfaces latency, receipt verification issues, and synchronization difficulties with cross- shard operations. This study findings are that organizations require more effective systems for communication of databetween shards. This research suggests future improvement throughthe addition of zero-knowledge proofs, dynamic re-sharding procedures, and decentralized arbitration secure and scalable smart contractdeployment methods. The proposed solution is applicable to use casessuch as freelancing, crowdfunding, and supply chain processes, withdemonstrations using real-life examples.As a result of the comprehensive assessment, improved safe smartcontract frameworks for next generation blockchain systems are developed, making it easier to implement decentralized applications widelyin scale network contexts.
The application of blockchain technology provides the opportunity to effectively coordinate the production process and logistics tasks. From the first step of creating individual products to the process of their storage, transportation, and placement on store shelves, every single step can be continuously monitored, thereby guaranteeing the appropriate quality. In the blockchain, data can be stored securely, unauthorized persons cannot modify the data, as it is a very complex task. As a result, the research aims to examine which blockchain technology is the most suitable for logistics and production processes, considering both efficiency and security aspects. By applying the smart contract, the third party can be excluded from the process, thereby increasing efficiency and security, while also reducing costs. As part of the practical implementation of the research, a blockchain that meets the conditions listed above will be created, and an Ethereum-based smart contract will also be written using the Solidity programming language. The research motivation is to create a blockchain and smart contract that are capable together of replacing centralized data storage while eliminating its security shortcomings. The purpose of creating the blockchain is to implement distributed Peer-to-Peer node-based data storage for logistics data in a decentralized environment. By creating a smart contract, blockchain-based data storage can become an automated process, making the server and system administrator redundant. The research aims to achieve the highest possible level of data storage security so that the data storage is performed automatically, thus reducing the tasks of the system administrator. The key point of this is the selection of the most optimal smart contract platform and the creation of the contract in which the data storage conditions can be defined. Through the practical application of the smart contract, some logistics processes are carried out automatically, thereby further increasing efficiency.
This dissertation presents an empirical analysis of decentralized finance through three distinct studies. By harnessing the power of on-chain data, this research delves into the mechanics of DeFi, exploring how we assess financial risk and measure market efficiency. Furthermore, it directly addresses the significant economic exter nalities of the sector by measuring the annualized energy draw of Bitcoinâs global mining industry. The first article, Risk forecasting comparisons in decentralized fi nance: An approach in constant product market makers (this research was presented at the Annual Conference of the Banco Central do Brasil (2024) and subsequently published in the Journal of Economics and Business, Volume 133, 2025; 2024 JCR Impact Factor: 3.4), pioneers by comparing risk measures between centralized and decentralized exchanges. By employing a vast dataset from Uniswap V2 Liquid ity Pool (LP) and conducting a meticulous comparative analysis of Value-at-Risk (VaR) and Expected Shortfall (ES) forecasts, using both parametric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and the non para metric DeepAR neural network, it demonstrates that liquidity provision generally presents a statistically significant lower risk profile than an equivalent buy and hold strategy. A critical exception exists for stablecoin pairs, where protocol fees become the primary risk driver. This research provides a crucial empirical foundation for developing sophisticated, model informed risk management tools in Decentralized Finance (DeFi). The second article, Pricing efficiency in cryptocurrencies: the case of centralized and decentralized markets (published in the Journal of Economics and Business, Volume 133, 2025; 2024 JCR Impact Factor: 3.4), offers a comparative analysis of market efficiency between liquidity pool mechanisms and traditional order book systems. Utilizing Asymmetric Multifractal Detrended Fluctuation Analysis (asym metric MF-DFA) and the Thermal Optimal Path (TOP) method on data from Binance and Uniswap V2, it reveals that algorithmic LP can achieve superior weak form market efficiency compared to Centralized Exchanges (CEX) order books. The study conclusively identifies the Decentralized Exchanges (DEX) as the lead market in price discovery, transmitting signals to its centralized counterpart with an average lag of under 24 hours. This efficiency premium, driven by radical transparency and high velocity arbitrage, intensified significantly following the Ethereum 2.0 upgrade. The third article, Bitcoins halving events and the fractal nature of mining energy consumption, investigates the long term impact of Bitcoins programmed monetary policy on its mining energy consumption behavior. Applying asymmetric MF-DFA to data from the Cambridge Centre for Alternative Finance, it uncovers the com plex, multifractal nature of Bitcoins energy dynamics, showing an evolution from persistent, heterogeneous behavior before halving events toward more efficient and random consumption characteristics after each subsequent halving. The analysis provides the first documented evidence of a significant cross-chain effect, showing that Ethereumâs transition to Proof of Stake (PoS) consensus triggered an immediate and sustained decrease in the persistence of Bitcoinâs energy consumption patterns. This finding reveals previously unrecognized interconnectivity between seemingly independent networks and creates new pathways for assessing the environmental relationships within blockchain ecosystems.
As decentralized finance (DeFi) expands, Ethereumâs role as the backbone for digital asset exchange, smart contracts, and financial protocols has grownâbut so has its exposure to fraud. Phishing, money laundering, and malicious contracts exploit its openness. Existing ML and deep learning models often lack the balance between speed, accuracy, and explainability needed for real-time blockchain analysis. High-performing models like transformers are accurate but too resource-heavy and opaque. This study leverages TabNetâa sparse-attention deep learning model optimized for tabular Ethereum transaction data. It dynamically selects relevant features during training, enhancing both interpretability and efficiency. With an accuracy of 0.86, precision of 0.80, and F1-score of 0.79, TabNet outperforms traditional models in fraud detection while remaining lightweight and transparent. Its feature-level insights make it ideal for environments where trust, latency, and transparency are crucial. The results position TabNet as a scalable, practical alternative for fraud detection in blockchain ecosystems.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard
ABSTRACT Cryptocurrency blockchain networks safeguard digital assets using cryptographic keys, with wallets playing a critical role in generating, storing, and managing these keys. Wallets, typically categorized as hot and cold, offer varying degrees of security and convenience. However, they are generally softwareâbased applications running on microcontrollers. Consequently, they are vulnerable to malware and sideâchannel attacks, allowing perpetrators to extract private keys by targeting critical algorithms, such as ECC, which processes private keys to generate public keys and authorize transactions. To address these issues, this work presents EthVault, the first hardware architecture for an Ethereum hierarchically deterministic cold wallet, featuring hardware implementations of key algorithms for secure key generation. Also, an ECC architecture resilient to sideâchannel and timing attacks is proposed. Moreover, an architecture of the child key derivation function, a fundamental component of cryptocurrency wallets, is proposed. The design minimizes resource usage, meeting market demand for small, portable cryptocurrency wallets. FPGA implementation results validate the feasibility of the proposed approach. The ECC architecture exhibits uniform execution behavior across varying inputs, while the complete design utilizes only 27%, 7%, and 6% of LUTs, registers, and RAM blocks, respectively, on a Xilinx Zynq UltraScale+ FPGA.
Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses focus on static code analysis, public mempool screening, attacker contract detection, or trusted off-chain monitors, none of which prevents exploits submitted through private relays or malicious contracts that execute within the same block. We present the first decentralized, fully on-chain learning framework that: (i) performs gas-prohibitive computation on Layer-2 to reduce cost, (ii) propagates verified model updates to Layer-1, and (iii) enables gas-bounded, low-latency inference inside smart contracts. A novel Proof-of-Improvement (PoIm) protocol governs the training process and verifies each decentralized micro update as a self-verifying training transaction. Updates are accepted by PoIm only if they demonstrably improve at least one core metric (e.g., accuracy, F1-score, precision, or recall) on a public benchmark without degrading any of the other core metrics, while adversarial proposals get financially penalized through an adaptable test set for evolving threats. We develop quantization and loop-unrolling techniques that enable inference for logistic regression, SVM, MLPs, CNNs, and gated RNNs (with support for formally verified decision tree inference) within the Ethereum block gas limit, while remaining bit-exact to their off-chain counterparts, formally proven in Z3. We curate 298 unique real-world exploits (2020 - 2025) with 402 exploit transactions across eight EVM chains, collectively responsible for $3.74 B in losses. We demonstrate that on-chain ML governed by PoIm detects previously unseen attacks with over 97% attack detection accuracy and 82.0% F1. A single inference, such as one made via an external call, typically incurs zero cost. Fully on-chain inference consumes 57,603 gas (â $0.18) for linear models, 143,647 gas (â $0.49) for CNN(F2, K1), and 506,397 gas (â $1.77) for CNN(F8, K4) on L1 (e.g., Ethereum). Our results show that practical and continually evolving DeFi defenses can be embedded directly in protocol logic without trusted guardians, and our solution achieves highly cost-effective protection while filling a critical gap between vulnerability scanners and real-time transaction screening.
Ozan Solmaz, Lioba Heimbach, Yann Vonlanthen, Roger Wattenhofer
Layer 2 rollups are rapidly absorbing DeFi activity, securing over $40 billion and accounting for nearly half of Ethereum's DEX volume by Q1 2025, yet their MEV dynamics remain understudied. We address this gap by defining and quantifying optimistic MEV, a form of speculative, on-chain MEV whose detection and execution logic reside largely on-chain in smart contracts. As a result of their speculative nature and lack of off-chain opportunity verification, optimistic MEV transactions frequently decide not to execute any trades. In this work, we focus on cyclic arbitrage, which we find is predominantly executed as optimistic MEV on Layer 2s. Using our multi-stage identification pipeline on Arbitrum, Base, and Optimism, we show that in Q1 2025, transactions from cyclic arbitrage contracts account for over 50% of on-chain gas on Base and Optimism and 7% on Arbitrum, driven mainly by "interaction" probes (on-chain computations searching for arbitrage). This speculative probing indicates that cyclic arbitrage on Layer 2s is predominantly executed as optimistic MEV and contributes to generally keeping blocks on Base and Optimism persistently full. Despite consuming over half of on-chain gas, these optimistic MEV transactions pay less than one quarter of total gas fees. Cross-network comparison reveals divergent success rates, differing patterns of code reuse, and sensitivity to varying sequencer ordering and block production times. Finally, OLS regressions link optimistic MEV trade count to ETH volatility, retail trading activity, and DEX aggregator usage. Together, these findings show that optimistic MEV has become a major source of persistent spam-like transaction activity on Layer 2s, dominating blockspace with low-value probes and reshaping the composition of on-chain activity.
Dakai Kang, Junchao Chen, Tien Tuan Anh Dinh, Mohammad Sadoghi
The rise of cryptocurrencies like Bitcoin and Ethereum has driven interest in blockchain database technology, with smart contracts enabling the growth of decentralized finance (DeFi). However, research has shown that adversaries exploit transaction ordering to extract profits through attacks like front-running, sandwich attacks, and liquidation manipulation. This issue affects blockchains where block proposers have full control over transaction ordering. To address this, a more fair transaction ordering mechanism is essential. Existing fairness protocols, such as Pompe and Themis, operate on leader-based consensus protocols, which not only suffer from low throughput caused by the single-leader bottleneck, but also allow adversarial block proposers to manipulate transaction ordering. To address these limitations, we propose a new framework, FairDAG, that runs fairness protocols on top of DAG-based consensus protocols. FairDAG improves protocol performance in both throughput and fairness quality by leveraging the multi-proposer design and validity property of DAG-based consensus protocols. We conducted a comprehensive analytical and experimental evaluation of two FairDAG variants - FairDAG-AB and FairDAG-RL. Our results demonstrate that FairDAG outperforms prior fairness protocols in both throughput and fairness quality.
Pavloff, Ulysse, Amoussou-Guenou, Yackolley, Tucci-Piergiovanni, Sara
This paper investigates whether following the fork-choice rule in the Ethereum PoS consensus protocol constitutes a Nash equilibrium - i.e., whether the protocol that maintains the canonical chain in Ethereum is incentive-compatible. Specifically, we explore whether selfish participants may attempt to manipulate the fork-choice rule by forking out previous blocks and capturing the rewards associated with those blocks. Our analysis considers two strategies for participants: the obedient strategy, which adheres to the prescribed protocol, and the cunning strategy, which attempts to manipulate the fork-choice rule to gain more rewards. We evaluate the conditions under which selfish participants might deviate from the obedient strategy. We found that, in a synchronous system, following the prescribed fork-choice rule is incentive-compatible. However, in an eventually synchronous system, the protocol is eventually incentive-compatible - that is, only a limited number of proposers will find it profitable to fork the chain during the synchronous period. After this sequence of cunning proposers, subsequent proposers will find it more profitable to follow the protocol.
Ethereum is a multifaceted ecosystem shaped by diverse commitments and aspirations. This dissertation focuses on a relatively understudied micro-culture within Ethereum, known as âpublic goodsâ represented by a subcultural group known as âRegensâ. Unlike the dominant financial use cases of blockchain, Regens aim to harness blockchain technologies for communal and participatory purposes, often through blockchain operated digital institutions known as decentralized autonomous organizations (DAOs). I focus on Giveth - a well-known public goods DAO within the Ethereum ecosystem. Using an empirical approach grounded in ethnography, I map its culture, governance practices, and labour dynamics. Through this analysis, I examine how blockchain mediated infrastructures can both enable and constrain political life. In particular, I trace how blockchain infrastructures challenge traditional conceptions of democracy through the concept of âcoordination,â a central organizing principle that characterizes DAO governance. Examining how Givethâs governance mechanisms operate in practice, I argue that while Giveth and similar public goods DAOs endeavor to address the problems inherent in existing traditional political systems, they often end up producing the same plutocratic outcomes. Drawing on Marxist theory of labour and value, I also demonstrate that despite Givethâs noble intentions to reimagine non-profit work through blockchain, the reality is that members continue to sacrifice significant time and resources in pursuit of the projectâs mission. This reveals a fundamental contradiction within such initiatives: while projects like Giveth aim to dismantle hegemonic neoliberal structures, they paradoxically reproduce and extend many of the same conditions they seek to overcome by embedding market computational logics and game-theoretic principles at the core of their operational design. Nevertheless, I emphasize on the potential of DAOs to foster alternative world-building practices, serving as spaces for the development of creative political imaginaries that can advance collective organizational efforts and greater participation when designed with intention.
Die zunehmende Verbreitung verteilter Datenspeicher wie dem InterPlanetary File System erfordert robuste Sicherheitsmechanismen, um den Schutz sensibler Daten in dezentralen Umgebungen zu gewĂ€hrleisten. Diese Masterarbeit prĂ€sentiert ein auf Smart Contracts basierendes Authentifizierungs- und Zugangskontrollsystem, das den Ethereum Attestation Service nutzt, um Rollen und Berechtigungen als nicht ĂŒbertragbare Attestierungen zu verwalten. Durch die Integration von IPFS und clientseitiger VerschlĂŒsselung wird ein hybrides Modell entwickelt, das öffentliche Datenspeicher durch granular definierte Zugriffskontrollbedingungen absichert. Die Arbeit evaluiert das System in einem praxisnahen Anwendungsfall einer Decentralized Autonomous Organization und demonstriert die Skalierbarkeit durch die Nutzung von Layer-2-Blockchains wie Scroll. Die Analyse der Kosteneffizienz von Smart Contract-Interaktionen zeigt, dass das Framework eine dezentrale, interoperable Verwaltung von Zugriffsrechten ermöglicht und dabei Transaktionskosten durch die Nutzung von Layer-2-Netzwerken signifikant reduziert. Zudem wird die Machbarkeit einer rollenbasierten Zugriffskontrolle ohne zentrale Instanz nachgewiesen, die gleichzeitig die IntegritĂ€t und VerfĂŒgbarkeit der Daten gewĂ€hrleistet. Die Arbeit leistet einen Beitrag zur Erforschung blockchainbasierter Sicherheitslösungen und bietet eine Blaupause fĂŒr Organisationen, die dezentrale Speicherlösungen in offenen Ăkosystemen einsetzen. ZukĂŒnftige Arbeiten könnten die Integration weiterer dezentraler IdentitĂ€tsmodelle oder die Erweiterung um attributbasierte Zugriffskontrollmechanismen untersuchen, um die PrivatsphĂ€re und FlexibilitĂ€t des Systems weiter zu optimieren.
Shahbaz Siddiqui, Sufian Hameed, Muhammad Rafi, Syed Attique Shah · 5 authors
The development of blockchain consensus mechanisms has put sustainability, scalability, and efficiency first in the list of cryptocurrency studies. As Bitcoin still uses the energy-intensive Proof-of-Work (PoW) algorithm, Ethereum is in the process of switching from PoW to Proof-of-Stake (PoS) with the Ethereum Merge in September 2022. The cause of this change was the rising energy prices, bottlenecks in scalability, and increased concerns among the global population regarding the environmental effects of PoW-based mining. Our study provides a rationale for the Ethereum transition by comparing and contrasting the volume of transactions, network performance through hash rate, and electricity consumption of Bitcoin and Ethereum in their PoW stages.With the help of both historical trend and predictive modelling in the form of the ARIMA framework, the obtained results showed that Bitcoin and Ethereum had different dynamics of growth under PoW, which was increased hash and energy expenditures but limited throughput. The ARIMA-based analysis achieved a mean absolute error (MAE) of 27.76, a root mean square error (RMSE) of 31.16, and a mean absolute percentage error (MAPE) of 26.08, which provided an overall forecast accuracy of 73.9%. These measures show that there is a medium predictive reliability, and volatility is mainly caused by sudden changes in mining intensity and market forces. The predictions further indicated that under PoW, Ethereum would have experienced a steep rise in operational energy consumption and congestion of its network transactions, which would have weakened network sustainability.Results thus confirm that the switch of Ethereum to PoS was not only an environmental need but also a strategic optimization of a transaction to scale higher, operational overhead reduction, and alignment with sustainable and ESG (Environmental, Social, and Governance) goals on a global scale. This piece leads to a better comprehension of the trade-offs between PoW and PoS, as well as the Ethereum consensus shift as the turning point in the evolution of blockchainsâestablishing a precedent of other cryptocurrencies that can select a balance between performance, security, and sustainability.
Common blockchain protocols are monolithic, i.e., their security relies on a single assumption, e.g., honest majority of hashing power (Bitcoin) or stake (Cardano, Algorand, Ethereum). In contrast, so-called optimistic approaches (Thunderella, Meshcash) rely on a combination of assumptions to achieve faster transaction liveness. We revisit, redesign, and augment the optimistic paradigm to a tiered approach. Our design assumes a primary (Tier 1) and a secondary (Tier 2, also referred to as fallback) blockchain, and achieves full security also in a tiered fashion: If the assumption underpinning the primary chain holds, then we guarantee safety, liveness and censorship resistance, irrespectively of the status of the fallback chain. And even if the primary assumption fails, all security properties are still satisfied (albeit with a temporary slow down) provided the fallback assumption holds. To our knowledge, no existing optimistic or tiered approach preserves both safety and liveness when any one of its underlying blockchain (assumptions) fails. The above is achieved by a new detection-and-recovery mechanism that links the two blockchains, so that any violation of safety, liveness, or censorship resistance on the (faster) primary blockchain is temporary - it is swiftly detected and recovered on the secondary chain - and thus cannot result in a persistent fork or halt of the blockchain ledger. We instantiate the above paradigm using a primary chain based on proof of reputation (PoR) and a fallback chain based on proof of stake (PoS). Our construction uses the PoR and PoS blockchains in a mostly black-box manner - where rather than assuming a concrete construction we distil abstract properties on the two blockchains that are sufficient for applying our tiered methodology. In fact, choosing reputation as the resource of the primary chain opens the door to an incentive mechanism - which we devise and analyze - that tokenizes reputation in order to deter cheating and boost participation (on both the primary/PoR and the fallback/PoS blockchain). As we demonstrate, such tokenization in combination with interpreting reputation as a built-in system-wide credit score, allows for embedding in our two-tiered methodology a novel mechanism which provides collateral-free, multi-use payment-channel-like functionality where payments can be instantly confirmed.
Blockchain technology, exemplified by platforms such as Ethereum and Bitcoin, is reshaping decentralised ecosystems and holds profound implications for socio-economic transformation within an increasingly volatile, uncertain, complex, and ambiguous (VUCA) global environment. Yet, its broader adoption is hindered by challenges related to scalability, security, environmental sustainability, governance, and interoperability. This study, grounded in the Technology-Organisation-Environment (TOE) framework, critically investigates these multifaceted barriers and proposes strategic interventions to optimise blockchain deployment. Employing a mixed-methods approach, the research integrates quantitative performance metrics with qualitative insights from expert interviews and case analyses. It evaluates Ethereum's transition to proof of stake (PoS), Bitcoin's evolving economic roles, and the expanding applications of blockchain in DeFi, asset tokenisation, and Central Bank Digital Currencies (CBDCs). Further, it explores governance innovations, privacy safeguards, and the emerging threat of quantum computing. The study presents empirically grounded, theoretically informed recommendations to enhance blockchain's scalability, security, and compliance, fostering ethical, inclusive, and sustainable digital futures.
Two MEV builders now produce nearly 80\% of Ethereum blocks. Block builders have the ability to reorder transactions on the blockchain in a way that can be harmful to participants. We estimate they would pay in the aggregate nearly \$14 million per month to ensure that they remained in the first quartile of the block. Sandwich attacks, in which a transaction is front-run, are frequent, averaging more than one per block. Gas fees on these transactions pay for nearly 15\% of the MEV payments to the validator. These attacks have especially large marginal effects and skew the distribution. Reforms such as gas fee priority or private transaction pools might be helpful.
Modern education is constantly adapting to the dynamically changing technological landscape, looking for innovative solutions to support learning processes. Among these solutions, blockchain technology is emerging as a promising area, potentially revolutionary for the field of education. The implementation of digital certificates in education enhances the efficiency, security, and global recognition of qualifications, making the process of verifying achievements more modern and transparent. The article analyzes the main features of using blockchain technologies in the education system, based on a number of fundamental theoretical concepts, and describes the author's software tool for the academic certification system. The core of the application is a decentralized database built on the Ethereum platform. The application utilizes smart contracts for secure issuance and verification of digital diplomas. The key modules of the application and their software implementation features are described. One of the features of the system under consideration is the implementation of a tokenization mechanism based on the Ethereum Request for Comments 20, ensures that these tokens are fungible and compatible with other tokens and applications within the Ethereum ecosystem. Examples of practical use of the application are also presented
Phishing attacks continue to pose a significant threat to the Ethereum ecosystem, accounting for a major share of Ethereum-related cybercrimes. To enhance the detection of such fraudulent transactions, this dissertation develops a comprehensive framework for machine learning-based phishing detection in Ethereum transactions. The framework addresses critical aspects such as feature selection, class imbalance, model robustness, and the vulnerability of detection models to adversarial attacks. By systematically evaluating these key practices, this work contributes to the development of more effective detection methods. The first part of the dissertation assesses the current state of phishing detection methods, identifying gaps in feature selection, dataset composition, and model optimization. We propose a systematic framework that evaluates these factors, providing a foundation for improving the overall performance and reliability of detection models. The second part explores the vulnerability of machine learning models, including Random Forest, Decision Tree, and K-Nearest Neighbors, to single-feature adversarial attacks. Through extensive experimentation, we analyze the impact of various adversarial strategies on model performance and uncover alarming weaknesses in existing models. However, the varied effects of these attacks across different algorithms present opportunities for mitigation through adversarial training and improved feature selection. Finally, the dissertation investigates how phishing detection models generalize across datasets, focusing on the role of preprocessing techniques such as feature engineering and class balancing. Our findings show that optimizing these techniques enhances model accuracy and robustness, making detection methods more adaptable to evolving threats. Overall, this work presents a comprehensive framework that addresses the critical elements of phishing detection in Ethereum transactions, offering valuable insights for the development of more robust and generalizable machine learning-based security models. The proposed framework has broad implications for improving blockchain security and advancing the field of phishing detection.