Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
This chapter introduces blockchain network structures in both UTXO- and account-based systems. It begins with Bitcoin’s transaction and address graphs, showing how Satoshi Nakamoto’s design defines network topology and enables modeling through transaction graphs, address graphs, and chainlets. Privacy coins like Monero and Zcash extend this framework with ring signatures and zero-knowledge proofs that obscure data but still permit partial inference. Ethereum shifts focus to account-based networks, covering coin and token transactions, contract interactions, and trace analysis. Ripple concludes the chapter with credit networks built on trust lines and path-based settlements, showing how global credit flows can be modeled graphically.
Digital assets, a broad term encompassing crypto-currencies, tokens and digital representations of value, have transformed the financial landscape over the past decade. Ghana has transitioned from an unregulated crypto-currency environment to a structured, licensed digital assets space following the passage of the Virtual Asset Service Providers (VASP) Act 2025 Act 1154. Unlike traditional assets, digital assets exist exclusively in electronic form and are secured through cryptographic techniques, most notably blockchain technology. Bitcoin, Ethereum, and other crypto-currencies serve as prominent examples, alongside digital tokens used in decentralized finance (DeFi), security tokens, and stablecoins. They may serve a variety of functions, including use as a medium of exchange, for investment, or as a means of accessing goods, services, or applications within specific ecosystems. These assets include crypto-currencies, tokens, stablecoins, and other blockchain-based instruments. Global digital assets represent any item of value securely stored and managed via distributed ledger or blockchain technology. Encompassing cryptocurrencies, stablecoins, tokenized securities, and non-fungible tokens (NFTs), the sector has rapidly expanded into mainstream finance, revolutionizing global payments, portfolio diversification, and record-keeping. This article discusses the challenges and opportunities of digital currencies and the way forward. This research shows that digital currencies have advantages like making transactions faster, cheaper, and more accessible and also reveals a lot of disadvantages like creating major risks concerning compliance with regulations, cybersecurity, and potential impacts on monetary policy. The review emphasizes the necessity for robust regulatory frameworks for digital assets. It supports both innovation and stability for the digital currencies. It suggests that policymakers and financial institutions should adapt to changes and face the challenges by integrating digital currencies with existing systems. Overall, this review highlights the potential of digital currencies to transform finance. It also stresses the importance of focusing on the challenges they pose to ensure they can coexist successfully with traditional financial systems. As digital currencies evolve, the Ghanaian traditional financial sector faces pressure to adapt, with CBDCs, in particular, being explored as a secure, regulated alternative to volatile crypto-assets. nThe findings revealed that the central bank must adopt robust regulatory and licensing frameworks must align with Virtual Assets Service Providers (VASP) (Act 2025 Act 1154) by enforcing strict licensing for exchanges and custodians while adhering to AML/CFT (Anti-Money Laundering) directives. Also, the Bank of Ghana and the Securities and Exchange Commission must develop a comprehensive public education programme on the digital assets in the financial ecosystem. Given the novelty of the trend of criminality in the digital asset space, the establishment of specialized cybercrime courts to be presided over by judges, proficient in digital law and cybercrime would be of immense benefit. The mandate of such courts could be to expedite trials and ensure thorough adjudication of complex cyber cases. This would have the combined effect of empowering the Ghana Police Service and Cyber-Security Authority to fully invest time, money, and human resources towards the investigation of cybercrime, as well as serve as a deterrent for criminal elements, ultimately protecting our citizens and providing justice for those seeking redress.
Ethereum non-fungible tokens (NFTs) implement ownership, transfer, authorization, and metadata operations through smart contracts, making contract vulnerabilities a direct risk to digital assets. Existing static analyzers provide efficient rule-based screening but can struggle with application-specific logic, whereas unconstrained large language model analysis may be distracted by irrelevant code or produce inconsistent outputs. We present a vulnerability-detection method that combines vulnerability-focused code slicing, an ERC-721-oriented knowledge base, and constrained DeepSeek analysis. Regular-expression patterns locate candidate statements for reentrancy, integer overflow or underflow, and timestamp dependence. A structure-aware context-window algorithm then extracts line-numbered code slices. DeepSeek analyzes each slice using explicit decision rules and a fixed output schema, and the resulting records support automated batch processing. On 450 NFT contract samples, the full configuration produced 437 positive labels, corresponding to a reported positive-label rate of 97.1%. Removing the external knowledge base reduced this rate to 87.11%, while analyzing complete contracts without the knowledge base reduced it to 73.78%. These results indicate that focused code context and domain constraints materially affect the detector's reported output.
Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
In this chapter, you will learn how to write, deploy, and interact with smart contracts using Solidity. We will cover fundamental data types, control structures, functions, and contract organization. You will understand the Ethereum Virtual Machine, how contracts send and receive Ether, and how to use events, modifiers, and visibility specifiers. The chapter also introduces reference types like arrays and mappings, common security practices, and techniques for optimizing gas usage.
Fei Wu, Thomas Thiery, Stefanos Leonardos, Carmine Ventre
Block production in modern blockchains is increasingly shaped by economic gains that arise from control over transaction ordering. These gains—known as Maximal Extractable Value (MEV)—have led to concerns about centralization and market power among blockchain consensus participants. To address these concerns, Ethereum introduced Proposer-Builder Separation (PBS), in which specialized block builders compete in block building auctions to construct blocks on behalf of validators. The current implementation of PBS, MEV-Boost, mediates this competition through an open-bid first-price ascending auction, termed the MEV-Boost auction. This paper analyzes the strategic incentives of builders in MEV-Boost auctions. We develop an agent-based simulation framework and apply empirical game-theoretic analysis to study how asymmetries in network latency and access to MEV opportunities shape bidding behavior and market concentration. Our findings show that while latency differences mildly affect builder incentives, MEV opportunity access fundamentally alters equilibrium strategies: builders with privileged access to MEV opportunities bid less aggressively, maintain higher profit margins, dominate market share, and reduce proposer revenue. These effects contribute to centralization and oligopolistic outcomes in the builder market. To validate these findings, we further analyze an idealized symmetric benchmark market where builders have comparable latency and MEV access. Under such settings, the auction behaves as expected—bidding is competitive, proposer revenue is higher, and the market is more decentralized—confirming that the observed inefficiencies arise specifically from the asymmetries present in practice.
We provide a large-scale empirical audit of DEX routing using 2.98 million WETH-USDC swaps on Ethereum. Comparing realized routes with optimized benchmarks, we measure an average shortfall of 2.02 bps per trade or \$24 million. To attribute losses, we introduce three reproducible optimal benchmarks: a Support-Constrained Optimum (SCO) that evaluates split quality conditional on the pools actually used; a Full-Venue Optimum (FVO) that considers all available pools to quantify the value of broader pool access; and a Gas-Aware FVO (G-FVO) that augments FVO with gas costs to capture the trade-off between additional pool usage and gas expenditure. Computing these benchmarks at scale is enabled by a bisection-based algorithm for optimal routing across multiple pools for the same token pair. Two regularities emerge. First, information timeliness is crucial: moving from execution-time state to one-block lagged state optimization significantly raises mean shortfall and additional delays further degrade performance, albeit with diminishing increments; evaluated on the same stale snapshots, realized routes lie closer to optimal, indicating timing-mismatch as a key component. Second, inefficiency is heterogeneous and heavy-tailed: small trades suffer higher percentage losses, while a few extreme outliers dominate the aggregate dollar shortfalls. Finally, we demonstrate that sandwiching attacks drive a significant fraction of routing sub-optimality. Our benchmark protocol and algorithm offer a rigorous, reproducible basis for evaluating and improving information-timely, gas-aware routing.
Zero-Knowledge Ethereum Virtual Machines (zkEVMs) secure Ethereum rollups by generating zero-knowledge proofs that guarantee off-chain execution correctness. However, subtle implementation bugs (e.g., incorrect gas accounting) can lead to valid proofs certifying semantically faulty states, thereby silently defeating cryptographic guarantees. Formal verification via SMT solvers can prevent this, but is bottlenecked by specification: current zkEVM development practice lacks automated methods to translate Rust opcode handlers into verification models. Current practices rely on unsustainable manual specifications, while LLM-based approaches suffer from hallucination and lack formal guarantees. To address this, we propose VeriSynth, a framework that synthesizes executable Python/Z3 verification models from Rust zkEVM code. VeriSynth enforces a hybrid paradigm: an LLM acts strictly as a formalization frontend to translate code into symbolic constraints, while an SMT solver serves as the correctness arbiter. To handle complex multi-component state transitions, VeriSynth integrates semantic decomposition, retrieval-grounded prompting, and verification-guided auto-repair into a closed-loop pipeline. We evaluate VeriSynth on the first source-level zkEVM verification benchmark, encompassing both correct and faulty opcode implementations. VeriSynth achieves a bug detection rate of over 90%, substantially outperforming direct and conversational LLM baselines, as well as a production-grade handwritten mutation-testing suite. Ablation studies confirm that each pipeline component is critical to the framework's overall effectiveness.
Gouher Ahmed, Hamza Naim, Aqila Rafiuddin, Mohammed Nizamuddin · 5 authors
This study deals with the performance analysis and volatility estimation of conventional indices including Dow Jones, S&P 500, Brent Oil, Crude Oil and Gold and cryptocurrencies including Bitcoin and Ethereum for the period January 3, 2011 to November 26, 2021 for all of the indices except Ethereum for which the period chosen was from March 10, 2016 to November 26, 2021 due to late incorporation of the cryptocurrency. The stationarity, heteroscedasticity, and serial correlation of the data were considered. Time series regression using the GARCH model is applied for performance analysis and volatility estimation. GARCH (1, 1) estimates show the high performance of cryptocurrencies over the conventional indices, except Gold, which was insignificant, with Ethereum followed by Bitcoin being the most volatile among the different indices. However, Gold remains inert in response to the different indices. However, although the cryptocurrencies add to the country’s revenue, thus minimizing the deficits, there should still be proactive policies and practices to prevent the exploitation of stakeholders, especially for the sake of minority ones.
This dataset accompanies the paper An Architectural and Empirical Study of Root-Only Zero-Knowledge Verification and contains the scripts, intermediate artifacts, and published results used to reproduce the empirical evaluation. The repository is organized around two experiment groups: Blockchain-Side Deployment and Verification: deployment and Groth16 proof verification on Ethereum Sepolia and zkSync Sepolia, including contract sources, Merkle-tree inputs, Groth16 proofs, and blockchain measurement CSVs and figures. ZKP proving and off-chain verification: Constraint-count comparison — Groth16 R1CS constraint counts and expanded PLONK gate counts for Merkle-tree depths 5–15, with measurement scripts and summary CSVs/figures. Proving-time comparison — off-chain Groth16 and PLONK proving benchmarks across depths 5–15, including proving scripts, generated witness/proof/key artifacts, and benchmark CSVs/figures.
Cryptocurrency is no longer that of a topic solely for traders and tech enthusiasts, as crypto ETFs have worked their way into mainstream retirement accounts, bringing with them many questions to financial planners. The question this study addresses is whether small Bitcoin and Ethereum ETF allocations actually improve the risk-adjusted performance of a traditional balanced retirement portfolio. To find out, five different portfolio constructions were tested using real ETF return data, with performance measured across Sharpe ratio, Sortino ratio, maximum drawdown, and correlation, all with quarterly rebalancing built in. Every portfolio that included cryptocurrency outperformed the standard baseline on risk-adjusted return metrics, though drawdown did increase as the allocation grew. What this tells us is that small, structured cryptocurrency allocations have the potential to improve retirement portfolio performance for the right investor, but suitability still needs to be worked out on an individual basis, something financial planners can take directly into their practice.
This dataset accompanies the paper An Architectural and Empirical Study of Root-Only Zero-Knowledge Verification and contains the scripts, intermediate artifacts, and published results used to reproduce the empirical evaluation. The repository is organized around three experiment groups: On-chain verification — deployment and Groth16 proof verification on Ethereum Sepolia and zkSync Sepolia, including contract sources, Merkle-tree inputs, Groth16 proofs, and blockchain measurement CSVs and figures. Constraint-count comparison — Groth16 R1CS constraint counts and expanded PLONK gate counts for Merkle-tree depths 5–15, with measurement scripts and summary CSVs/figures. Proving-time comparison — off-chain Groth16 and PLONK proving benchmarks across depths 5–15, including proving scripts, generated witness/proof/key artifacts, and benchmark CSVs/figures. Shared setup files include Circom circuits, Merkle-tree preparation scripts, circuit inputs, and compiled circuit artifacts. Most of the generated data is produced by the provided scripts and does not need to be included separately if the reproduction pipeline is documented.
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Formal Methods in Verification
Physical Unclonable Functions (PUFs) and Hardware Security
Federated Learning (FL) enables collaborative model training across decentralized participants without sharing raw data. However, existing FL systems remain vulnerable to Byzantine attacks and suffer from a lack of accountability, verifiability, and economic incentives for honest participation. We present BFL-Guard, a novel blockchain-orchestrated federated learning framework integrating: (i) zk-SNARK-based zero-knowledge gradient proofs, (ii) an on-chain Byzantine-tolerant aggregation smart contract, and (iii) a tokenized incentive protocol (FedToken). BFL-Guard stores model checkpoints as IPFS hashes anchored on Ethereum, ensuring tamper-evident auditability. Experiments on CIFAR-10 and Shakespeare benchmarks demonstrate 95.2% and 87.6% accuracy in IID and Non-IID settings, surpassing all baselines while converging 12.4% faster even under 30% Byzantine injection.
Similar to all other cryptocurrency platforms, Ethereum is constantly confronted with malicious activities. In recent years, research efforts have targeted the detection and mitigation of malicious activities and the associated accounts within the Ethereum ecosystem. Yet, the malicious accounts represent only a small visible part of the substantial collaborative network enabling these activities. In this work, we offer the first analysis of this collaborative network and the corresponding affiliate accounts that often remain hidden from detection. We present enEtherShield, an enhanced framework for detecting affiliate accounts that assist malicious accounts in the related Ethereum scams. Our research findings lay the foundation for the detection of the collaborative network enabling Ethereum scams.
Aditya Saraf, Ioannis Kaklamanis, Sarisht Wadhwa, Fatima Elsheimy
Censorship resistance is the defining advantage of blockchains over their centralized counterparts. Yet block proposers censor transactions for many reasons, from legal consequences to economic incentives. We study economically-incentivized censorship, modeled by an adversary who bribes proposers to exclude a target transaction, and define the economic censorship resistance (eCR) of a transaction as the adversary's expected cost of successful censorship divided by the user's expected payment for inclusion. Single-proposer systems are structurally weak by this measure: under a first-price auction the adversary need only match the user's bid, and fee burning pushes eCR to a few percent of what the user pays. We therefore turn to multiple concurrent proposers (MCP), where block capacity is divided among $n$ proposers and the block is the union of their sub-blocks. While MCP can substantially increase the cost of censorship by requiring the adversary to bribe many proposers, it also introduces transaction duplication, reducing throughput. The resulting trade-off depends critically on the transaction fee mechanism (TFM), which determines how fees are shared among competing proposers. We create a game theoretic model where validators construct blocks from a shared mempool, subject to an adversary's bribery attempt. We provide an algorithm that solves for the mixed equilibrium of a given mempool, which is characterized by the probability of including each transaction. This algorithm works for a wide class of TFMs, and allows us to calculate the expected throughput and censorship resistance for any bid distribution. We then use simulations to show how the eCR and throughput vary as the number of proposers increases. We compare three TFMs, finding that the duplication-penalizing TFM dominates the others across many settings. We also validate our findings with empirical Ethereum data.