Abdou-Essamad Jabri, Mostafa Azizi, Cyril Drocourt, Gil Utard
Being propelled by the fourth industrial revolution (Industry 4.0), IoT devices and solutions are well adopted everywhere, ranging from home applications to industrial use, crossing through transportation, healthcare, energy, and so on. This wide use of IoT has not gone unnoticed, hackers are tracking the weakness of such a technology and threatening them continuously. Their security at various levels has become an important concern of professionals and researchers. This issue takes more risk, especially with the IoT variants, IIoT (Industrial IoT) and MIoT (Medical IoT). Many existing security solutions are adapted and proposed for addressing IoT security. In this paper, we are interested in exploring blockchain technology and we make a comparison of three free Blockchain platforms towards their applicability for MIoT context, namely Ethereum, Hyperledger Fabric and Corda. In general, Blockchain technology provides a decentralized, autonomous, trustless, and distributed environment. It is challenging to find a Blockchain platform that fits the MIoT context and performs well in terms of security. The retained platform should be deployed smartly to avoid its practical drawbacks related to energy-consuming and excessive computing.
Ovaj rad predstavlja razvoj prototipa decentralizirane društvene mreže temeljene na tehnologijama Web3. S obzirom na sve veće nepovjerenje korisnika prema tradicionalnim, centraliziranim društvenim mrežama, cilj je izraditi rješenje koje korisnicima omogućuje privatnost, sigurnost i vlasništvo nad vlastitim podacima. Aplikacija koristi blockchain Ethereum za upravljanje identitetom i interakcijama korisnika i Metamask za autentikaciju. Sadržaj se pohranjuje distribuirano putem sustava IPFS i mreže istorazinskih dionika Gun. Implementirane su funkcionalnosti poput objavljivanja sadržaja, spremanje objava, označavanje objava sa "sviđa mi se", komentiranja, slanja zahtjeva za prijateljstvo i drugih interakcija. Uz sve navedeno, aplikacija uključuje izravni (peer-to-peer) chat, te sustav za nagrađivanje korisnika putem ERC-20 tokena za aktivnosti, interakciju i kvalitetan sadržaj. Sustav je implementiran lokalno pomoću IPFS i GUN čvorova na dva uređaja. Postavljen je temelj za daljnju optimizaciju i širenje funkcionalnosti, kako bi se ostvarila stvarna, sigurna i decentralizirana mreža.
Ovaj rad prikazuje dizajn i implementaciju decentralizirane aplikacije za kvizove koja spaja prednosti blockchain tehnologije s praktičnošću Web2 tehnologija. Koristeći hibridnu arhitekturu on-chain i off-chain obrade podataka. Korišteni su Angular, Ethers.js, MetaMask na frontendu, Node.js/Express i MongoDB na backendu te Solidity pametni ugovori na ethereum virtual machine (EVM)-kompatibilnoj mreži (Polygon Amoy za testiranje). Arhitektura uključuje QuizFactory za kreiranje ins- tanci, osnovni Quiz za besplatne sesije i proširivi QuizWithFee koncept za nagradni fond. Testiranjem je utvrđena funkcionalnost sustava, ali i ukazan je linearni porast troškova s porastom broja igrača i pitanja. Rezultat je na kraju funkcionalna apli- kacija za live kvizeve u kojem sudionici nemaju on-chain troškove, ključni ishodi su javno provjerljivi na blockchain-u, a visoko-frekventne interakcije ostaju izvan lanca radi boljeg korisničkog iskustva i nižih troškova; sustav je modula
Traditional centralized systems often fail to prevent fraud and ensure data integrity, especially as cyber threats grow more complex. This paper proposes a blockchain-based framework enhanced with artificial intelligence to address these limitations. Blockchain provides secure, tamper-proof storage and smart contract–based access control, while AI enables realtime anomaly detection by analyzing behavioral patterns. The system is built using Ethereum smart contracts and machine learning models, with a modular architecture connecting frontend, backend, and AI components. Evaluation shows over 92% accuracy in fraud detection, efficient response times, and reliable audit trails. The approach proves scalable and suitable for sensitive sectors such as healthcare and finance, offering a secure, intelligent, and decentralized solution for modern data protection
The FTX collapse marked a significant shock to global crypto markets, prompting concerns about systemic contagion. This paper investigates the dynamic connectedness between cryptocurrencies, DeFi tokens, and tech stocks, focusing on the systemic impact of the FTX collapse. We decompose total, internal, and external connectedness across asset groups using a time-varying parameter VAR model. The results show that post-FTX, Bitcoin and Ethereum intensified their roles as core shock transmitters, while Tether consistently acted as a volatility absorber. DeFi tokens exhibited heightened intra-group spillovers and occasional external influence, reflecting structural fragility. Tech stocks remained largely insulated, with reduced cross-market linkages. Network visualizations confirm a post-crisis fragmentation, characterized by denser internal crypto-DeFi ties and weaker inter-group contagion. These findings have important policy implications for regulators, investors, and system designers, indicating the need for targeted risk monitoring and governance within decentralized finance.
The InterPlanetary File System (IPFS) has been successfully established as the de facto standard for decentralized data storage in the emerging Web3. Despite its decentralized nature, IPFS nodes, as well as IPFS content providers, have converged to centralization in large public clouds. Centralization introduces BGP routing-based attacks, such as passive interception and BGP hijacking, as potential threats. Although this attack vector has been investigated for many other Web3 protocols, such as Bitcoin and Ethereum, to the best of our knowledge, it has not been analyzed for the IPFS network. In our work, we bridge this gap and demonstrate that BGP routing attacks can be effectively leveraged to censor content in IPFS. For the analysis, we collected 3,000 content blocks called CIDs and conducted a simulation of BGP hijacking and passive interception against them. We find that a single malicious AS can censor 75% of the IPFS content for more than 57% of all requester nodes. Furthermore, we show that even with a small set of only 62 hijacked prefixes, 70% of the full attack effectiveness can already be reached. We further propose and validate countermeasures based on global collaborative content replication among all nodes in the IPFS network, together with additional robust backup content provider nodes that are well-hardened against BGP hijacking. We hope this work raises awareness about the threat BGP routing-based attacks pose to IPFS and triggers further efforts to harden the live IPFS network against them.
Recent years have seen many industrial implementations and much scholastic research, i.e., prototypes and theoretical frameworks, in Decentralized Identity Management Systems (DIDMS). It is safe to say that Attestation-Based Attribute-Based Decentralized IDM (ABABDIDM) has not received anywhere near the same level of attention in the literature as general Attribute-Based DIDMs (ABDIDM), i.e, decentralized Attribute-Based Access Control (ABAC). The use of decentralization, i.e., DIDM, is to improve upon the security and privacy-related issues of centralized Identity Management Systems (IDM) and Attribute-Based IDMs (ABIDM). And blockchain is the framework used for decentralization in all these schemes. Many DIDMs - even ABDIDMs - have been defined on popular blockchains such as Hyperledger, Ethereum, and Bitcoin. However, despite the characteristics of Ripple that makes it appealing for an ABIDM, there is a lack of research to develop an Identity Management System (IDMS) on Ripple in literature. We have attempted to conceptualize an ABABDIDM on Ripple.
In this paper, we design, implement, and empirically evaluate a tamper-evident, blockchain-secured solar energy logging system for resource-constrained edge Internet of Things (IoT) devices. Using a Merkle tree batching approach in conjunction with threshold-triggered blockchain anchoring, the system combines high-frequency local logging with energy-efficient, cryptographically verifiable submissions to the Ethereum Sepolia testnet, a public Proof-of-Stake (PoS) blockchain. The logger captured and hashed cryptographic chains on a minute-by-minute basis during a continuous 135 h deployment on a Raspberry Pi equipped with an INA219 sensor. Thanks to effective retrial and daily rollover mechanisms, it committed 130 verified Merkle batches to the blockchain without any data loss or unverifiable records, even during internet outages. The system offers robust end-to-end auditability and tamper resistance with low operational and carbon overhead, which was tested with comparative benchmarking against other blockchain logging models and conventional local and cloud-based loggers. The findings illustrate the technical and sustainability feasibility of digital audit trails based on blockchain technology for distributed solar energy systems. These audit trails facilitate scalable environmental, social, and governance (ESG) reporting, automated renewable energy certification, and transparent carbon accounting.
Rakan B Aldosari, Farah Kalmey, Abdullah T Alanazi, Ashraf A’aqoulah
Background: Blockchain is a cutting-edge innovation being applied to offer innovative solutions in various fields, including healthcare. The healthcare industry uses blockchain networks to store and distribute patient data across hospitals, physicians, diagnostic labs, and pharmaceutical firms. Blockchain applications are essential in the healthcare industry for identifying crucial fallacies that might be fatal. The effectiveness, security, and transparency in exchanging medical data may thus be improved in the healthcare industry. This technology may also aid medical institutions in procuring information and analysing patient data. Aim: To examine the published papers that discussed the ability of utilization and probable challenges of blockchain technology in KSA's healthcare supply chain management. Methods: Until February 10, 2023, the prime databases: Pub Med, Science Direct, Scopus, Google Scholar, Web of science, Embase and The Cochrane Library were searched. Published studies (except case reports), commentaries, editorials, reviews, and meta-analysis reporting on the use of blockchain technology in healthcare supply chain management were included and a Preferred Reporting Items of systematic Reviews and Meta-analyses (PRISMA) flow diagram was used to present the process. To assess risk of bias and the quality of the included studies, the Joanna Briggs Institute's (JBI) critical evaluation tools were implemented. Results: A total of 22 studies were included and most of them used blockchain in technology for ensuring transparency, security, and storage of electronic health or medical records. Patients benefited from seamless electronic health records provided by a multi-level blockchain eHealth system. It was observed that blockchain technology effectively addresses clinical trial misconduct, and its potential in this area is to boost data productivity for the healthcare sector. The distinctive data storage pattern of blockchain offers a high-security standard that potentially reduces concerns about data tampering. It provides flexibility, accountability, connection, and data access authentication. Blockchain helps the healthcare sector avoid certain risks and offers decentralized data protection. In our study, we observed that the most preferred network for integrating the healthcare authority, manufacturer, wholesaler, retailer, and service was Ethereum (ETH). Conclusion: Healthcare policymakers should implement blockchain in Healthcare Supply Chain Management. Moreover, they need to be aware that the primary issues with blockchain technology in the healthcare industry are the lack of practical applications, the high rate of failed initiatives, and the requirement for collaboration between diverse stakeholders. However, there is a lack of studies on how to evaluate the acceptability and assist healthcare organisations in using blockchain.
We investigate the contagion effects of rapid memecoin growth, a phenomenon often characterised by irrational exuberance and illicit behaviour. Using an EGARCH methodology, the results indicate that while memecoin growth generates revenue for host platforms like Ethereum and Solana, it broadly increases market-wide risk and is detrimental to established cryptocurrencies, such as Bitcoin. Furthermore, we find that PolitiFi memecoins are uniquely susceptible, characterised by positive responses to broad memecoin growth, exhibiting statistical properties deeming them attractive due to the cloaking provided by wider memecoin market growth, without evidence for tangible purposes. • We investigate memecoin contagion effects on the cryptocurrency market using EGARCH and on-chain data. • Memecoin growth adds systemic risk towards major cryptocurrencies such as Bitcoin. • We find strong evidence of sentiment contagion from launchpads to the entire memecoin sub-class. • PolitiFi memecoins are highly susceptible to contagion, suggesting use for opaque financing. • We demonstrate that the memecoin sector is a source of systemic risk from irrationality and illicit activity.
Current Ethereum fraud detection methods rely on context-independent, numerical transaction sequences, failing to capture semantic of account transactions. Furthermore, the pervasive homogeneity in Ethereum transaction records renders it challenging to learn discriminative account embeddings. Moreover, current self-supervised graph learning methods primarily learn node representations through graph reconstruction, resulting in suboptimal performance for node-level tasks like fraud account detection, while these methods also encounter scalability challenges. To tackle these challenges, we propose LMAE4Eth, a multi-view learning framework that fuses transaction semantics, masked graph embedding, and expert knowledge. We first propose a transaction-token contrastive language model (TxCLM) that transforms context-independent numerical transaction records into logically cohesive linguistic representations. To clearly characterize the semantic differences between accounts, we also use a token-aware contrastive learning pre-training objective together with the masked transaction model pre-training objective, learns high-expressive account representations. We then propose a masked account graph autoencoder (MAGAE) using generative self-supervised learning, which achieves superior node-level account detection by focusing on reconstructing account node features. To enable MAGAE to scale for large-scale training, we propose to integrate layer-neighbor sampling into the graph, which reduces the number of sampled vertices by several times without compromising training quality. Finally, using a cross-attention fusion network, we unify the embeddings of TxCLM and MAGAE to leverage the benefits of both. We evaluate our method against 21 baseline approaches on three datasets. Experimental results show that our method outperforms the best baseline by over 10% in F1-score on two of the datasets.
Yifan Jia, Ye Tian, Liguo Zhang, Yanbin Wang · 6 authors
Ethereum's rapid ecosystem expansion and transaction anonymity have triggered a surge in malicious activity. Detection mechanisms currently bifurcate into three technical strands: expert-defined features, graph embeddings, and sequential transaction patterns, collectively spanning the complete feature sets of Ethereum's native data layer. Yet the absence of cross-paradigm integration mechanisms forces practitioners to choose between sacrificing sequential context awareness, structured fund-flow patterns, or human-curated feature insights in their solutions. To bridge this gap, we propose KGBERT4Eth, a feature-complete pre-training encoder that synergistically combines two key components: (1) a Transaction Semantic Extractor, where we train an enhanced Transaction Language Model (TLM) to learn contextual semantic representations from conceptualized transaction records, and (2) a Transaction Knowledge Graph (TKG) that incorporates expert-curated domain knowledge into graph node embeddings to capture fund flow patterns and human-curated feature insights. We jointly optimize pre-training objectives for both components to fuse these complementary features, generating feature-complete embeddings. To emphasize rare anomalous transactions, we design a biased masking prediction task for TLM to focus on statistical outliers, while the Transaction TKG employs link prediction to learn latent transaction relationships and aggregate knowledge. Furthermore, we propose a mask-invariant attention coordination module to ensure stable dynamic information exchange between TLM and TKG during pre-training. KGBERT4Eth significantly outperforms state-of-the-art baselines in both phishing account detection and de-anonymization tasks, achieving absolute F1-score improvements of 8-16% on three phishing detection benchmarks and 6-26% on four de-anonymization datasets.
Md. Rafid Haque, Sakibul Islam Munna, Sabbir Ahmed, Md. Tariqul Islam · 6 authors
Centralized version control systems (VCS) are vital for software development but pose risks of data loss and ownership disputes. While blockchain offers a decentralized alternative, existing solutions are often hindered by high latency, compromising the real-time collaboration essential for modern workflows. This study introduces a novel hybrid architecture combining the security of the Ethereum blockchain and the InterPlanetary File System (IPFS) with two key contributions: 1) Shamir's Secret Sharing (SSS) to create a trust-minimized model for key distribution, and 2) an authoritative-first, optimistic-fallback retrieval protocol utilizing a temporary middleware to decouple the user experience from blockchain confirmation delays. We implemented a full prototype and conducted a comprehensive performance evaluation on the public Sepolia testnet. Our results demonstrate that this architecture not only provides a secure, auditable, and resilient platform for source code hosting but also achieves highly competitive user-perceived performance. Our user-perceived push time reduces submission latency by up to 49% compared to a standard git push for common repository sizes, proving that a well-designed decentralized VCS can balance the core tenets of security and decentralization with the practical need for speed and efficiency.
Ethereum smart contracts hold tens of billions of USD in DeFi and NFTs, yet comprehensive security analysis remains difficult due to unverified code, proxy-based architectures, and the reliance on manual inspection of complex execution traces. Existing approaches fall into two main categories: anomaly transaction detection, which flags suspicious transactions but offers limited insight into specific attack strategies hidden in execution traces inside transactions, and code vulnerability detection, which cannot analyze unverified contracts and struggles to show how identified flaws are exploited in real incidents. As a result, analysts must still manually align transaction traces with contract code to reconstruct attack scenarios and conduct forensics. To address this gap, TraceLLM is proposed as a framework that leverages LLMs to integrate execution trace-level detection with decompiled contract code. We introduce a new anomaly execution path identification algorithm and an LLM-refined decompile tool to identify vulnerable functions and provide explicit attack paths to LLM. TraceLLM establishes the first benchmark for joint trace and contract code-driven security analysis. For comparison, proxy baselines are created by jointly transmitting the results of three representative code analysis along with raw traces to LLM. TraceLLM identifies attacker and victim addresses with 85.19\% precision and produces automated reports with 70.37\% factual precision across 27 cases with ground truth expert reports, achieving 25.93\% higher accuracy than the best baseline. Moreover, across 148 real-world Ethereum incidents, TraceLLM automatically generates reports with 66.22\% expert-verified accuracy, demonstrating strong generalizability.
This paper investigates the dependence structure between returns and trading volumes for five major cryptocurrencies: Bitcoin, Cardano, Ethereum, Litecoin, and Ripple. Using a copula-based framework, we focus on a mixture of the Joe copula and its 90-degree rotation to capture asymmetric relationships, especially in the tails of the distribution. Our findings reveal significant upper and lower–upper tail dependencies, suggesting that extreme trading volumes are associated with both positive and negative return extremes. The results confirm a nonlinear and asymmetric volume–return relationship, which traditional linear models fail to capture.
The rapid growth of Ethereum has spurred widespread adoption of smart contracts, enabling substantial financial transactions. Once deployed on the blockchain, smart contracts are immutable, rendering them unmodifiable even if vulnerabilities are present. In recent years, numerous attacks exploiting these vulnerabilities have caused significant financial losses. Although prior research has improved vulnerability detection in source code or bytecode before deployment, identifying attacks that exploit vulnerabilities during the execution phase after deployment remains a significant challenge. These challenges arise from the limited adaptability of predefined detection rules and an overreliance on opcode sequence names, which often neglects a comprehensive analysis of opcode sequence properties. In this study, we propose an advanced multidimensional feature fusion technique designed to detect attacks during the execution phase of smart contracts. By leveraging deep learning, our approach enhances detection accuracy through a comprehensive analysis of attack behaviors across four dimensions: operation objects, action behaviors, functional categories, and gas consumption. Extensive experiments demonstrate that our method achieves a detection accuracy of 97.21% and a weighted F1-score of 97.21%, confirming its effectiveness in identifying attacks.
In recent years, surveys on vulnerability detection tools for Solidity-based smart contracts have shown that many of them display poor capabilities. One of the causes for such deficiencies is the absence of quality benchmarking datasets, where bugs typically found in smart contracts are present in quantity and accurately labeled. VulLab’s main aim is to help tackle this issue as a framework that incorporates both, state-of-the-art vulnerability insertion and vulnerability detection tools. Such capabilities empower users to seamlessly generate benchmark capable datasets from collected contracts and employ them to validate novel analysis tool and obtain an accurate comparison with current state-of-the-art solutions. The framework was able to, from 50 smart contracts collected from the Ethereum mainnet, generate an annotated dataset more than 300 entries which included 20 unique vulnerabilities, and use them to compare 14 analysis tools in approximately 24 hours. VulLab is open-source and is available at https://github.com/lsRyan/vullab.
A segurança de contratos inteligentes continua sendo um desafio na blockchain Ethereum. Este artigo investiga a evolução de ferramentas de análise de segurança por meio de dois experimentos com a estrutura SmartBugs. O primeiro analisa 215 contratos do Etherscan verificados recentemente, focando nas vulnerabilidades detectadas. O segundo replica um estudo de 2020, usando o mesmo conjunto de contratos com vulnerabilidades, mas com ferramentas atualizadas. Resultados indicam defasagem da taxonomia DASP Top 10 e uma queda na precisão de detecção (de 41,7% para 24,3%), levantando dúvidas sobre o real progresso das ferramentas.
In today's rapidly evolving landscape of smart city applications, particularly in sensitive areas like the healthcare sector, safeguarding the security, integrity, and privacy of data has become a significant and challenging concern. Specifically in the healthcare sector, the sharing and access of patient records across various stages of care by doctors, nurses, pharmacies, and diagnostic centers introduce new complexities and potential vulnerabilities. However, these challenges intensify more in the case of distributed healthcare networks where data is fragmented across institutions. This work addresses issues such as data vulnerability and misuse in distributed healthcare environments by proposing a Blockchain-enabled Distributed Healthcare System (BeDHS). The model is designed to facilitate secure, transparent, and privacy-preserving collaboration among healthcare entities. It adopts a hybrid approach, integrating a quantum key-based image encryption technique to enhance the security of health records. The encrypted images are securely stored in the InterPlanetary File System (IPFS) to ensure data integrity and availability. Additionally, a Federated Learning (FL) framework is employed to enable collaborative training of AI models across institutions without exposing sensitive patient data. The proposed BeDHS model is implemented using Solidity-based smart contracts on the Ethereum blockchain, ensuring decentralized and tamper-resistant operations. Simulation results demonstrate that the proposed model outperforms existing healthcare data management systems in terms of efficiency and security. • A blockchain-enabled distributed healthcare system is proposed, where the number of healthcare institutions of a smart city are integrated to form a collaborative and transparent model for sharing health records while maintaining security, privacy, and immutability. • A Quantum-Chaos-Encryption cryptographic technique integrated with blockchain for protecting digital documents and medical images from unauthorized access. • To build a privacy-preserved distributed-collaborative healthcare system, a federated learning approach is incorporated that trains the AI models directly at the data source of multiple healthcare institutions while eliminating the need to transfer between the institutions.
Muhammad Ali Nawaz, Wajid Alim, Sammar Abbas, Shahid Manzoor Shah · 5 authors
The study investigates the co-movement relationships between cryptocurrencies and South Asian stock markets, focusing on five leading cryptocurrencies: Bitcoin, Ethereum, Tether, Binance Coin, and Ripple, and five South Asian stock indices: BSE, PSX 100, DSE 30, NEPSE, and Sri Lanka's All Share Index, and also used five major global indices for the accuracy of analysis. The study aims to understand their integration and causal dynamics. The analysis uses 357 weekly observations of historical prices from November 6, 2017, to September 2, 2024, applying econometric tools such as the Augmented Dickey-Fuller and Phillips-Perron tests, Johansen's Cointegration Test, Vector Auto-Regression, Vector Error Correction Model, and Granger causality to examine statistical properties, integration, and causality among the variables. Results show significant cointegration and causality between cryptocurrencies and South Asian stock indices, with cryptocurrency prices exhibiting higher volatility and faster adjustments than stock indices. These findings provide actionable insights for investors, policy-makers, and researchers regarding regulation and cross-market investment strategies. This study uniquely explores the interplay between emerging digital assets and traditional finance in a South Asian context, offering novel evidence on volatility dynamics and causal relationships that inform coupled regulatory frameworks and cross-market investment planning.
Traditional portfolio optimization models, rooted in the mean–variance framework of Markowitz, rely heavily on variance as a risk measure. Although theoretically elegant, this approach becomes fragile in volatile and structurally unstable markets such as cryptocurrencies, where return distributions deviate significantly from normality, cor-relations are unstable, and concentration risk emerges. These limitations have motivated the search for alternative frameworks capable of capturing uncertainty in a more flexible and distribution-free manner. Entropy, originally introduced by Shannon as a measure of information, has gradually been recognized in the financial literature as a suitable proxy for diversification and systemic uncertainty. To address the shortcomings of variance-based models, this paper introduces the Weighted Shannon Entropy (WSE) model as a diversification-oriented alternative. By extending the classical Shannon entropy with asset-specific informational weights, the WSE framework provides additional flexibility for modeling heterogeneous asset char-acteristics, such as liquidity, informational value, or perceived reliability. Using the principle of maximum entropy and the method of Lagrange multipliers, we derive ex-ponential-form solutions for portfolio weights that naturally discourage concentration, ensure balanced allocations, and remain analytically tractable. The methodology is validated empirically on a portfolio of four leading cryptocurren-cies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—using market data from January to March 2025. The results demonstrate that the entropy-based optimization framework produces well-diversified portfolios, robust to volatility and structural instability, and provides a distribution-free alternative to the classical mean–variance model. Beyond its empirical performance, the WSE formulation highlights the conceptual advantage of entropy in integrating return, risk, and diversification into a single unified framework. The paper contributes both theoretically and practically: it strengthens the mathematical foundation of entropy-based portfolio selection, extends its applicability to digital asset markets, and illustrates how weighting schemes can enrich the classical Shannon measure. Future research may extend this approach to multi-period optimization, gen-eralized entropies such as Tsallis and Kaniadakis, or integration with machine learning models for dynamic portfolio management.
Ethereum enables the creation and execution of decentralized applications through smart contracts, that are compiled to Ethereum Virtual Machine (EVM) bytecode. Once deployed in the blockchain, the bytecode is immutable; hence, ensuring that smart contracts are bug-free before their deployment is of utmost importance. A crucial preliminary step for any effective static analysis of EVM bytecode is the extraction of the control-flow graph (CFG): this presents significant challenges due to potentially statically unknown jump destinations. In this paper we present a novel approach, based on Abstract Interpretation, aiming to build a sound CFG from EVM bytecode smart contracts. Our analysis, which is implemented in our static analyzer EVMLiSA, is based on a parametric abstract domain that approximates concrete execution stacks at each program point as an l -sized set of abstract stacks of maximal height h ; the results of the analysis are then used to resolve the jump destinations at jump nodes. Furthermore, EVMLiSA includes a checker for reentrancy detection, working on the constructed CFG. Our experiments show that, by fine-tuning the analysis parameters, EVMLiSA is able to build sound CFGs for all real-world smart contracts in the considered benchmark suite. Moreover, EVMLiSA successfully detects all reentrancy vulnerabilities in EVM bytecode smart contracts, while producing a small number of false positives.
Smart contracts are self-executing programs that run on blockchain platforms, most notably Ethereum.They automate transactions and enforce agreements without intermediaries, forming the foundation of decentralized finance (DeFi), non-fungible tokens (NFTs), and decentralized applications (dApps).Despite their growing importance, smart contracts remain prone to security vulnerabilities.Exploited bugs can lead to irreversible financial losses, service disruptions, and systemic failures.Although machine learningbased tools have emerged to aid vulnerability detection, two critical challenges remain: (1) limited fault localization at the function level, and (2) a lack of interpretable, human-readable explanations that enable developers to understand and fix issues effectively.This thesis addresses both challenges by proposing a unified framework that combines graph-based neural network modeling with explainable language model techniques.Specifically, the contributions consist of: (1) a function-level vulnerability detection system using Sub-Graph Neural Networks (Sub-GNNs), and (2) an explanation generation mechanism based on synthetic data and Chain-of-Thought (CoT) prompting using large language models (LLMs).These two components aim to improve both the technical granularity and practical usability of smart contract security analysis.The first part of the thesis introduces a novel function-level detection method that decomposes smart contracts into subgraphs centered around individual functions.While prior approaches using Graph Neural Networks (GNNs) operate at the contract level, they fail to pinpoint specific sources of vulnerabilities, limiting their value for debugging and remediation.To overcome this, we construct function-level subgraphs that incorporate controlflow and data-flow dependencies, preserving the semantic and structural context of each function.We then apply a Sub-GNN model to perform vulnerability classification at this finer granularity.Empirical evaluation on a curated synthetic dataset demonstrates that the proposed method achieves high precision in localizing faulty functions.Although it trades off a small margin of global classification accuracy compared to full-graph models, the localized predictions are significantly more actionable for developers.A benchmark comparison quantifies this trade-off and validates the effectiveness of subgraph-based analysis in practical settings.To facilitate this line of work, we develop a synthetic dataset of smart contracts with function-level vulnerability labels.The dataset includes diverse vulnerability types such as reentrancy, integer overflows, access control flaws, and unhandled exceptions.Each function is annotated with corresponding vulnerability types and contains metadata for constructing control and data flow graphs.This dataset fills a gap in the current landscape, which largely lacks fine-grained, labeled corpora for training and evaluating function-level detectors.The second component of the thesis tackles the issue of explanation.While detecting a vulnerability is important, understanding why it occurs and how to resolve it is crucial for real-world usability.Most existing detection tools output low-level indicators such as line numbers or vulnerability labels without offering semantic explanations.To address this gap, we propose an explanation generation system that produces structured, human-readable justifications for detected vulnerabilities.We construct another synthetic dataset where each entry consists of a vulnerable function, its formal label, and a professionally formatted explanation describing the issue, its cause, and suggested remediation steps.These explanations are derived from real-world audit patterns and follow a consistent template.Together, these two components form a comprehensive framework for smart contract vulnerability analysis.The Sub-GNN-based detector provides precise localization of faulty functions, while the CoT-guided explanation generator delivers semantic insight into the causes and consequences of the vulnerabilities.This dual capability bridges the gap between vulnerability detection and developer comprehension.The thesis concludes with a discussion of future directions.On the detection side, extending the Sub-GNN architecture to support inter-function and inter-contract reasoning could enable the modeling of call chains and complex compositional vulnerabilities.On the explanation side, integrating user feedback to iteratively refine generated explanations could support interactive auditing tools.Furthermore, we propose exploring multimodal models that combine graph-based embeddings with textual features to enhance both detection and explanation tasks.