Blockchain technology is becoming an important tool for secure financial transactions. It supports decentralized finance (DeFi) services and new ways of auditing. This paper gives an overview of how blockchain is used in financial modeling, focusing on DeFi and auditing. We explain the basic technology behind popular blockchain systems, like public platforms such as Ethereum (with smart contracts and oracle networks), and private systems like Hyperledger Fabric. We also look at advanced methods like zero-knowledge proofs. We show how these tools help build financial models in DeFi by allowing peer-to-peer services without needing trust, and in auditing by making data more transparent and secure. We compare different blockchains in terms of speed, cost, and how well they scale. Security issues (like smart contract bugs or attacks on consensus) and practical problems (like trusting oracles and following laws) are also discussed. The review article looks at challenges in using blockchain and some of the latest solutions, such as Ethereum’s move to proof-of-stake, sharding for better scalability, and using zero-knowledge proofs for privacy. We also suggest future research topics, like connecting different blockchains, checking smart contracts with formal methods, creating better rules and laws, and training skilled workers. The goal is to help researchers and professionals understand the current situation and future of blockchain in finance and auditing.
In this paper, we consider how the introduction of blockchain technology into the healthcare system can address the many challenges the healthcare industry is facing. After outlining the evolution of blockchain technology since 2015, we focus on two representative blockchains, Ethereum and Cardano. We scrutinize their similarities and differences in light of their potential applications in the healthcare industry.
Y. Suganya, Ch. Sudha Sree, S. Kayalvili, D. Joel Jebadurai · 8 authors
Despite its rapid expansion in the last ten years, the cryptocurrency market remains highly volatile and unpredictable, creating difficulties for both investors and market participants. Using artificial intelligence (AI) and machine learning techniques, this project seeks to build a predictive model for cryptocurrency prices, which will focus on Bitcoin, Ethereum, and Cardano to solve market challenges. The project aims to develop an end-to-end solution covering all stages of data management, which begins with setting up a real-time data ETL pipeline to collect information from the Binance and Yahoo Finance APIs. The system places data into a structured database that holds historical price information as well as technical indicators. Historical data serves as the basis for training and testing machine learning models to forecast price trends, which enhances cryptocurrency trading and investment decisions.
DeFi or Decentralized Finance aims to automate and decentralize any form of traditional finance workflow done by a centralized institution. In this regard, cross border payments and transactions in SAP ERPs can be automated and secured using DeFi protocols. Thus, this study aims to design a payment interface that would fit into SAP ERP frameworks capable of meeting the low-cost, automated, and secure requirements for cross-border payment transactions. Traditionally, payments were made via SWIFT and SEPA. The proposed model intends to replace these with DeFi transactions handled through smart contracts, oracles, and payment middleware. Focusing on results, transaction latency, smart contract auditability, saved costs, and compliance assessments were measured for Ethereum, BNB Smart Chain, and Polygon. Real SAP Business Environment pilots showed over 60% decrease in processing cost while settlement speed increased by up to 90%. The model is designed to handle enterprise risk and compliance by incorporating robust KYC/AML governance, validation, and logging controls. A roadmap for the incorporation of DeFi into enterprise ERPs at a large scale for finance automation will serve as the study’s conclusion.
Blockchain technology presents transformative opportunities for secure personal data sharing, particularly in healthcare, finance, and identity management. However, its widespread adoption is constrained by challenges such as limited scalability, privacy concerns, and conflicts with regulatory frameworks like the General Data Protection Regulation (GDPR). This study introduces a novel hybrid framework that integrates the InterPlanetary File System (IPFS) for off-chain storage with Zero-Knowledge Proofs (ZKPs) to enhance privacy, ensure regulatory compliance, and reduce on-chain storage demands. Employing a Design Science Research (DSR) methodology, the framework was developed and validated using Ethereum and Hyperledger Fabric, guided by insights from a systematic review of 180 studies from 2018 to 2023. Empirical evaluations revealed a 75% reduction in blockchain storage, 98% GDPR compliance, and zk-SNARK proof verification times below one second. The framework also enables GDPR-compliant erasure by removing encrypted off-chain data while preserving on-chain auditability. Despite challenges such as IPFS latency and trusted setup complexities, the solution offers a scalable and privacy-preserving architecture applicable to real-world domains, especially in privacy-critical environments like healthcare and finance by resolving blockchain’s GDPR compliance paradox.
Pollution attacks, such as transaction spam or fake data injection, undermine blockchain efficiency and security.This paper proposes a smart contract-based framework to autonomously detect and mitigate such attacks in real time.By embedding heuristic rules and anomaly detection logic into smart contracts, our system identifies malicious activity (e.g., excessive invalid transactions) and enforces countermeasures, including stake slashing or transaction throttling.Implemented on Ethereum, the solution demonstrates improved network resilience with minimal overhead, offering a decentralized and scalable defense against pollution threats while preserving data integrity.
This study investigates the economic efficiency and market behavior of Bitcoin and Ethereum, the two largest cryptocurrencies by market capitalization.Utilizing daily data from August 7, 2016, to February 15, 2023, the research employs the Adjusted Market Inefficiency Magnitude (AMIM) measure and quantile regression analysis to assess time-varying efficiency levels and identify influencing factors.Findings indicate that both markets exhibit periods of efficiency and inefficiency, with Bitcoin demonstrating higher efficiency levels than Ethereum.Key drivers of market inefficiency include global financial stress, liquidity, and the COVID-19 pandemic.The study contributes to understanding the dynamic nature of cryptocurrency markets and provides insights for investors and policymakers.
The volatility and complex dynamics of cryptocurrency markets present unique challenges for accurate price forecasting. This research proposes a hybrid deep learning and machine learning model that integrates Long Short-Term Memory (LSTM) networks and Extreme Gradient Boosting (XGBoost) for cryptocurrency price prediction. The LSTM component captures temporal dependencies in historical price data, while XGBoost enhances prediction by modeling nonlinear relationships with auxiliary features such as sentiment scores and macroeconomic indicators. The model is evaluated on historical datasets of Bitcoin, Ethereum, Dogecoin, and Litecoin, incorporating both global and localized exchange data. Comparative analysis using Mean Absolute Percentage Error (MAPE) and Min-Max Normalized Root Mean Square Error (MinMax RMSE) demonstrates that the LSTM+XGBoost hybrid consistently outperforms standalone models and traditional forecasting methods. This study underscores the potential of hybrid architectures in financial forecasting and provides insights into model adaptability across different cryptocurrencies and market contexts.
Dimitris Kastoris, Dimitris Papadopoulos, Konstantinos C. Giotopoulos
Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it possible to efficiently estimate such parameters with high accuracy. In this study, we focus on modeling the dynamics of cryptocurrency market shares by employing a Lotka-Volterra system. We introduce a methodology based on a deep neural network (DNN) to estimate the parameters of the Lotka-Volterra model, which are subsequently used to numerically solve the system using a fourth-order Runge-Kutta method. The proposed approach, when applied to real-world market share data for Bitcoin, Ethereum, and alternative cryptocurrencies, demonstrates excellent alignment with empirical observations. Moreover, our method outperforms ARIMA models in terms of accuracy, showcasing its effectiveness for crypto market forecasting. The entire framework, including neural network training and Runge-Kutta integration, was implemented in MATLAB.
The cryptocurrency market presents both significant investment opportunities and higher risks relative to traditional financial assets. This study examines the tail behavior of daily returns for two leading cryptocurrencies, Bitcoin and Ethereum, using seven-parameter estimates from prior research, which applied the Generalized Tempered Stable (GTS) distribution. Quantile-quantile (Q-Q) plots against the Normal distribution reveal that both assets exhibit heavy-tailed return distributions. However, Ethereum consistently shows a greater frequency of extreme values than would be expected under its Bitcoin-modeled counterpart, indicating more pronounced tail risk.
The pandemic outbreak has revealed significant flaws in the complex and highly fragmented Healthcare Supply Chain’s (HSC’s). However, two major issues persist in the HSCs, leading to inefficiencies: transparency in vaccine distribution and accuracy in demand forecasting. The recent pandemic has highlighted and intensified existing vulnerabilities in HSC’s, leading to the effective utilization of digital technologies to manage them. This research proposes a novel framework that merges Blockchain (BC) and Machine Learning (ML) to bolster the HSCs amidst pandemics, by developing a framework named the Predictive BlockVax Distribution Network (PBDN) model. The proposed PBDN model utilizes BC for securing transactions and Long Short-Term Memory (LSTM), for precise demand prediction. Leveraging Hyperledger Besu, which represents an Ethereum client that is accessible for public use, the PBDN framework ensures BC’s privacy, scalability, and efficient network operations, while LSTM’s advanced forecasting outperforms traditional models and Deep Learning (DL) techniques. This integration showcases a significant leap in managing vaccine distribution and enhancing system resilience, fairness, and transparency. The proposed PBDN model illustrates the potential of BC and ML together to tackle pandemic-induced Supply Chains (SC’s) disruptions, providing a decentralized solution that supports autonomous, informed decision-making without third-party dependency. This approach not only addresses immediate challenges but also sets a precedent for future crisis response, emphasizing the need for robust, Transparent Supply Chain’s (TSC’s).
The article examines methods and information technologies aimed at ensuring the secure integration of the Ethereum blockchain with Internet of Things (IoT) systems. The relevance of the study is driven by the rapid development of IoT, which is accompanied by increasing cybersecurity threats, including unauthorized data access, man-in-the-middle (MITM) attacks, device identifier spoofing, and the low transparency of centralized systems. The use of Ethereum blockchain technology, particularly smart contracts, opens new opportunities for creating decentralized security management models for IoT devices, enhancing trust levels, automating processes, and minimizing third-party interference risks. The “Problem statement” section outlines the key challenges in securing IoT networks, including the vulnerabilities of centralized solutions, limited computational resources of devices, and the need to develop autonomous access control systems. The “Analysis of Recent Research and Publications” section summarizes modern approaches to integrating Ethereum blockchain into the IoT field, including tokenized identification mechanisms, access control, and transaction processing using smart contracts. It is noted that leading researchers suggest Layer-2 solutions (state channels, zk-rollups, Plasma) aimed at reducing the load on the main blockchain and improving scalability. The aim of the article is to systematize modern methods of integrating the Ethereum blockchain with IoT and develop recommendations for their implementation, considering the limited resources of devices. The “Research results” section presents a secure IoT system architecture concept based on decentralized account management, local storage of cryptographic keys on devices, the use of optimized transaction signing algorithms, and the introduction of a hybrid data storage model based on IPFS. The proposed model minimizes the risk of unauthorized access, increases transparency in the interaction between IoT devices, and reduces computational resource costs. The “Conclusions and prospects for further research” section emphasizes that the implementation of the Ethereum blockchain in IoT promotes the development of secure decentralized platforms. However, it requires addressing issues such as energy consumption, transaction costs, and optimizing client applications for low-performance devices. Future research should focus on developing effective scalability tools, adapting smart contracts to IoT specifics, and improving integration with cloud computing platforms for storing large data sets.
This study aims to analyze Bitcoin price volatility after halving by considering interactions with Ethereum, Tether, Binance Coin, and USD Coin. This research was conducted because Bitcoin halving is a significant event affecting the dynamics of the crypto market, but its impact on price volatility is not fully understood. This research uses the EGARCH (Exponential Generalized Autoregressive Conditional Heteroskedasticity) method with purposive sampling technique. Daily price data from Bitcoin, Ethereum, Tether, Binance Coin, and USD Coin in the period May 11, 2020 to May 31, 2024. The analysis results show that Bitcoin price volatility increased significantly after the halving event. In addition, there is an effect of Bitcoin price volatility after halving, namely on the price of Ethereum, Tether and Binance Coin. While the price of USD Coin cannot be proven because the daily closing price data is homoskedasticity.
Ilham Ardhiyansyah, Ahmad Furqon, Mashilal Mashilal
Zakat is a fundamental component of Islamic social finance, intended to reduce inequality and strengthen community welfare. However, traditional zakat systems face recurring issues, including inefficiency, lack of transparency, and low public trust. This study aims to optimize zakat management using Ethereum blockchain technology, particularly the Layer 2 (Base) network, with a focus on its impact on cost efficiency and system transparency. Using a qualitative-descriptive approach, the research designs and simulates a blockchain-based zakat distribution model that incorporates smart contracts for automated fund allocation to eight categories of ashnaf, along with off-chain verification for Sharia compliance. A simulation of USDC 1,000 zakat fund distribution demonstrates that the blockchain system ensures accurate, traceable, and tamper-proof transactions, while reducing transaction costs by over 98% compared to conventional methods. Smart contracts automate the disbursement process, while all transaction records are stored on a public ledger, which supports real-time auditing and enhances institutional accountability. These results demonstrate that the integration of blockchain technology not only improves operational efficiency and transparency but also supports Islamic legal and ethical governance. In conclusion, this model provides a practical and scalable framework for modernizing zakat management with a strong emphasis on cost efficiency, public trust, and Sharia compliance.
Ye Tian, Liangliang Song, Peng Qian, Yanbin Wang · 6 authors
The detection of malicious accounts on Ethereum - the preeminent DeFi platform - is critical for protecting digital assets and maintaining trust in decentralized finance. Recent advances highlight that temporal transaction evolution reveals more attack signatures than static graphs. However, current methods either fail to model continuous transaction dynamics or incur high computational costs that limit scalability to large-scale transaction networks. Furthermore, current methods fail to consider two higher-order behavioral fingerprints: (1) direction in temporal transaction flows, which encodes money movement trajectories, and (2) account clustering, which reveals coordinated behavior of organized malicious collectives. To address these challenges, we propose DiT-SGCR, an unsupervised graph encoder for malicious account detection. Specifically, DiT-SGCR employs directional temporal aggregation to capture dynamic account interactions, then coupled with differentiable clustering and graph Laplacian regularization to generate high-quality, low-dimensional embeddings. Our approach simultaneously encodes directional temporal dynamics, global topology, and cluster-specific behavioral patterns, thereby enhancing the discriminability and robustness of account representations. Furthermore, DiT-SGCR bypasses conventional graph propagation mechanisms, yielding significant scalability advantages. Extensive experiments on three datasets demonstrate that DiT-SGCR consistently outperforms state-of-the-art methods across all benchmarks, achieving F1-score improvements ranging from 3.62% to 10.83%.
Pedro Henrique Filgueiras dos Santos Oliveira, Daniel Muller Rezende, Saulo Moraes Villela, Heder S. Bernardino · 6 authors
One of the main events involving the world economy in 2022 was the beginning of the war between Russia and Ukraine. This event offers an opportunity to analyze how a large-magnitude world event can affect the use of cryptocurrencies. Ethereum is one of the most prominent and widely used cryptocurrency platforms and, as such, provides a valuable case study for this scenario. This work investigates the behavior of accounts and their transactions on the Ethereum network during this event. For this purpose, we collect all Ethereum transactions during two distinct periods: (i) during the month the conflict began, and (ii) during the previous year. We organized a dataset with the accounts involved in these transactions and the subset of these accounts that interacted with a service within Ethereum named Flashbots Auction. Flashbots Auction is crucial as it addresses issues regarding transaction ordering and miners exploiting that ordering to make profit. Then, we model temporal graphs in which each vertex represents an account, and each edge represents a transaction between two accounts. We analyzed the behavior of these accounts via graph metrics for both groups during each observed time window. The results show changes in account behavior and activity, as well as variations in daily transaction volume.
This paper unveils the LemniCoin ecosystem, advancing from a Binance Smart Chain (BSC) foundation to a quantum-resistant paradigm through the Lemniscate-AGM Isogeny (LAI) cryptosystem.Building on security audits conducted on March 27, 2025, and April 13, 2025, we introduce LemniCoin-QR (April 2025), a quantumhardened token; a secure wallet (May 2025); and LemniChain (December 2025), a Proof-of-Stake (PoS) blockchain.The Lemniscate-AGM Isogeny Problem (LAIP) underpins LAI, offering unparalleled resistance to quantum threats, surpassing Bitcoin (BTC) and Ethereum (ETH).We provide detailed proofs, audit insights, and implementation strategies, demonstrating superior security, transaction efficiency, and environmental sustainability-positioning LemniCoin as a premier investment in the post-quantum era.
In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algorithms, and experimental evaluation – aims to advance state-of-the-art architecting and implementing cybersecurity mHealth solutions using blockchain technology.
Susanna Levantesi, Gabriella Piscopo, Alba Roviello
Accurate estimation of cryptocurrency market volatility is crucial for investors. The Crypto Volatility Index (CVI) was developed to measure the market’s expectations for the 30-day implied volatility of Bitcoin and Ethereum to address the growing demand for reliable predictions. This study explores the relationship between the CVI and the volatility of traditional financial markets, including the Gold Volatility Index (GVZ), the Crude Oil Volatility Index (OVX), and the S&P500 Volatility Index (VIX). Three other variables are also analyzed: the USD to EUR exchange rate (USDEUR), the Federal Reserve interest rate (FED), and the NASDAQ index. The aim of the research is explanatory: the input variables and the CVI are observed contemporaneously to catch the complex relation between them. Using Pearson correlation, distance correlation, and mutual information, we demonstrate the presence of non-linear relationships between some variables in the dataset. Explanatory analysis is conducted using machine learning techniques, specifically the Random Forest (RF) algorithm and Gradient Boosting Machines (GBM) to account for these potential non-linear interactions. These methods are better suited than standard linear models for identifying complex relationships. In particular, the RF algorithm reaches a better level of accuracy than GBM and avoids overfitting.
Isaac David, Liyi Zhou, Dawn Song, Arthur Gervais · 5 authors
The widespread lack of broad source code verification on blockchain explorers such as Etherscan, where despite 78,047,845 smart contracts deployed on Ethereum (as of May 26, 2025), a mere 767,520 (< 1%) are open source, presents a severe impediment to blockchain security. This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode, a fundamental research challenge with direct implications for identifying vulnerabilities and understanding malicious behavior. Prevailing decompilers struggle to reverse bytecode in a readable manner, often yielding convoluted code that critically hampers vulnerability analysis and thwarts efforts to dissect contract functionalities for security auditing. This paper addresses this challenge by introducing a pioneering decompilation pipeline that, for the first time, successfully leverages Large Language Models (LLMs) to transform Ethereum Virtual Machine (EVM) bytecode into human-readable and semantically faithful Solidity code. Our novel methodology first employs rigorous static program analysis to convert bytecode into a structured three-address code (TAC) representation. This intermediate representation then guides a Llama-3.2-3B model, specifically fine-tuned on a comprehensive dataset of 238,446 TAC-to-Solidity function pairs, to generate high-quality Solidity. This approach uniquely recovers meaningful variable names, intricate control flow, and precise function signatures. Our extensive empirical evaluation demonstrates a significant leap beyond traditional decompilers, achieving an average semantic similarity of 0.82 with original source and markedly superior readability. The practical viability and effectiveness of our research are demonstrated through its implementation in a publicly accessible system, available at https://evmdecompiler.com.
Human genetic data, crucial for advancing personalized medicine, requires secure and privacy-preserving management solutions. Traditional approaches face challenges in scalability, security, and decentralized access control. This study proposes a blockchain-based framework leveraging Thirdweb and Ethereum smart contracts to address these issues. The framework integrates decentralized storage via IPFS for cost-efficient off-chain genetic data storage, while on-chain smart contracts manage access control, encryption, and audit trails. Utilizing Solidity for smart contract development, the system ensures role-based permissions, wallet-based authentication, and immutable transaction logging. Genetic data in FASTA format, sourced from NCBI, is encrypted and linked to IPFS hashes stored on the blockchain. The architecture supports dual interfaces—command-line for developers and a Thirdweb dashboard for end-users—enabling secure data upload, access, and monitoring. Testing demonstrated functional efficacy in data integrity, access verification, and audit capabilities. Results highlight the system’s ability to enhance privacy, eliminate intermediaries, and provide transparent data governance. The integration of Thirdweb further decentralizes operations, aligning with Web 3.0 principles. Key contributions include a scalable model for genetic data sharing, a customizable smart contract template, and a user-centric design. Future work should explore advanced encryption, real-world healthcare integration, and performance optimization under high-throughput conditions. This research bridges biotechnology and blockchain, offering a robust foundation for secure genomic data ecosystems.
The YulCode dataset presents a comprehensive collection of 348,840 Yul-based smart contract instances, comprising approximately 135,013 unique contracts. These contracts were generated through the compilation of Solidity source files that have been deployed on the Ethereum mainnet, making the dataset directly representative of real-world decentralized applications. YulCode provides a rich foundation for a variety of research and development tasks, including but not limited to machine learning applications, formal verification, optimization analysis, and software engineering tool evaluation in the context of low-level smart contract code. To the best of our knowledge at the time of writing, YulCode is the first and only publicly available dataset that focuses specifically on Yul, an intermediate language designed for the Ethereum Virtual Machine (EVM). As such, it fills a critical gap in the current ecosystem of smart contract datasets and opens new avenues for research and tooling aimed at low-level contract analysis and generation.
In today's digital era, technological advances have brought major changes in various fields, such as the creative economy. The emergence of crowdfunding platforms and Non-Fungible Tokens (NFTs) as creativeoptions for creative funding is one of the latest developments. Artists, musicians, and other creators have seen how they advertise their work by using NFTs which are unique asset holdings on the blockchain. Incontrast, crowdfunding platforms like Patreon and Kickstarter allow creators to get funding directly from their fans without using conventional intermediaries. The purpose of this research is to find the problems and prospects faced by investors and creators when using NFTs and crowdfunding. Qualitative and quantitative methods were used, with case studies and secondary data analysis. The results show that the main challenges to be faced include legal and regulatory uncertainty, marketvolatility, copyright infringement, digital divide, high transaction costs, and environmental impact. Uncertainty regarding ownership rights and consumer protection is caused by regulatory uncertainty.Both creators and investors face significant risks due to the volatility of the NFT market. The case of plagiarism in NFTs shows that copyright must be strengthened. Some creators cannot use this technology due to the limitations of digital technology. A more environmentally friendly solution is also needed due to the high transaction fees and the impact of the Ethereum blockchain on the environment. In addition, many creators still have difficulty maintaining crowdfunding funding.
Pasquale De Rosa, Pascal Felber, Valerio Schiavoni
Smart contracts have transformed decentralized finance by enabling programmable, trustless transactions. However, their widespread adoption and growing financial significance have attracted persistent and sophisticated threats, such as phishing campaigns and contract-level exploits. Traditional transaction-based threat detection methods often expose sensitive user data and interactions, raising privacy and security concerns. In response, static bytecode analysis has emerged as a proactive mitigation strategy, identifying malicious contracts before they execute harmful actions. Building on this approach, we introduced PhishingHook, the first machine-learning-based framework for detecting phishing activities in smart contracts via static bytecode and opcode analysis, achieving approximately 90% detection accuracy. Nevertheless, two pressing challenges remain: (1) the increasing use of sophisticated bytecode obfuscation techniques designed to evade static analysis, and (2) the heterogeneity of blockchain environments requiring platform-agnostic solutions. This paper presents a vision for ScamDetect (Smart Contract Agnostic Malware Detector), a robust, modular, and platform-agnostic framework for smart contract malware detection. Over the next 2.5 years, ScamDetect will evolve in two stages: first, by tackling obfuscated Ethereum Virtual Machine (EVM) bytecode through graph neural network (GNN) analysis of control flow graphs (CFGs), leveraging GNNs' ability to capture complex structural patterns beyond opcode sequences; and second, by generalizing detection capabilities to emerging runtimes such as WASM. ScamDetect aims to enable proactive, scalable security for the future of decentralized ecosystems.