Non-Fungible Tokens (NFTs) represent a revolutionary class of digital assets, characterized by their uniqueness and value derived from metadata and visual traits. However, NFT markets suffer from volatility and a lack of transparent valuation systems, making it difficult for collectors and investors to estimate asset worth. This paper presents a comprehensive machine learning pipeline for predicting the market value of NFTs based on trait rarity and sale metadata. We apply rigorous preprocessing, compute rarity scores from trait distributions, and compare multiple regression models, including Random Forest, LightGBM, CatBoost, and Extra Trees. Our analysis demonstrates that tree-based models significantly outperform simpler regressors, with Extra Trees achieving the lowest RMSE of 136.91 and the highest$\mathrm{R}^{\mathrm{2}}$score of$\text{1. 0}$. Visual and statistical analyses further validate the effectiveness of our methodology in predicting NFT prices with high precision.
With the development of science and technology, the Internet of Things (IoT) had been integrated into the daily life of people. That makes the security of IoT a necessity and gains more attention. The Device authentication is an important issue in the security of IoT. In this paper, we propose a device authentication scheme based on both Physically Unclonable Function (PUF) and zero-knowledge proof. The proposed mutual authentication scheme reduces the memory load on the server and provides both data integrity and confidentiality during the authenticating process. We verify the proposed device authentication algorithm on the IoT platform Raspberry Pi using SRAM-PUF. The experimental results reveal that the proposed device authentication scheme is novel for IoT. It can resist brute force attack, replay attack, man-in-the-middle attack, machine learning attacks, and etc.
Physical Unclonable Functions (PUFs) and Hardware Security
Ensemble learning techniques continue to show greater interest in forecasting the volatility of cryptocurrency assets. In particular, XGBoost, an ensemble learning technique, has been shown in recent studies to provide the most accurate forecast of Bitcoin volatility. However, the performance of XGBoost largely depends on the tuning of its hyperparameters. In this study, we examine the effectiveness of the Bayesian optimization method for tuning the XGBoost hyperparameters for Bitcoin volatility forecasting. We chose to explore this method rather than the most commonly used manual, grid, and random hyperparameter choices due to its ability to predict the most promising areas of hyperparameter spaces through exploitation and exploration using acquisition functions, as well as its ability to minimize error with a reduced amount of time and resources required to find an optimal configuration. The obtained XGBoost configuration improves the forecast accuracy of Bitcoin volatility. Our empirical results, based on letting the data speak for itself, could be used for a comparative study on Bitcoin volatility forecasting. This would also be important for volatility trading, option pricing, and managing portfolios related to Bitcoin.
Hakam Dzakwan Diash, Vannesa Nathania, Mohammad Idhom, Trimono Trimono
The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself. This study aims to develop an ETH price prediction model using a combined architecture of Convolutional Neural Network (CNN) and also Bidirectional Long Short-Term Memory (BiLSTM). CNN is used to extract local features from historical ETH closing price data, while BiLSTM models bidirectional temporal patterns. The dataset used includes ETH daily price from January 2020 to January 2025, which are obtained from Yahoo Finance and have gone through a normalization process and transformation into sequential form. The model is trained for 100 epochs with an early stopping mechanism to prevent overfitting and evaluated using the MAPE and coefficient of determination (R²) metrics. The evaluation results show that the CNN-BiLSTM model is able to predict ETH prices with a MAPE value of 2.8546% and an R² of 0.9415, indicating high performance in capturing actual data trends. This study shows that the hybrid CNN-BiLSTM approach is effective for Ethereum price prediction.
Smart contracts, self-executing programs deployed on blockchain networks, offer significant benefits in terms of transparency, efficiency, and trustless operations. However, their widespread adoption is hindered by security vulnerabilities that can lead to financial losses and operational failures. These vulnerabilities often stem from coding errors and logical flaws, such as input validation issues, race conditions, and value propagation problems. In this work, we propose a systematic analysis of common security risks in smart contracts, based on an in-depth literature review and the examination of several case studies. The results identify the most critical vulnerabilities and suggest best practices to mitigate these risks. For instance, the study highlights the importance of rigorous input validation and the use of formal models to verify contract logic. In short, this study provides valuable guidelines for smart contract developers and contributes to strengthening the security of blockchain applications. As a perspective for future work, avenues for research on the formalization of smart contracts and the automation of security testing will be explored.
Physical Unclonable Functions (PUFs) and Hardware Security
Hope Leticia Nakayiza, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae‐Min Lee
Increased automation, connectivity, and data sharing enabled by 6G technology have heightened the vulnerability of Internet of Vehicles (IoV) networks. Addressing this challenge requires an intrusion detection system (IDS) capable of accurately identifying data falsification within IoV while adhering to real-time constraints. This paper presents a Blockchain-enhanced feature-engineered IDS to ensure precise attack detection and classification with minimal computational overhead in in-vehicle networks (IVNs). The proposed lightweight IDS utilizes a hybrid Pearson’s Correlation Coefficient (PCC) feature selection technique designed for deployment on the Telematics Control Unit (TCU). Furthermore, we propose a custom private blockchain network utilizing the proof of authority and association (PoA) consensus mechanism, deployable on the Roadside Unit (RSU), for the secure logging of vehicle Electronic Control Unit (ECU) information, detection results, and the automatic isolation of malicious ECUs via smart contracts. Experimentation analysis demonstrates that the proposed approach achieves notable performance, with a 99.9% detection accuracy and minimal computation times of 0.24s and 1.32s on the CICIoV2024 and CAN-Intrusion datasets. Furthermore, the system achieves high blockchain scalability, maintaining stable throughput of 16 tx/s and low transaction latency of 0.062s under increasing ECU density and RSU coverage.
Xander Pottier, Thomas De Ruijter, Jonas Bertels, Wouter Legiest · 6 authors
The Multi-Scalar Multiplication (MSM) is the main barrier to accelerating Zero-Knowledge applications. In recent years, hardware acceleration of this algorithm on both FPGA and GPU has become a popular research topic and the subject of a multi-million dollar prize competition (ZPrize). This work presents OPTIMSM: Optimized Processing Through Iterative Multi-Scalar Multiplication. This novel accelerator focuses on the acceleration of the MSM algorithm for any Elliptic Curve (EC) by improving upon the Pippenger algorithm. A new iteration technique is introduced to decouple the required buckets from the window size, resulting in fewer EC computations for the same on-chip memory resources. Furthermore, we combine known optimizations from the literature for the first time to achieve additional latency improvements. Our enhanced MSM implementation significantly reduces computation time, achieving a speedup of up to x12.77 compared to recent FPGA implementations. Specifically, for the BLS12-381 curve, we reduce the computation time for an MSM of size 224 to 914 ms using a single compute unit on the U55C FPGA or to 231 ms using four U55C devices. These results indicate a substantial improvement in efficiency, paving the way for more scalable and efficient Zero-Knowledge proof systems.
The quality control of investment casting remains a critical challenge due to defect detection, real-time processing, and data traceability inefficiencies. This study presents an innovative Blockchain-integrated IoT system for advanced inspection of casting defects, combining a ResNet-based deep learning model for defect detection and dimensional measurement with Blockchain technology to ensure data integrity and traceability. The system demonstrated a significant improvement in defect detection accuracy, achieving an F1-score of 0.94, alongside high data integrity (0.99) and traceability (0.98) metrics. Additionally, it processes each casting in an average of 2.3 s, supporting a throughput of 26 castings per minute. By addressing critical challenges in smart manufacturing, this approach enhances operational efficiency, regulatory compliance, and user confidence. While scalability and energy efficiency remain areas for improvement, the proposed method provides a transformative solution for Industry 4.0, fostering transparency and reliability in manufacturing processes.
The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse.
Non-fungible tokens (NFTs) are unique digital assets whose possession is defined over a blockchain. NFTs can represent multiple distinct objects such as art, images, videos, etc. There was a recent surge of interest in trading them which makes them another type of alternative investment. The inherent volatility of NFT prices, attributed to factors such as over-speculation, liquidity constraints, rarity, and market volatility, presents challenges for accurate price predictions. For such analysis and forecasting, machine learning methods offer a robust solution framework. Here, we focus on three related prediction problems over NFTs: Predicting NFTs sale price, inferring whether a given NFT will participate in a secondary sale, and predicting NFT's sale price change over time. We analyze and learn the visual characteristics of NFTs by deep pre-trained models and combine such visual knowledge with additional important non-visual attributes such as the sale history, seller's and buyer's centralities in the trading network, and collection's resale probability. We categorize input NFTs into six categories based on their characteristics. Across detailed experiments, we found visual attributes obtained from deep pre-trained models to increase the prediction performance in all cases, and EfficientNet seems to perform the best. In general, CNN and XGBoost consistently outperformed the rest of them across all categories. We also publish our novel NFT dataset with temporal price knowledge, which is the first dataset to have NFT prices over time rather than at a single time point. Our code and NFT datasets are publicly available at https://github.com/seferlab/deep_nft .
As a key enabler of Web3, Ethereum has long faced scalability challenges. The recent EIP-4844 upgrade aims to alleviate the scalability issue by introducing the ''blob'', a new data structure for Layer-2 rollups that enables off-chain storage with much reduced costs. Yet, this new mechanism's impact on Ethereum, and the wider Web3 ecosystem, remains largely underexplored. In this paper, we conduct the first large-scale empirical analysis of the post-EIP-4844 ecosystem, leveraging a dataset of 319.5 million transactions, out of which 1.3 million are blob-carrying. Our analysis reveals two major trends: (1) average block size has increased 2.5 times, from 150 KB to 400 KB, while the share of conventional transactions has shrunk from over $150$ KB to around 80 KB; (2) rollups are rapidly migrating from expensive calldata, falling from approximately 7,500 to nearly zero, toward cheap blobs, rising from zero to about 10,000. These shifts introduce a new economic game between block builders and rollups. Thus, we develop a game-theoretic model to characterize their equilibrium strategies: a profit-maximizing inclusion rule for builders, and a cost-minimizing blob batching strategy for rollups. Empirically, however, we find notable economic inefficiencies: for example, 29.48% of blob-containing blocks are built sub-optimally, yielding less revenue than available alternatives. These findings highlight the intricacies of the blob marketplace, and our work has established both methodological and empirical foundations to understand the evolving post-EIP4844 Ethereum ecosystem.
With the popularization of blockchain technology, the application of smart contracts in various scenarios is becoming increasingly widespread. However, due to its inherent complexity and dynamism, the security issues of smart contracts are gradually becoming prominent. In order to effectively detect and repair defects in smart contracts, this paper proposes a multimodal smart contract defect detection method. This method integrates multimodal data information, including visual, semantic, and inheritance relationship structural information. It combines the Transform method to comprehensively analyze and identify potential defects in the operational status of smart contracts. Compared with other mainstream methods, it has been proven that our method can more comprehensively identify and locate defects and improve detection accuracy and coverage.
This chapter aims to present the core technologies that underpin non-fungible tokens (NFTs). It begins with an exposition of blockchain technology and consensus mechanisms, before highlighting Ethereum’s role in the space and its key standards for NFTs. The chapter then discusses NFT metadata and highlights contemporary use cases of NFTs, before describing the structure and function of NFT marketplaces. A concluding section presents remarks pertinent to the remainder of the volume.
Artists are digitally disenfranchised, rampant online sharing and ease of copying make protecting their work from unauthorized use an uphill battle. Digital art is effortlessly duplicated, shared, and manipulated without credit or compensation, even leading to individuals profiting off stolen work. This widespread issue, particularly plaguing social media, demands a solution. We propose a blockchain-based platform utilizing Non-Fungible Tokens (NFTs) to empower artists. By creating NFTs for their art, they claim irrefutable ownership, authenticity, and copyright, enabling secure sales via cryptocurrencies and combating unauthorized use. This innovative approach empowers artists to thrive in the digital age
Open access
Visual Attention and Saliency Detection
Generative Adversarial Networks and Image Synthesis
In the context of blockchain technology and cryptocurrencies, computer graphics integration seeks to improve data visualization and user interfaces, offering intuitive and aesthetically pleasing experiences. This entails developing immersive user interfaces for decentralized apps, strengthening security using visual cryptography, and increasing accessibility to complicated blockchain data. Visualizations help users comprehend transaction patterns and system efficiency by making blockchain data easier to analyze. The ultimate goal of this strategy is to improve the usability and engagement of interactions with digital assets and blockchain technology. Index Terms: Blockchain technology, cryptocurrency, data visualization, Computer Graphics, Supply chain, finance.
The IOTA Tangle, a Directed Acyclic Graph (DAG)-based distributed ledger, is popular for its scalability and suitability for IoT applications, offering fee-less transactions. A critical component of IOTA’s architecture is the Cumulative Weight Calculation (CWC), essential for its tip selection mechanism. This paper introduces an optimization of the IOTA Reference Implementation (IRI) CWC process originally implemented using Breadth-First Search (BFS) by employing Depth-First Search (DFS) and Iterative Deepening Search (IDS) algorithms. We present a comparative analysis of these methods, demonstrating that DFS and IDS provide significant improvements in computational efficiency, particularly beneficial for IoT devices with limited processing capabilities. Our findings are substantiated through a series of experiments on a Tangle snapshot, highlighting the enhanced performance and reduced resource utilization of the proposed methods. This study contributes to the ongoing development of DAG-based distributed ledgers, offering insights into more efficient algorithmic solutions for large-scale, decentralized networks.
991013340354003412 HKUST Electronic Theses Defect detection for smart contracts and blockchain applications by Wuqi Zhang thesis 2024 1 online resource (xix, 129 pages) : illustrations Blockchain technology has attracted significant interest with the advent of Turing-complete smart contracts, which enable…Read more ›
In this research, we analyzed three different models for Ethereum price prediction: a custom Hidden Markov Models (HMM), GHMM Pretrained, Deep Reinforcement Learning and LSTM. Our results demonstrate the distinct strengths and weaknesses of every model. Although HMM and HMM Pretrained excel in capturing volatility and short-term price fluctuations, the custom model demonstrates remarkable predictive capabilities for long-term trends. The present study provides significant contributions to the field of cryptocurrency price prediction, hence assisting traders, investors, and scholars in maneuvering through the complex Ethereum market.
In the rapidly advancing domain of smart manufacturing, securing data integrity and preventing unauthorized access are critical challenges. This study introduces a novel approach that synergizes anomaly detection techniques with Zero-Knowledge Proofs (ZKPs) to fortify the security framework of smart manufacturing systems. Our methodology employs a combination of data preprocessing, including statistical imputation and data smoothing, alongside advanced anomaly detection using classification methods and neural networks, particularly focusing on deep learning architectures. The detected anomalies undergo verification through zk-SNARKs, a specialized ZKP scheme, ensuring a robust validation process without compromising data confidentiality. Our findings reveal a notable enhancement in the accuracy of anomaly detection, achieving detection rates of approximately 95% for temperature fluctuations and 90% for pressure irregularities, with a significant reduction in false positives. This performance is markedly superior to traditional methods and aligns closely with the highest efficacy rates reported in contemporary studies. Moreover, the utilization of ZKPs for anomaly verification demonstrated a 98% success rate, ensuring the secure and private verification of anomalies. The integration of anomaly detection with ZKPs presents a significant leap forward in addressing the security vulnerabilities inherent in smart manufacturing. This study not only showcases the effectiveness of our approach in enhancing data security and integrity but also sets a benchmark for future research in creating more resilient and trustworthy industrial operations.
Non-fungible tokens (NFTs) have recently gained widespread popularity as an alternative investment. However, the lack of assessment criteria has caused intense volatility in NFT marketplaces. Identifying attributes impacting the market performance of NFT collectibles is crucial but challenging due to the massive amount of heterogeneous and multi-modal data in NFT transactions, e.g., social media texts, numerical trading data, and images. To address this challenge, we introduce an interactive dual-centric visual analytics system, NFTeller, to facilitate users’ analysis. First, we collaborate with five domain experts to distill static and dynamic impact attributes and collect relevant data. Next, we derive six analysis tasks and develop NFTeller to present the evolution of NFT transactions and correlate NFTs’ market performance with impact attributes. Notably, we create an augmented chord diagram with a radial stacked bar chart to explore intersections between NFT collection projects and whale accounts. Finally, we conduct three case studies and interview domain experts to evaluate the effectiveness and usability of this system. As such, we gain in-depth insights into assessing NFT collectibles and detecting opportune moments for investment.
In traditional fruit traceability systems, the opacity of the process and the ease of data tampering have always been major challenges. To address this, we propose a fruit traceability system based on blockchain and computer vision in this paper. In terms of data storage, we chose the Ethereum blockchain to ensure the authenticity of the data. As the fruit passes through each node, we upload the fruit image and conduct quality checks. We trained the YOLOv5 model to identify fruit types, and on this basis, we trained an improved Unet model for saliency detection to extract the fruit image area, both of which performed very well. Based on the fruit image, we evaluate from four aspects: saturation, hue, defects, and shape. This system can be used for traceability throughout the fruit supply chain.
Decentralized Finance (DeFi) is revolutionizing the traditional financial system by offering trustless financial transactions and services worldwide through blockchain technology.Yield farming is a well-known DeFi application enabling users to earn passive income in cryptocurrency through providing liquidity to decentralized protocols.This research paper focuses on yield farming, its working mechanisms, the benefits and risks involved, the various strategies deployed, and its future in the DeFi ecosystem.
Decentralized machine learning (DML) is a new paradigm in artificial intelligence (AI) that combines the power of distributed computing and blockchain technology to enable secure and privacy-preserving machine learning.In DML, multiple devices or nodes collaborate to train a machine learning model without sharing their data, thereby enhancing data privacy and security.This research paper provides a comprehensive review of recent developments in DML on the blockchain, including its applications, challenges, and potential solutions.The paper analyzes relevant literature and case studies to highlight the advantages and limitations of DML on the blockchain.The study looks at the many consensus techniques used in DML and how they affect system performance, including proof-of-work, proof-of-stake, and proof-of-authority.The function of smart contracts in DML and how they might improve the system's security and transparency are also discussed in the paper.The paper also covers DML on the blockchain's difficulties and potential solutions, including scalability, interoperability, and privacy issues.According to the study's results, DML on the blockchain has the power to change the AI industry by providing safe and private machine learning.To address the technological and nontechnical problems, however, it also requires additional research and development.To fully realize the potential of DML on the blockchain, the study emphasizes the necessity of a coordinated effort by researchers, developers, and policymakers.