The rapid evolution of digital finance has transformed the way payments are processed globally, leading to a growing comparison between cryptocurrency-based payment systems and traditional payment systems. Traditional payment systems, such as banks, credit cards, and online payment gateways, have long been the backbone of financial transactions, offering reliability and regulatory oversight. However, these systems often face challenges related to transaction speed, high processing costs, and centralized security risks. In contrast, cryptocurrency payment systems leverage blockchain technology to enable decentralized, peer-to-peer transactions that promise faster settlement, reduced transaction fees, and enhanced transparency. This study compares crypto-based payment systems and traditional payment systems across three critical dimensions: speed, cost, and security.
Open access
Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
Blockchain technology has evolved from its initial application in cryptocurrencies such as Bitcoin to a versatile decentralized infrastructure supporting decentralized finance (DeFi), digital identity systems, smart contracts, and Web3 ecosystems. Despite its transformative potential, the rapid expansion of blockchain platforms has significantly increased the security attack surface, exposing networks to threats such as double-spending, Sybil attacks, smart contract vulnerabilities, transaction laundering, and large-scale financial fraud. At the same time, the emergence of quantum computing introduces a fundamental challenge to classical cryptographic mechanisms particularly Elliptic Curve Digital Signature Algorithm (ECDSA) and RSA that form the backbone of blockchain authentication and transaction verification. This paper presents a comprehensive study of Machine Learning (ML) techniques and Post-Quantum Cryptographic (PQC) frameworks for strengthening blockchain security and threat detection. The study reviews supervised, unsupervised, and deep learning models used for fraud detection, anomaly identification, smart contract vulnerability analysis, and blockchain transaction monitoring. In parallel, it examines quantum-resistant cryptographic algorithms emerging from the NIST post-quantum standardization process, including lattice-based, hash-based, and code-based schemes, and evaluates their suitability for blockchain environments. Furthermore, the paper analyzes the limitations of ML-based security mechanisms and the practical challenges of integrating PQC into decentralized infrastructures, including scalability, key size overhead, and performance trade-offs. A comparative analysis highlights that ML enhances adaptive behavioral threat detection, while PQC ensures long-term cryptographic resilience against quantum attacks. Therefore, the study emphasizes the importance of a hybrid ML–PQC security model that combines intelligent anomaly detection with quantum-resistant cryptographic protection. Finally, the paper identifies key research challenges and outlines future directions toward building scalable, adaptive, and quantum-secure blockchain ecosystems capable of supporting next-generation decentralized applications.
Ameer Majeed Dahdouh Al-Alili, Zaid Salam Abdullah
This research aims to clarify the actual works of criminal liability for crimes committed using cryptocurrency and to highlight the flaws of Iraqi legislation about this modern type of crime. Accordingly, an attempt has been made to analyse the elements, kinds, and difficulties of evidence, leading up to determining the legal system in which to protect from and suppress this type of crime, of which cyberspace is a part. This research is descriptive-analytical in nature, where legislation has been examined. The research indicates that the wide scope of risks involving cryptocurrency crimes makes it difficult to subject them to existing laws on movable property, especially since the legislator has omitted the criminalization of certain attacks like wallet hacking, while the sophisticated nature of making inquiries and collecting evidence complicates establishing a definitive link between the perpetrator and the transaction. All in all, this study finishes off with the need to develop or amend legislation to extend the definition of digital assets and criminalise attacks against them, strengthen investigative capacity in electronic tracking, establish units for cryptocurrency crimes, and regulate digital seizures and confiscation mechanisms. This further highlights the importance of modernising legislation in light of the criminal threat’s cryptocurrencies pose to upholding economic and legal security.
Block chain technology has gained significant attention across multiple domains such as finance, healthcare, education, and real estate. It serves as the foundational technology behind cryptocurrencies, enabling secure and decentralized digital transactions. Transactions are carried out using digital wallets on computing devices and are permanently recorded as blocks linked together in a distributed ledger known as the block chain. This paper presents a comprehensive study of block chain technology, its operational principles, consensus mechanisms, and real-world applications. It also explores the integration of artificial intelligence techniques to enhance security, scalability, and trust in block chain-based cryptocurrency systems.
The increasing incidents of forged academic certificates and the inefficiencies of traditional verification systems highlight the urgent need for a secure, transparent, and reliable credential management mechanism. Conventional systems are largely centralised, time-consuming, and prone to manipulation, resulting in high administrative overhead and verification delays. We prepared an AI-Based Decentralized Academic Credential Verification System that leverages blockchain technology, smart contracts, and artificial intelligence to provide a tamper-proof platform for issuing, storing, and validating academic records. Artificial Intelligence is integrated to perform anomaly detection during certificate issuance and AI-based facial authentication for students, enhancing security and preventing fraudulent entries before blockchain storage. Students gain permanent, secure access to their verified credentials, while verifiers, such as employers, can instantly authenticate certificates using blockchain records or QR code scanning, eliminating the need for intermediaries. By integrating Ethereum, Solidity, Web3.js, IPFS, React.js, and AI models, the proposed system delivers a decentralized, scalable, and cost-effective solution that enhances trust, reduces verification time, and effectively combats academic credential fraud.
Dr. A. Radhika, D. Avinash, D. Sowjanya, K. Karthik · 5 authors
The increasing use of digital communication has made it essential to maintain the confidentiality, integrity, and authenticity of sensitive information. Conventional image steganographic methods offer data hiding in digital images, but they fail to offer effective tamper proofing and secure ownership verification. To overcome these issues, this paper presents a Blockchain-Integrated Secure Image Steganography system using IPFS and Ethereum. In the proposed system, secret data is hidden within digital images using a Least Significant Bit (LSB) image steganographic method developed in Python. The stego images are then stored in the Inter Planetary File System (IPFS) for efficient and decentralized data storage. To ensure data integrity and secure access, the cryptographic hash values of the stego images and their corresponding IPFS Content Identifiers (CIDs) are securely stored on the Ethereum blockchain using smart contracts. The use of blockchain technology provides immutability, transparency, and tamper resistance, and IPFS provides decentralized storage without depending on centralized storage servers. The proposed system is validated to offer high image quality with negligible distortion and robust data security and traceability. This system is applicable for secure data sharing in confidential communication, digital forensics, and secure document transfer.
Open access
Advanced Steganography and Watermarking Techniques
Abstract The rapid growth of digital finance—including FinTech platforms, online payment gateways, and e-commerce marketplaces—has revolutionized global financial systems while significantly expanding the cyber-attack surface. Sophisticated attacks such as AI-generated deepfakes, automated malware, ransomware, and synthetic identity fraud now threaten financial transactions. In response, cybersecurity strategies are evolving toward decentralized models, AI-enabled detection systems, Zero Trust architectures, and quantum-safe cryptography. This paper synthesizes recent academic research and industry developments (2025–2026), covering threat taxonomies, defensive strategies, emerging attack vectors, and regulatory enhancements in payment authentication. The integration of these trends underscores the necessity of robust, AI-driven, and compliance-aware security architectures for securing modern financial ecosystems.
Chukwuebuka Francis Ikenga-Metuh, Abel Yeboah-Ofori
Background: Blockchain technology has emerged as a transformative communication solution for securing distributed systems. However, several vulnerabilities exist during transactions, including latency and network congestion issues during mempool processing, topology weaknesses, cross-chain bridge exploits, and cryptographic weaknesses. These vulnerabilities have led to attacks that have threatened system integrity, including Block Extractable Value (BEV) attacks, Maximal Extractable Value (MEV) attacks, sandwich attacks, liquidation, and Decentralized Finance (DeFi) reordering attacks, among others. Thus, implementing a robust security framework based on the Confidentiality, Integrity, and Availability (CIA) triad remains critical for addressing modern blockchain technology threats. Objective: This paper examines blockchain technology, its various vulnerabilities, and attacks to determine how criminals exploit the system during transactions. Further, it evaluates its impact on users. Then, implement a blockchain attack in a “MasterChain” virtual environment to demonstrate how vulnerable spots can be practically exploited and discuss the application of the CIA security triad through modern cryptographic primitives. Methods: The approach considers Hevner’s design science framework, which emphasizes creating innovative artifacts that address identified problems while contributing to the knowledge base through rigorous evaluation. Furthermore, we developed a MasterChain tool using Python with Flask for distributed node communication, utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA) with the Standards for Efficient Cryptography Prime 256-bit Koblitz curve 1 (secp256k1) for digital signatures and Secure Hash Algorithm 3 (SHA-3) (Keccak-256) hashing for block integrity. Results: show how the CIA has been implemented to provide secure communication through ECDSA-based transactions, SHA-3 chain integrity verification, and a multi-node distributed architecture, respectively. The performance analysis shows that ECDSA provides 256-bit security with 64-byte signatures compared to 2048-bit Rivest–Shamir–Adleman (RSA)’s 256-byte signatures, achieving a 75% reduction in bandwidth overhead. SHA-3 provides immunity to length extension attacks while maintaining equivalent collision resistance to SHA-256. Conclusions: The MasterChain framework provides a practical foundation for implementing blockchain security that addresses both classical and emerging vulnerabilities. The adoption of ECDSA and SHA-3 (Keccak-256) positions the system favourably for modern blockchain applications, while providing insights into the cryptographic trade-offs between performance, security, and compatibility.
Abstract This study explores transformation of business and IT through the lens of five emerging technology fields: artificial intelligence, Machine Learning, Data Analytics, Data Science and Blockchain. The contemporary business landscape is undergoing a profound transformation driven by the convergence AI, ML, DS, DA, and Blockchain technology. Individually, these technologies offer significant advancements: AI and ML provide sophisticated decision- making and automation capabilities, while data analytics and data science extract actionable insights and non-obvious patterns from vast datasets. Blockchain technology, a decentralized and immutable ledger, establishes a foundation of trust, transparency, and security in data management and transactions. By facilitating automation, data-driven decision-making and Data security all the above technologies transforming number of industries. The synergistic integration of these technologies creates novel business models and powerful operational enhancements in smart contract, Data sharing, Decentralized AI Marketplaces, cybersecurity. Important methods to use with these technology are covered including supervised learning, unsupervised learning, deep learning, descriptive analytics, predictive analytics, prescriptive analytics and distributed ledger technology. The challenges are also discussed, such as data privacy and quality, high cost, skill gap and interoperability. This study highlights opportunities and challenges in current trends available in AI, ML, DA, DS and Blockchain on business and IT sector. Though challenges related to scalability, regulatory compliance, and implementation complexity exist, ongoing technological advancements are actively addressing these barriers. It will be overcome by doing a thorough assessment of recent studies and identifying the potential benefits, impacts, and future directions of all the five technologies.
The global logistics sector is confronted with crucial data reliability challenges wherein traditional centralized systems have a 15-20% manual error rate and are highly susceptible to counterfeiting. In this regard, the current research proposes Sentinel, a decentralized supply chain tracking framework utilizing the Polygon Proof-of-Stake blockchain coupled with smart contracts in Solidity for granting immutability to data governance. It follows a hybrid architecture wherein on-chain cryptographic verification is coupled with MongoDB for high-speed off-chain data retrieval. Extensive performance testing was performed on a simulated supply chain network with 10,000 transaction cycles of creation, transfer, and delivery. It shows that Sentinel has been able to achieve 100% in data integrity, thus rejecting all 500 unauthorized ledger modifications attempted during security stress testing. In terms of efficiency, the proposed framework minimized data retrieval latency to less than 180 ms, which was an improvement of 92% compared to traditional decentralized architectures. Additionally, it minimized the transaction cost to roughly ₹0.45/unit, thus offering a cost reduction of about 99.9% compared to traditional Ethereum Layer-1 implementations.
Abstract The rapid evolution of computer technology is changing digital ecosystems, business processes, governmental operations, and how humans use computers to perform tasks. This paper is a comprehensive analysis of modern computer technology trends, including advancements in artificial intelligence; cloud computing; edge computing; the internet of things (IoT); 5G networks; blockchain; cybersecurity; quantum computing; emerging technologies such as immersive technologies and robots; big data; and sustainable computing. In this extensive review of how these advances work together to drive digital transformation, this paper synthesizes current research from academic literature with real-world applications of computer technologies from industry. The paper includes discussions regarding the emergence of generative AI and multimodal ML methods, explainable AI, and intelligent automation as new methods to generate better decision-making results and innovations within the business sector. It includes descriptions of multi-cloud/hybrid architectures, serverless computing, edge AI, and fog computing as ways to achieve low-latency scalable infrastructure; and ultimately describes use cases for using IoT with AI-enabled analytic platforms for smart cities; IIoT; and real-time data ecosystems. Cybersecurity subjects discussed in this paper include innovations such as Zero Trust Architecture, AI-based threat detection, and quantum-resistant cryptography. Emerging technology paradigms like blockchain-powered decentralized apps (DApps), Web3 environments, quantum algorithms, AR/VR/MR technologies, and smart robots are examined for potential to change organisations and challenges encountered during their adoption. 'Green computing' strategies are discussed in terms of developing low carbon power systems, creating carbon aware IT systems, and developing sustainable IT practices that reduce environmental impact. This study also explores advances in the fields of human computer interaction, accessibility technology, and ethical governance frameworks, with a focus on society's responsibility to develop inclusive and responsible technological products. The research has revealed multiple challenges that prevent sustainable technology development from progressing, including: scalability; interoperability; regulatory compliance; security threats; digital equity; and adapting to the workforce's new skill sets caused by this shift to sustainable technology. Therefore, developing sustainable technology will require multi-disciplinary co-operation; ethical guidance/path; strategic governance; and continuous innovation in technology development. By combining a technical assessment of IT technology along with a social perspective; an umbrella of knowledge will form to forecast how IT technologies will advance during the period referred to as the era of Intelligent Connected Systems.
Modern precision agriculture depends on safe and effective fertilizer management. However, existing systems lack real-time decision-making capabilities, rarely incorporate secure traceability methods, and mainly concentrate on nutrient prediction without determining the type of soil fertilizer utilized for a specific crop. To classify fertilizer types (organic vs. inorganic) in real-time based on soil nutrient parameters (temperature, pH, EC, N, P, and K), this investigation suggests an innovative, lightweight self-attention transformer neural network (TNN) based Fertilizer class contract network (FCCN) model. The proposed research is one of the first to combine secure blockchain recording, fertigation, and fertilizer-type detection into a single edge-based pipeline that operates in real time. The process integrates blockchain-based transaction logging and IoT-edge computing for recording transparent and secure agricultural activity. Whenever deficits emerge, the suggested method uses Venturi irrigation to automatically activate fertigation after processing real-time sensor data at the edge to determine the types of fertilizer utilized and the nutritional status. This work uses a decentralized and scalable architecture compared to cloud-dependent or AI-based-only models. Fertilizer classification and fertigation actions based on the real-time nutrient level recommendation are recorded as immutable transactions on an Ethereum blockchain using a Proof-of-Stake (PoS) consensus. Before the final on-chain recording, validator logic confirms the accuracy of field data, fertigation events, and real-time soil nutrient levels. Real-time blockchain measurements reveal transaction completion speeds of less than 0.03 seconds, gas consumption of less than 62,000 units, and throughput of 15-35. Experimental findings show that FCCN categorization accuracy surpasses 98.85%.
Building structural designs, utilizing materials such as steel, concrete, and cross-laminated timber, contribute significantly to embodied carbon emissions in construction projects. However, traditional carbon accounting methods employed to quantify and record these emissions are often characterized by a lack of traceability, transparency, and immutability. This limitation undermines the reliability of emissions data, making it challenging for stakeholders to establish credible emissions records and implement regulatory strategies, such as carbon credits, taxes, subsidies, and green certifications, for building’s structural designs and materials. This paper addresses these challenges by proposing a transformational emissions accounting system that integrates Building Information Modeling (BIM) for automatic extraction of emissions-relevant data, alongside blockchain-enabled smart contracts to ensure traceability and immutability of emissions records. The proposed system enables data-driven decision-making for low-carbon structural designs and materials, while also facilitating the application of emissions regulations to support their implementation based on trustworthy emissions accounting.
muddasar naeem, Zaib Ullah, Fadi M. Al Turjman, Abdullah waqas · 6 authors
Abstract Blockchain technology, characterized by its distributed ledger system, has revolutionized currency and the global digital economy. Its potential applications extend beyond finance, captivating academia and industry alike. This article delves into the latest advancements in blockchain technology, exploring its innovative state-of-the-art applications in various futuristic communication domains such as the Internet of Things (IoT), cloud computing, security, privacy, artificial intelligence (AI), wireless network optimization, intelligent grids, smart transportation, and more. The article also provides an overview of blockchain's operational principles, including smart contracts and consensus algorithms, highlighting their contributions to decentralization, security, and transparency. Moreover, through statistical analysis of research articles, we identify current challenges and outline future research directions in the field.
Permissionless Proof-of-Stake (PoS) blockchain networks must demonstrate security and dependability as blockchain technology evolves. This research analyzes the ongoing need for a way to identify and eradicate rogue nodes inside such networks, also to advocate for the real-time rogue node’s identification in PoS blockchain networks by the application of neural network methodologies, particularly random neural networks. Experimental results indicate the ability of the developed model in differentiating legitimate blockchain nodes from malicious ones. The dataset in this paper is divided into two groups- malicious (Permissionless Proof-Of-Stake Blockchains) and non-malicious; this dataset is crucial for anyone interested in blockchain. The dataset includes details of the creation and validation of the PoS blocks. The paper aims to detect malicious nodes, analyze node behavior, and enhance security on PoS permissionless blockchains through data visualization. This paper shows the performance and process of the random neural network to refine and learn and then recognize the permissionless blockchain (malicious nodes) from the dataset of Proof-of-Stake Blockchain. We selected 100 records from the original dataset to examine them with our proposal. After analyzing the results, we found clearly how the proposal algorithm works properly with the proposed dataset to achieve fine accuracy and efficiency in the work to distinguish benign nodes from malicious (permission-less blockchain) nodes. This is the result of refining and learning the dataset using the random neural network for 340 instances, coming from the neural learning of 100 instances and 21 variables: [15 features: BlockHeight, UnixTimestamp, TxnFee (ETH), Block Generation Rate, TxnFee (Binary), Status (Tags), Stake Reward, Txnsize, Coin Days, Coin Age, Coin Stake, Stake Distribution Rate, Block Density, Block Score, Coin Day Weight) + 5 meta (node label, neural network, neural network0, neural network1, fold, selected) + no missing value), by two attributes: (node label, block score), two classes(0 non-malicious nodes,1 malicious nodes).
José-María Oliet-Villalba, Jose-Amelio Medina-Merodio, Mikel Ferrer-Oliva, José-Javier Martínez-Herráiz
The rapid growth of cryptocurrencies and non-fungible tokens (NFTs) has expanded technological opportunities, but it has also increased the exposure surface to cyber threats, creating a need for a more precise understanding of the field’s scientific evolution. This study aims to systematically analyse academic output related to cybersecurity and cyber threats within cryptocurrency and NFT ecosystems, identifying central themes, the most influential authors, and emerging trends. A bibliometric methodology was employed, based on the PRISMA 2020 protocol and scientific mapping tools such as SciMAT (v1.1.06) and VOSviewer (v1.6.20), using a corpus of 337 articles published between 2014 and 2025. The findings indicate sustained growth in the literature, a marked geographical and editorial concentration, and the presence of motor themes such as blockchain, cybersecurity, emerging technologies and illegal mining, alongside emerging areas such as intrusion detection. The results also reveal a progressive integration of artificial intelligence techniques in the detection and prevention of attacks. In conclusion, this study provides a comprehensive overview of the state of the art, identifies critical gaps, and underscores the need for interdisciplinary approaches to strengthen security in decentralised environments.
Manaf Ahmed, Mohammed Adnan, Ali Matar, Faez Hlail Srayyih · 7 authors
Predicting cryptocurrency price is challenging owing to high volatility, less historical data, and the impact of external parameters like news, public sentiment, and regulatory announcements. This challenge is tackled in this research by employing models of deep learning like Recurrent Neural Network (RNN), Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU)—to predict Bitcoin's OHLC prices daily. Based on historical time-series data of Coin Codex, the research uses an autoencoder-based feature extraction method with five-day sliding window method for sequence generation. Hyperband optimization is used to tune hyperparameter of each model. The result shows that BiLSTM performs better than all the other models with minimum Mean Squared Error (MSE = 0.001183), Mean Absolute Error (MAE = 0.026090), and maximum R² score (0.980596) after optimization. The results emphasize the significance of deep learning in capturing nonlinear dynamics in time series of financial applications and bear testimony to the effectiveness of hyperparameter tuning in enhancing model accuracy. The study enhances the development of prediction tools for digital asset markets and enables more informed investment decisions.
India holds a crucial place in the worldwide leadership of sustainable development since it is the largest democracy in the world and has one of the nations with the greatest economic growth. With innovation, inclusivity, and sustainability at its core, Viksit Bharat @2047 symbolizes India's ambition to become a fully developed country by the century of its independence. Emerging technologies are increasing productivity, boosting global competitiveness, and spurring innovation in various industries. By providing tailored financial assistance & investment suggestions, artificial intelligence-powered chatbots & robo-advisors are democratizing the provision of financial planning services. Decentralized finance (DeFi) systems and other blockchain-based solutions are simplifying trade finance procedures, lowering operating costs, and facilitating safe and transparent cross-border transactions. This chapter examines how innovation and technology are essential to achieving this lofty goal. It provides a thorough examination of India's contemporary digital infrastructure, the country's ascent in international innovation rankings, & the use of cutting-edge technologies including biotechnology, renewable energy, artificial intelligence, and space research. The story highlights government programs such as Start-up India, Digital India, and the National AI & Green Hydrogen Missions. Furthermore, the story underscores the importance of inclusive growth, which encompasses youth empowerment, women-led innovation, and rural digitization. Alongside strategic advice, issues like cybersecurity concerns, low investment in research and growth, and the digital divide are also discussed. India is positioned to emerge as a worldwide leader in technology, not simply a consumer, by cultivating a strong innovation ecosystem and utilizing partnerships between university, industry, and the private sector. This chapter provides a comprehensive plan for a tech-powered, inclusive, and sustainable Viksit Bharat before 2047. Higher education is one of the areas that must use developing technology, especially artificial intelligence (AI), to achieve Viksit Bharat 2047 (the Developed India 2047). Outside of higher education, artificial intelligence influences technology and economic progress. Young minds will realize this transformative vision as soon as they actively interact with AI. AI literacy empowers students in higher education to investigate, produce, and innovate. Students may do research, find solutions to real-world issues, and alter the course of history as they learn AI.
Open access
Innovation and Socioeconomic Development
Innovations and Analysis in Business and Education
Blockchain technology has emerged as a pivotal and transformative force, establishing transparent, secure, and decentralized frameworks for transaction management. Its core strengths include immutability, data decentralization, and consensus validation, alongside the automation provided by self-executing smart contracts. This review examines its foundational technologies, diverse applications, and associated challenges. Blockchain demonstrates profound potential across sectors like finance (e.g., Anti-Money Laundering and fraud reduction), education (credential verification), healthcare (secure record management), and the Metaverse (verifiable digital asset ownership via non-fungible tokens). However, adoption is significantly hindered by critical issues, including scalability bottlenecks, the energy inefficiency of protocols like Proof of Work, and security risks stemming from smart contract flaws, with case-based testing revealing up to 40% of public contracts have exploitable vulnerabilities. Recent advancements in high-throughput rollups and formal verification mitigate these risks. This coincides with a 2025 shift toward structured legal mandates, such as the EU’s MiCA, India’s VDA policy, and the U.S. GENIUS and CLARITY Acts. Therefore, future research must prioritize enhancing smart contract verification, developing energy-efficient consensus mechanisms, cross-chain interoperability, and fostering the continued alignment of supportive legal and regulatory frameworks.
The increasing importance of privacy and secure communication in distributed environments has fueled research into innovative solutions that combine data concealment and tamper-resistant recordkeeping. This article presents a logically structured architectural framework for covert steganographic communication, utilizing the Microsoft Azure web3 ecosystem as its foundation. The motivation behind this research stems from the limitations of traditional steganography and blockchain technologies when used independently, particularly in addressing the challenges of operational transparency, scalability, and robust data protection. To bridge these gaps, the proposed system integrates Azure Blockchain Development Kit with other Azure native services to provide a unified architecture. This research article introduces a pioneering architectural framework designed to facilitate covert steganographic communication through blockchain technologies, with a focus on leveraging the Microsoft Azure web3 ecosystem. By integrating Azure Blockchain Development Kit (BDK), Azure Confidential Ledger, Azure Blockchain Services, and Azure Blockchain Workbench with Open Steganography solutions deployed on Azure Virtual Machines (VM), the proposed system aims to achieve secure, confidential, and unobtrusive data exchange. The research methodology encompasses a comprehensive literature review, system design, implementation, and rigorous security analysis, followed by experimental evaluation on cloud infrastructure. By leveraging the strengths of Azure’s blockchain and confidential ledger capabilities alongside advanced steganographic techniques, this study demonstrates a practical approach to achieving secure, confidential, and unobtrusive data exchange. The findings confirm the feasibility and effectiveness of the proposed solution, highlighting its potential to facilitate adaptive, scalable, and privacy-preserving covert communication networks. In conclusion, this work charts new directions for integrating blockchain and steganography within cloud-native platforms, offering enhanced privacy and security for sensitive communications in distributed settings.
This study reviews the advancements in AI-driven methods for predicting stock prices, tracing their evolution from traditional approaches to modern finance. The role of AI in the market extends beyond predictive systems to encompass the intersection of financial markets with emerging technologies, such as blockchain, and the potential influence of quantum computing on economic modeling. A decentralized finance system examines the application of Reinforcement Learning in financial market prediction, highlighting its potential for continuous learning from dynamic market conditions. The study discusses the development of hybrid prediction models, stock market machine learning systems, and AI-driven investment portfolio management. The potential of quantum computing enhances portfolio analysis, fraud detection, optimization, and asset valuation for complex market predictions, as well as the impact of blockchain technologies on transparency, security, and efficiency. Machine learning techniques can significantly automate data collection and purification. Financial decision-making and the application of time-series analysis techniques can be readily learned through deep reinforcement learning for stock price prediction. Deep Neural Networks and Strategic Asset Allocation can be managed by evaluating performance and portfolio using real-time market insights from AI models. Although there are numerous ethical, sentimental, regulatory, and data quality issues in market prediction, the future job market is heavily dependent on these criteria, particularly through effective risk management and fraud detection.
In this work, we utilize the blockchain transactions and financial instruments to pre-dict the Bitcoin price using machine learning. We use three models: Light Gradient Boosting Machine (LightGBM), Decision Tree Regressor and Random Forest Regressor applied on a feature set which includes lagged close prices, 14-day Simple Moving Av-erage (SMA), Relative Strength Index (RSI) and daily confirmed Bitcoin transactions. The data is temporally aligned and pre-processed to maintain temporal coherence, as well as for conversational fluency. Through the results assessment by means of RMSE MAE, MAPE and R², we can found that Random Forest model has results closer to best performance with values of: 264.81 (RMSE); 175.41(MAE); for MAPE is 0.27% and; R² equals to 0.9958. Our findings also lend strong support for the effectiveness of simul-taneously considering not only blockchain-specific market variables but also tradi-tional financial predictors towards improved model performance and generalization. Our findings underscore the importance of raw blockchain transaction data for pre-dicting cryptocurrency prices, and present a new tool for data-based decision making in decentralized finance.
Guma Ali, Otim Emmanuel, Maad M. Mijwil, Bosco Apparatus Buruga · 6 authors
The rapid digitalization of financial services has given rise to smart finance ecosystems that integrate FinTech platforms, Internet of Things (IoT) devices, cloud infrastructures, and decentralized applications. While these systems enhance automation, operational efficiency, and financial inclusion, their highly distributed, data-intensive architectures introduce critical security, privacy, and trust challenges. In this context, artificial intelligence (AI) and blockchain have emerged as complementary technologies capable of addressing these challenges through intelligent decision-making, advanced threat detection, data integrity, and transparent operations. This survey provides a comprehensive review of recent research on securing smart finance systems using AI- and blockchain-based approaches. The survey comprehensively analyzed research published between 2023 and 2026 using the Scopus database, focusing on the keywords “AI,” “blockchain,” and “smart finance.” The analysis reveals extensive use of AI-driven security mechanisms, including credit scoring and risk assessment, transaction monitoring and fraud detection, anti-money laundering (AML) and know-your-customer compliance, identity verification, cyber threat detection, smart contract security analysis, behavioral biometrics, insurance fraud detection, and market risk prediction. In parallel, the survey examines blockchain-enabled security solutions, including secure payment and settlement systems, cross-border remittances, AML and counter-terrorism financing frameworks, digital identity management, smart contracts, asset tokenization, decentralized finance, auditability, and secure interbank communication. The integration of AI and blockchain offers significant advantages, including improved fraud detection accuracy, enhanced transparency and traceability, stronger data integrity, automated compliance, real-time threat response, and increased system resilience. Despite these benefits, key challenges persist, particularly in scalability, privacy preservation, interoperability, regulatory and ethical compliance, energy efficiency, explainability, and post-quantum security. The survey concludes by outlining future research directions and design guidelines for developing secure, scalable, and trustworthy smart finance systems that effectively leverage the integration between AI and blockchain.
Abstract The relentless growth of connected devices is transforming industrial, urban and domestic environments, yet it also expands the attack surface for distributed denial of service (DDoS), unauthorized access and data manipulation. Centralized security architectures struggle to cope with the scale and heterogeneity of the Internet of Things, creating single points of failure and privacy risks. This review takes a close look at how blockchain and artificial intelligence (AI) can work together to solve these problems. Blockchain plays an important role in decentralizing trust, maintaining data integrity, and enabling transparent audit trails. AI subfields such as machine learning (ML), deep learning (DL), reinforcement learning (RL), and multi-agent systems (MAS) enhance these benefits. They enable real-time anomaly detection, predictive analytics, and adaptive policy control. A seven axis Blockchain–AI Security Integration Schema (BASIS) is proposed to classify solutions by security objectives, intelligence modalities, trust primitives, deployment choices, scalability techniques, privacy controls and interoperability mechanisms. In this study also review Layer-2 consensus protocols, federated learning and lightweight deep learning models that address energy and computational constraints. Case studies from supply chains, healthcare and smart grids illustrate the benefits and limitations of current deployments. The evidence suggests that while AI improves the accuracy and responsiveness of threat detection, blockchain offers tamper-proof data provenance. However, there are still issues in achieving scalability, reducing computational overhead, and striking a balance between auditability and privacy. Hybrid on-chain/off-chain architectures, quantum-safe cryptography, and standardized frameworks to guarantee adoption and interoperability are some future research avenues.