Dileep Kumar Murala, Shadab Ahmad, V. A. Sankar Ponnapalli, Veera Ankalu Vuyyuru · 5 authors
Wireless sensor networks (WSNs) will be necessary for the next generation of Internet of Things (IoT) apps. They make it possible to use smart and long-lasting sensors and smart automation in healthcare, Industry 4.0, and critical infrastructure. But security is particularly hard since they have built-in flaws, not enough computer power, not enough energy, and a significant danger of insider threats. Standard encryption methods aren't enough, and in situations where resources are restricted, heavier blockchain or machine learning solutions aren't always possible. This study presents ChainShieldML, a lightweight hybrid security architecture that combines Blockchain (BC) and machine learning (ML) to provide decentralised, adaptive, and resource-efficient protection for wireless sensor networks (WSNs). The idea is based on a two-pronged defence strategy. The Blockchain Prevention Module's permissionless blockchain architecture for base stations and cluster heads makes it possible to verify identities, maintain trust in a decentralised way, and keep node interactions unchangeable. Smart contracts made in solidity and connected to the Ethereum ecosystem make it possible to safely register nodes and keep an eye on what they do. The VBFT consensus algorithm makes it possible to quickly validate without using as much computing power as most proof of work methods. The machine learning detection module uses the lightweight gradient boosting method (LightGBM) to find and rank dangerous nodes in real time. LightGBM is the best machine learning classifier when looking at things like recall, F1-score, Matthews correlation coefficient, training cost, and inference latency. ChainShieldML dramatically improves the detection of insider attacks, builds trust, and protects data while using very little energy and having very little communication delay, as shown in tests. For Wireless sensor networks (WSNs) to keep working, all of these things are very important. ChainShieldML is a novel solution to keep IoT devices safe. It uses blockchain's decentralised trust and ML's adaptive intelligence to make a defence system for next-generation wireless sensor networks that can grow, is strong, and is ready for the future.
Blockchain has become peer-to-peer immutable distributed ledger technology network, and its consensus protocol is essential to the management of decentralized data. The consensus algorithm, at core of blockchain technology (BCT), has direct impact on blockchain's security, stability, decentralization, and many other crucial features. A key problem in development of blockchain applications is selecting the right consensus algorithm for various scenarios. Ensuring scalability is the most significant drawback of BCT. The industry has been rejuvenated and new architectures have been sparked by the usage of consensus protocols for blockchains(BC). Researchers analyzed shortcomings of proof of work (PoW) consensus process and subsequently, alternative protocols like proof of stake (PoS) arose. PoS, together with other improvements, lowers the unimaginably high energy usage of PoW, making it protocol of time. In PoS, only the user with highest stake becomes the validator. To overcome this, we propose Scalable Proof of Stake (SPoS), a novel consensus protocol, which is an enhancement of PoS protocol. In the proposed algorithm, each stakeholder based on the stake gets a chance to become the validator and can mine blocks in the blockchain. Clustering of the stakeholders is done using mean shift algorithm. Each cluster gets a different number of blocks to mine in BC. Cluster with highest stake will get a greater number of blocks to mine when compared to other groups and the cluster with the least stake gets least number of blocks to mine when compared to other groups. To mine the blocks, validator is chosen based on the cluster in which he is present. Fair mining is ensured for all stakeholders based on number of stakes. Mining is distributed among all the stakeholders. Since the validators are chosen fast, the transaction rate is high in the network. Validators in PoS are selected according to the quantity of cryptocurrency they stake. More stakeholders will get chance of validating blocks and receiving rewards. Over time, this reduces fairness and decentralization by concentrating on wealth and power. This is addressed in SPoS using clustering-based validator assignment.
Abstract- This survey paper presents a comprehensive examination of blockchain technology and its diverse applications across multiple domains. It begins by outlining the historical evolution and fundamental structure of blockchain systems, followed by an in-depth discussion of the core technologies that enable decentralized and secure operations. The study further explores the potential applications of blockchain, highlighting its transformative impact on various industries. Additionally, the paper analyzes the key challenges associated with blockchain adoption, offering insights into current limitations and areas requiring further research. Keyword- Blockchain technology, Consensus mechanisms, Smart contracts, Cryptography, Distributed Ledger, Supply Chain Management, Healthcare Data Security, Identity Management, Asset Tokenization, Blockchain Applications
Blockchain is the new age internet equivalent evolution along with the rise of artificial intelligence they both are revolutionizing a new way of safer, faster, and government free interaction for real time financial verification. This study primarily investigates the role of these new age technologies in providing better security and transparent transactions in accounting systems related to crypto notably in decentralized finance (DeFi) ecosystems currently prevailing on solana, Ethereum and Base networks. These blockchain networks currently power billions of transactions in value using great techniques to ensure immutable, verifiable audit trails while removing any 3rd party interference In 2025 major accounting firms and compliance institutions will use AI-driven analytics, anomaly detection and predictive modelling to provide much better scope and structure of audit; this will help to decrease human errors and compliance delays by an estimate of 30%. AI along with blockchain technologies will provide real time decentralized monitoring and forensic analysis via smart contracts with include the major one such as ERC-20The combination of blockchain’s immutable ledger and AIs supremely adaptive intelligence creates a new wave of real time auditing right from retrospective verification to predictive ongoing assurance. However due to delays and legislative fragmentation issues and government ethical remain major obstacles for successful development. Finally our study demonstrates that the combination of Al with blockchain marks a fundamental change toward transparent, automated, and resilient accounting ecosystems capable of maintaining confidence in an increasingly digital and decentralized global economy.
Open access
2 source records
Blockchain Technology Applications and Security
Internet of Things and AI
Innovations and Analysis in Business and Education
Ra’ed Fawzi Aburoub, Nabeel Mahdi Althabhawi, Mohamad Rizal Abd Rahman, Ammar Abbas Kadhim
This paper explores the lifecycle of a smart contract, from the stages of coding and deployment to execution and verification, in order to show that a smart contract can indeed be self-executing, transparent, and immutable. While such functionalities introduce efficiency, trust, and reliability within industries such as financial, supply chain management, and health sectors, smart contracts at the same time have a host of technical and legal challenges arising. This paper identifies key issues: critical vulnerabilities in coding, deployment on immutable blockchains, address assignment complexities, triggering mechanisms, and aspects of privacy. This study has adopted a critical analytical approach to evaluate the technical and legal aspects of smart contract formation, complemented by inductive reasoning to derive general insights and recommendations from specific cases and patterns. The study states that the apt legal framework must be provided for liability, regulatory compliance, and solutions that would be unlooked-for. It further supports hybrid models that blend automation with human oversight, superior communication protocols regarding updating an address, and the use of technologies that allow transparency with the preservation of confidentiality in a balance. The concrete ideas it offers are attempts at technology design aligned with legal frameworks by bringing developers, regulators, and stakeholders together in implementing certain solutions. It emphasizes that continuous research will hence be important to assure reliability, security, and equitability in the adoption of smart contracts, expanding possibilities for their application in an increasingly changing digital environment.
Nov 28, 2025·2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies (SISIMPACT)
Vishwa Nath Sharma, Amit Chouksey, K. Pithambar, Pankaj Agarwal · 6 authors
This research describes how we implemented machine learning technology to help detect malicious participants on the Ethereum blockchain, including fake smart contracts and honeypots. Honeypots are built to fool attackers into approaching them, as they act like vulnerable smart contracts. The work unites data science, blockchain and machine learning to help the system tell apart honeypot contracts from other types. The chosen system collects all necessary information about Ethereum contracts from the Etherscan API and sorts the data by method, transaction behavior and flow of funds. These aspects are applied to create and review an XGBoost classifier model. The model is tested in three different ways: using real data as a reference, with attackers as adversarial honeypots and by looking at its cost-benefit analysis. This unique way of tracing enables greater ease of updating, fully automated analysis and stronger accuracy while watching smart contracts live. Using machine learning in honeypot detection is an important step toward protecting decentralized applications by spotting and handling threats early in the blockchain system. With XGBoost as its foundation, the suggested honeypot detection approach achieves a total accuracy of 98.78 %. The high proportion is a result of the model's impressive accuracy in identifying honeypot and non-honeypot electronic contracts.
Abstract: The global real estate sector is currently hindered by centralized inefficiencies, opacity, and susceptibility to fraudulent activities. This paper presents a Real Estate Management System (REMS) utilizing Ethereum smart contracts and the InterPlanetary File System (IPFS) to establish a decentralized, tamper-proof registry. The system automates critical conveyancing processes, including ownership verification and funds escrow, thereby eliminating the need for traditional intermediaries such as brokers and notaries. By integrating a React.js frontend with a Node.js backend and MetaMask for non-custodial identity management, the proposed solution ensures high data integrity and operational efficiency. The study analyses the architectural implementation, security frameworks, and economic implications of transitioning from legacy databases to distributed ledger technology. Findings indicate that the proposed blockchain architecture significantly reduces transaction friction, enhances transparency, and provides a robust framework for secure property transfers. Index Terms: Blockchain, Smart Contracts, Real Estate, IPFS, Decentralization, Ethereum.
Blockchain technology has emerged as a disruptive paradigm for secure and transparent data exchange; however, it continues to face significant challenges in scalability, interoperability, and security. Fragmentation across blockchain networks restricts seamless data integration, while traditional consensus mechanisms such as Proof of Work and Proof of Stake impose high computational costs and latency. To address these limitations, this article proposes an Intractive Blockchain structure to AI that takes advantage of deep learning and light consensus mechanisms to improve performance and safety. The proposed structure introduces three main contributions: (i) a model of detection of deep learning vulnerabilities that identifies real-time intelligent contract weaknesses to reduce application failures; (ii) a lightweight consensus protocol inspired by Byzantine failure tolerance (BFT) to minimize latency and improve the transfer rate, ensuring safe authentication; and (iii) a cross -chain interoperability layer that facilitates the perfect data exchange between heterogeneous blockchain networks. Experimental assessment of TensorFlow Hyperledger tissue shows that the proposed model improves the accuracy of vulnerabilities detection by up to 96 %, reaches a 23 % reduction in latency and increases the transfer rate by 18 % compared to conventional approaches. This research highlights the potential of AI-Empowered blockchain systems for scalable, secure and interpreter applications in financial, health and public services.
AI-Driven Cybersecurity and Anomaly Detection in Blockchain While the decentralized and open-source nature of blockchain provides inherent security, vulnerabilities can still exist. AI tools and models can significantly bolster cybersecurity by identifying unusual patterns, detecting threats in real-time, and automating responses to maintain network integrity, prevent fraud, and enhance overall resilience. AI can detect fraud in real-time, predict vulnerabilities, and automate smart contracts for improved efficiency. It strengthens security by identifying unusual patterns that may indicate potential threats or breaches. AI-powered anomaly detection, utilizing techniques such as Long Short-Term Memory networks, can continuously monitor multi-sensor data streams to detect malicious data injection and sensor malfunctions in real-time, recording alerts on a blockchain ledger for incorruptibility and authenticity. Machine learning algorithms can analyze vast amounts of blockchain address and transaction data to identify patterns indicative of malicious activity, such as deviations from typical patterns or known fraud signatures. This includes detecting double-spending, transaction spamming, or unusual transaction volumes. In Decentralized Finance, AI-powered fraud detection systems, employing machine learning and graph-based algorithms, can map complex wallet connections, detect high-risk addresses, and adapt to changing scammer tactics in real-time. This capability is critical for Anti-Money Laundering audits. The ability to freeze accounts, block transfers, or notify users instantly is a key benefit of real-time AI fraud detection in crypto, as transactions are often fast and irreversible. One of the use cases our group implemented was using a modified Smart-LLaMa model to determine the reliability rating of blockchain addresses. We used large language models for detecting vulnerabilities in closed-source Ethereum smart contracts. The model was fine-tuned on a collected dataset of operational codes to adapt to the semantics of compiled smart contracts. The method allows for assessing the reliability of addresses based on the technical content of contracts, eliminating dependence on the source code, which is an excellent tool for enhancing the security of decentralized applications amidst the growing number of attacks on blockchain. www.srcmeetings.com17International Conference on Artificial Intelligence and Cybersecurity (ICAIC 2025)November 27-28, 2025 (Virtual)Conference Proceedings
Abstract Blockchain technology has become a transformative solution for secure and transparent digital ecosystems. This paper explores how decentralization, cryptographic hashing, distributed consensus, and immutable ledger architecture contribute to advanced data protection in the IT industry. The study integrates findings from existing literature, evaluates blockchain’s practical applications in sectors including finance, healthcare, supply chain, and governance, and examines a proposed multi-layer blockchain framework. The research highlights blockchain’s advantages in enhancing confidentiality, integrity, availability, and auditability, while identifying its limitations such as scalability, regulatory constraints, and environmental impact. Future scope emphasizes integration with AI, IoT, Web 3.0, quantum-resistant models, and cross-chain interoperability. Overall, the study concludes that blockchain is a critical technology for advancing trust-driven IT infrastructures. Keywords Blockchain, Data Security, Transparency, Decentralization, Smart Contracts, IT Industry
S Thillainayagi, Paolo Pavan, S Shashank, L V Preetham · 5 authors
Bitcoin is known for its high volatility and speculative trading behavior.Predicting Bitcoin prices is valuable for investors, traders, and financial analysts.The study uses historical price data, technical indicators, and/or sentiment analysis.Machine learning and statistical models like ARIMA, Linear Regression, and LSTM are applied.Deep learning models, especially LSTM, show better accuracy in capturing time-series patterns
In this project, our goal is to employ machinelearning algorithms and big data analysis for forecasting and examining cryptocurrency prices. Cryptocurrencies are digital assets created to function as a means of exchange, utilizing cryptography to ensure transaction security and govern the generation of additional currency units. A key characteristic of cryptocurrencies is their decentralized nature, as they are not issued by any central authority, theoretically making them resistant to government interference or manipulation. Decentralized cryptocurrencies are generated collectively by the entire cryptocurrency system at a predefined rate established during the system’s creation, which is publicly accessible. Most cryptocurrencies utilize blockchain technology in their operations. A blockchain is an ever-expanding ledger of records, referred to as blocks, that are interconnected and safeguarded through cryptographic methods. Each block typically contains a hash pointer linking it to a prior block, a timestamp, and transaction data. By design, blockchains are highly resistant to data tampering. Blockchains serve as open, distributed ledgers capable of efficiently and verifiably recording transactions between two parties in a permanent manner.
Blockchain technology has changed forever the concept of decentralized systems, which provide a secure, unchangeable (immutable), and distributed (shared) ledger. However, many organizations are concerned about how secure the foundations of Blockchain are because of exposed Cryptographic Vulnerabilities and Emerging Threats to Cryptography Used in Blockchain Technology. This paper analyzes these two issues. The first issue is an analysis of the different frameworks of existing blockchain technologies that are established on Cryptographic bases. The second issue addresses and categorizes the Security Threats Facing Blockchain Technology Today and into the Future. This research paper discusses Consensus Mechanisms, Encryption Algorithms, and Smart Contracts that are utilized in several of the more well-known Blockchain Technology Frameworks and notes various vulnerabilities associated with them, including Sybil Attacks, Quantum Computing Based Exploits, and Exploits associated with DeFi (Decentralized Finance). This research also found that a significant gap exists today regarding Threats to Blockchain Technology Frameworks and how those Threats may be leveraged to exploit the Vulnerability. Additionally, a Critical Need exists for the use of Proactive Threat Intelligence Processes, as well as Responsible Cryptography Practices, within the Ecosystems of Blockchain Technology.
Smart contracts automate blockchain transactions but are vulnerable to reentrancy attacks, where an attacker repeatedly calls a function before the contract updates its state, stealing funds. A well-known case is the 2016 DAO exploit, which caused a loss of $60 million.This study investigates methods to protect contracts from such attacks through a literature review, a case study comparing a vulnerable and a fixed contract, and evaluation of analysis tools (Mythril, Slither, Securify, Sereum, BlockWatchdog).The expected results include identifying effective coding patterns (like checks-effects-interactions and reentrancy guards), assessing tool accuracy, and providing secure development guidelines. All experiments will be conducted safely on test networks.
Blockchain technology has recently undergone substantial investigation into the prospect of integrating it with several service sectors, having originally been designed for the Peer-to-Peer cryptocurrency network, Bitcoin Database security could be an expensive and time-consuming operation. When discussing a legally binding contract, the phrase "automated transaction protocol that, executes the terms of the agreement" is used. The Internet of Things (IoT), big data artificial intelligence technologies, and blockchain technology into the supply chain may help solve the transparency and traceability issue stated in the literature.
C. Sathiyamoorthy, Mohammad Musa Al-Momani, Suseendran Surendran, J Adilakshmi · 6 authors
The blockchain technology is now widely accepted as an innovation in various areas such as finance, healthcare, logistics, and supply chain domain. Nevertheless, the interconnection of diverse blockchains is still an existing problem, which contributes to the impedance of seamless data and asset exchange between multiple blockchains. This paper proposes a new paradigm of the Protection of Communication Between Chains by Intelligent Translation Protocols system using Artificial Intelligence to achieve secure, efficient and scalable cross-chain interaction. Utilizing smart translation algorithms based on machine learning the system adapts to the underlying consensus rules, data structures and messaging patterns on multiple blockchains without any security or data integrity implications. This approach focuses on adaptive learning for multiple evolving chain standards, real-time verification to stop forged translations, and smart routing to maximize cross-chain transaction paths. Experimental results confirm that the AI-based interoperability tier achieves lower latency, higher translation accuracy, and less computation costs than the bridge-based access layer. They also include secure communication and cryptographic protection for the data in transit inter chaining. The findings of this research further the technological frontier of blockchain interoperability, while also presenting a foundational solution for developing the next-generation of decentralized applications (dApps) that can run across multiple blockchains. We conclude that intelligence protocol translation is a promising approach to secure, scalable and robust integration of blockchains with the Web3.
The Internet of Things (IoT) generates massive volumes of patient and healthcare data every day. Providing the required accuracy for data classification, processing time, and analyzing the vast volumes of data from IoT devices and sensors are said to be the main challenges in IoT. Cloud computing is widely utilized as the foundation for the technologies required to secure healthcare. The healthcare industry has the most promise for blockchain technology since it can be used to integrate fragmented systems, the standard of electronic medical records should be raised, and take a more patient-centric approach to healthcare systems. The objective is to protect medical data, enable patients to use it to support their medical care, and provide reliable consent protocols for data exchange between various institutions and apps. Provide a blockchain-based architecture that verifies user identity using the Secure Hash Algorithm (SHA256) and Proof of Stake (POS) cryptography consensus technique to guarantee EHR sharing across many electronic healthcare systems.In this study, we assessed the performance of our proposed architecture using several metrics, and found that blockchain is a reliable security solution for the upcoming IoT network.
L. Bharathi, Kabita Thaoroijam, Sri Raman Kothuri, P Joel Josephson · 6 authors
The blistering development of the Industrial Internet of Things (IIoT) has brought serious issues to the maintenance of large-scale sensor data security and processing with low latency and scalability. Conventional central and edge-only solutions are either limited in the number of trust bottlenecks or restricted in the detection accuracy, thus a hybrid solution is required. This study establishes a Blockchain-AI composite model, where federated anomaly detection and a superior Delegated Proof-of-Stake (eDPoS) consensus mechanism system are used to efficiently and safely process big data on IIoT scenarios. This methodology gives the analytical models that are vital in throughput, latency and the likelihood of hostile takeover. Researcher experimented the Indian IIoT and Blockchain Synthetic Dataset which includes DPoS information under a wide range of conditions, including safe and malicious adversarial stake attacks. It was found to significantly (up to 20 percent) improve throughput over vanilla DPoS, but latency is minimized under medium-delay networks and can anomaly detect (AUC [?] 0.93) with errors nearly equal to centralized (under 5 percent) baselines. Security analysis provides resistance to stake-boost attacks and optimization of storage using lightweight anchoring. This paper makes the framework a scalable and secure IIoT deployment solution, between blockchain consensus and AI-driven anomaly detection.
Pradeep Nazareth, Sathyaprakash T, Sharan S Shetty, Shrihith S Poojary
Internet of Things (IoT) devices are constrained by limited storage and processing capabilities, creating open entry points for cyber threats. These constraints limits the establishment of robust security using conventional, centralized methods. Blockchain technology presents a promising solution by offering a decentralized, secure, and tamper-proof method for storing data. This paper examines modern research directions for implementing blockchain to enhance the security and quality of IoT systems. The core strength of blockchain lies in its ability to protect information from corruption and unauthorized access through encrypted, distributed ledgers. However, a significant challenge remains where many standard blockchain implementations are computationally expensive and demand high processing power, making them unsuitable for lightweight IoT devices. Therefore, the primary issue is not the applicability of blockchain’s security principles to IoT, but rather the prohibitive cost and resource requirements for many practical use cases. This research focuses on overcoming these barriers to enable efficient, featherweight blockchain solutions for the IoT landscape.