This review article examines the state of blockchain-enabled identity management in Internet of Things (IoT) networks, focusing on decentralized and secure mechanisms for device identification, authentication, and access control. Traditional centralized identity systems face limitations such as single points of failure, scalability bottlenecks, and vulnerability to breaches. We systematically survey recent literature on blockchain-based frameworks applied to IoT, categorizing approaches by blockchain platform, identity credential models, consensus mechanisms, and smart contract implementations. The analysis highlights key performance metrics such as system latency, throughput, resource overhead, and energy consumption, and compares existing prototypes deployed across diverse IoT scenarios. We assess the security and privacy implications, including resistance to spoofing, Sybil attacks, unauthorized access, data tampering, and insider threats. Additionally, the review identifies open research challenges such as managing identity lifecycle in constrained devices, achieving interoperability across heterogeneous networks, balancing decentralization with scalability, and integrating with emerging technologies like edge computing and zero-knowledge proofs. Finally, we offer recommendations for future research directions and practical deployment strategies to advance blockchain-based identity solutions in IoT ecosystems. Our comprehensive synthesis aims to guide researchers and practitioners in developing robust, scalable, and trustworthy identity frameworks using blockchain for the evolving IoT landscape.
Hari Suresh Babu Gummadi, Mohan Sankaran, R. D. Shelke, Venkata Siva Kumar Tankani · 6 authors
In the advancing domain of drone systems, cybersecurity is a critical issue owing to the rising threat of advanced cyberattacks. This study presents an innovative framework for drone cybersecurity that utilizes the integration of deep learning and blockchain technologies to efficiently detect and prevent malicious intrusions. The proposed architecture consists of four main stages: data normalization, feature selection utilizing the greylag goose optimization algorithm (GLGOA), long short-term memory (LSTM)-based cyberattack detection, and blockchain-based data validation. Initially, raw drone sensors and network data are standardized using normalization techniques to ensure consistency and minimize noise. GLGOA is utilized to extract the most pertinent features, thereby improving detection efficiency and reducing computational burden. The enhanced feature set is input into an LSTM model designed to capture temporal dependencies and classify potential cyber threats. Ultimately, blockchain integration guarantees the immutable recording of drone interactions and improves overall data security and reliability. Comprehensive experimental assessment illustrates the superiority of the proposed GLGOA-LSTM-BC model compared to traditional methods such as SVM, random forest, CNN, and GRU regarding -score. The proposed method demonstrates a 97.8% accuracy and a 97.5% f1-score, establishing it as a robust and reliable solution for real-time cyberattack detection in drone environments. The amalgamation of bio-inspired optimization, deep learning, and distributed ledger technologies facilitates the development of secure, intelligent, and autonomous drone systems within contemporary digital infrastructure.
D. Manju, Poorvi Gandham, A. V. B. Paavan, Kruthik Sai Garipally · 6 authors
The nature of cyber threats is advancing at a phenomenal pace and thus advanced solutions based on artificial intelligence and blockchain technology are needed. Security systems in existence have scalability, real-time threat detection and data integrity problems. The contribution of this research is a hybrid AI Blockchain framework with a higher level of threat detection and data security. Specialized algorithms, that use artificial intelligence, search through cyber threats, while the block chain makes it possible to ensure the tamper-proof storing of all the data. Such methods include usage of smart contracts, user entity and behavior analysis and anomaly detection techniques. The merger is useful in improving detection accuracy, response time, data integrity. The model is proven to be effective for real-world dataset experiments and detection accuracy is improved with fewer false positives. At the same time, the system includes a blockchain emulator, Ganache, which allows to set up a local Ethereum blockchain environment for connecting to Web3.
The rapid integration of blockchain, cryptocurrency, and Web3 technologies into digital banks and fintech operations has created an integrated environment blending traditional financial systems with decentralised elements. This paper introduces the CryptoNeo Threat Modelling Framework (CNTMF), a proposed framework designed to address the risks in these ecosystems, such as oracle manipulation and cross-chain exploits. CNTMF represents a proposed extension of established methodologies like STRIDE, OWASP Top 10, NIST frameworks, LINDDUN, and PASTA, while incorporating tailored components including Hybrid Layer Analysis, the CRYPTOQ mnemonic for cryptocurrency-specific risks, and an AI-Augmented Feedback Loop. Drawing on real-world data from 2025 incidents, CNTMF supports data-driven mitigation to reduce losses, which totalled approximately $2.47 billion in the first half of 2025 across 344 security events (CertiK via GlobeNewswire, 2025; Infosecurity Magazine, 2025). Its phases guide asset mapping, risk profiling, prioritisation, mitigation, and iterative feedback. This supports security against evolving risks like state-sponsored attacks.
Ketav Shah, Aparna Kumari, Bhavya Solanki, S. N. Saud · 5 authors
This research presents ZTSec-FedSDN, a privacy-preserving federated framework for SDN attack detection that integrates zero-trust blockchain architecture with 6G terahertz networks. The framework enables collaborative training of Deep Neural Network models across distributed clients while maintaining data privacy and leveraging the high-speed capabilities of 6G terahertz communication. Using the SDNFlow dataset containing diverse network traffic patterns and attack types, we implement four federated optimization strategies: Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Adam (FedAdam), and Federated Adagrad (FedAdagrad). The zero-trust blockchain layer provides an immutable ledger for recording and verifying model updates, ensuring transparency and trustworthiness in the federated learning process. The integration with 6G terahertz networks enables ultra-low latency communication between federated clients, crucial for real-time intrusion detection in SDN environments. Multiple clients collaboratively train the shared DNN model on local traffic data without exposing sensitive information, preserving data privacy while benefiting from collective intelligence. This work systematically evaluates the performance of different federated learning algorithms by comparing model accuracy, convergence speed, robustness, and efficacy in multi-class network attack classification. The results provide comprehensive insights into each optimizer’s suitability for secure, transparent, and trustworthy intrusion detection systems in next-generation SDN environments powered by 6G terahertz networks.
Detecting similar data is crucial for optimizing file storage and transmission in HTTP protocols and Content Delivery Networks. Traditional MinHash methods encounter significant efficiency challenges due to their reliance on K-shingle structures, resulting in high computational costs and storage requirements. Additionally, these methods expose privacy risks in cloud environments, where sensitive information can be inferred from MinHash signatures. To address both efficiency and security concerns, we propose Horse-MinHash, which integrates a fast, content-defined feature extraction scheme with a non-interactive zero-knowledge proof-based similarity estimation method. Our approach significantly enhances computational efficiency while ensuring robust privacy protection by preventing plaintext exposure. Experimental results demonstrate that Horse-MinHash achieves lower mean squared error in Jaccard similarity estimation and reduces time overhead for average block sizes of 16 KB or more, outperforming state-of-the-art methods.
V. Hemamalini, R. Jagathrajah, K. Annapurani Panaiyappan
The Domain Name System (DNS) is an indispensable part of the internet’s infrastructure, allowing humans to enter human readable domain names into a computer and the computer will translate it to machine readable IP addresses. However, both the traditional DNS systems have several security, privacy, and scalability issues because of the centralized nature. This paper describes the design and implementation of a Web3 decentralized DNS system on top of the blockchain technology based on Ethereum smart contracts. The Web3 DNS system distributes domain registration, resolution and management in a way that is transparent and unbreakably secured, scalable and insanely fast. It’s a system with integrated criticals such as Proof of Request (PoR) for logging all actions and rate limiting to stop abusing the system. In this paper we explore the root components of the Web3 DNS system such as: domain management via a smart contract, PoR logging and rate limiting. This Web3 DNS is tested with the performance tests and compared to the traditional DNS systems for the handling of domain related transactions with improved security and decentralization. Furthermore, blockchain usage in the system leads to the immutable and transparent record of all actions, decreasing the danger of DNS hijacking and other evil activities. This work tries to bring a robust, transparent and censorship resistant alternative to existing DNS infrastructures. Future work will attempt to scale the system and enhance the capability of interoperating with existing, legacy DNS infrastructure.
This chapter explores the transformative potential of generative artificial intelligence (AI) in combating financial fraud, redefining traditional detection systems. It examines generative AI's capabilities in anomaly detection, behavioral modeling, and predictive analytics, addressing fraud complexities from technologies like cryptocurrencies, decentralized finance (DeFi), and AI-driven scams. The integration of generative AI with blockchain enhances transparency, scalability, and proactive prevention. Real-world case studies highlight its effectiveness against credit card fraud, deepfake impersonations, and synthetic identity fraud. Ethical and operational concerns, including AI bias, privacy, and security, are discussed alongside strategies for ethical governance and collaboration. Generative AI is positioned as a key tool for building resilient and trustworthy financial ecosystems to counter current and emerging fraud threats.
ABSTRACT The rise of cyber threats has underscored the critical need for robust intrusion detection systems (IDS). While traditional approaches, including statistical, knowledge‐based, and AI‐driven methods, have been pivotal, they often face limitations such as data privacy concerns, scalability challenges, and low detection accuracy on unfamiliar threats. This paper addresses these issues by adopting a federated learning (FL) paradigm for collaborative intrusion detection, allowing data to remain local and enhancing privacy protection. The proposed solution integrates advanced encryption techniques and differential privacy to safeguard confidentiality while ensuring system scalability and adaptability. By introducing a robust separation of agents' roles and leveraging FL's decentralized architecture, the system overcomes the limitations of centralized learning, including single points of failure and communication overhead. Experimental results validate the proposed architecture, demonstrating significant improvements in performance and offering a promising direction for modern network security. This work not only highlights the potential of FL‐based IDS but also explores the integration of distributed ledger technologies to further enhance trust and security. These findings contribute to the growing field of privacy‐preserving computing and lay the groundwork for future innovations in scalable, secure, and efficient intrusion detection systems.
M. J. Jeyasheela Rakkini, R. Mohanram, G. Dheepak, S. Subha · 6 authors
In the evolving landscape of cybersecurity, traditional intrusion detection systems (IDS) face significant challenges in handling high-dimensional data, real-time threat detection, and maintaining data privacy. To address these limitations, this paper proposes a novel TransformerBased Intrusion Detection System (IDS) integrated within a Deep Federated Learning (DFL) framework, aiming to achieve robust cybersecurity with strong privacy preservation. The proposed model leverages the selfattention mechanisms of transformers to effectively capture complex temporal and spatial dependencies inherent in network traffic, enabling highly accurate anomaly and attack detection. Meanwhile, federated learning ensures that sensitive data remains decentralized, minimizing privacy risks while collaboratively improving the global IDS model across distributed nodes. The system is trained and evaluated on multiple benchmark cybersecurity datasets, demonstrating superior performance compared to traditional convolutional and recurrent architectures. Experimental results reveal substantial improvements in detection accuracy, reduced false positive rates, and enhanced adaptability to emerging cyber threats. This work presents a scalable and privacy-preserving paradigm, opening new possibilities for next-generation IDS solutions in decentralized and sensitive environments such as healthcare, finance, and smart grids.
With the rapid development of the Ethereum platform, phishing fraud has become increasingly rampant, posing significant security risks to both users and the platform. However, existing phishing fraud detection methods are manually designed, requiring substantial human effort, and are unable to adapt to diverse detection scenarios. In this article, we propose phishing detection on Ethereum via graph neural architecture search of transaction subgraph (PETS-GNAS). The phishing detection problem on Ethereum is transformed into a graph classification task, where accounts and transactions are represented as nodes and edges, respectively. Specifically, we acquire account labels and their corresponding transaction information from credible sources and then extract transaction subgraphs centered on labeled accounts as datasets. Subsequently, we introduce a mapping mechanism to extend these transaction subgraphs into corresponding temporal transaction subgraph (TTSG), encoding transaction attributes during the TTSG construction process. Then, graph neural architecture search (GNAS) strategy that incorporates early stopping and L2 regularization is proposed to enhance the feasibility and accuracy of Ethereum phishing detection by avoiding redundant parameters and complex architectures. Extensive experimental results demonstrate that PETS-GNAS achieves strong performance in phishing detection tasks, enabling early and accurate identification of phishing accounts.
Privacy remains a significant challenge in public blockchain ecosystems. Mainstream add-on privacy solutions, such as Stealth Address Protocols (SAPs) and Zero-Knowledge Proof (ZKP)-based mixers, have recently attracted considerable attention. However, existing SAPs offer only ephemeral anonymity for users' transaction data, and their implementation and evaluation within the highly concurrent Unspent Transaction Output (UTXO) model remain largely unexplored. ZKP-based mixers are limited to native coin transfers with fixed denominations and require additional security assumptions, employing out-of-band encrypted channels to transmit notes. To overcome these challenges, we unify the core principles underlying both SAPs and ZKP mixers and formally introduce StealthHub, a UTXObased SAP. Compared with the widely adopted dual-key-based Umbra protocol prevalent on Ethereum Virtual Machine (EVM)-compatible chains, StealthHub reduces computational overhead for the prepare and scan announcements stages by over 71% and 32%, respectively. Furthermore, by leveraging Merkle Mountain Range (MMR) commitments and off-chain batch aggregation, our StealthHub implementation lowers deposit and shielded transfer transaction costs to approximately 76% of those for a standard transfer, substantially improving practical usability.
The escalating complexity and frequency of malware attacks pose a significant challenge to conventional cybersecurity frameworks, particularly in scenarios demanding high data privacy and cross-organizational threat intelligence sharing. Traditional centralized machine learning models for malware detection often rely on aggregating data in a central server, thereby increasing the risk of data breaches and limiting the deployment of models in privacy-sensitive environments such as healthcare, finance, and critical infrastructure. To address these limitations, this study explores an integrated approach that combines Federated Learning (FL) with Explainable Artificial Intelligence (XAI) for enhancing malware detection while preserving user privacy and system confidentiality. Federated learning enables the collaborative training of robust malware classifiers across multiple decentralized nodes without sharing raw data, thus maintaining local data sovereignty and complying with data protection regulations. The proposed framework incorporates deep learning architectures such as convolutional neural networks (CNNs) trained in a federated environment using feature vectors extracted from malicious binaries and behavior logs. To ensure transparency and trust in model predictions, explainable AI techniques specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are integrated, providing actionable insights into the model’s decision-making process. This study also presents a comprehensive evaluation using a benchmark malware dataset distributed across simulated client environments, measuring detection accuracy, communication overhead, privacy leakage, and interpretability performance. Results demonstrate that the FL-XAI approach achieves detection rates comparable to centralized models while ensuring data confidentiality and interpretability. The research contributes to the evolving field of privacy-preserving threat intelligence by offering a scalable and explainable framework suitable for real-time cybersecurity applications.
Yu Zhang, Yafei Li, Jufang Zhang, Claudio J. Tessone
When analyzing the balance distribution of Bitcoin users, we found that it follows a log-normal pattern based on a rigorous Uniformly-Most-Powerful-Unbiased test. Drawing parallels from the successful application of Gibrat’s law in explaining city size and word frequency distributions, we tested whether a similar principle could account for the log-normal distribution in Bitcoin balances. However, our calculations revealed that the exponent parameters in both the drift and variance terms deviate slightly from 1 when applying Geometric-Brownian-Motion on the Bitcoin balance, which means that Bitcoin users’ balance distribution cannot be explained only by the proportional growth rule alone. During this exploration, Bitcoin users’ behaviors are also investigated. We discovered an intriguing phenomenon: Bitcoin users tend to fall into two distinct categories based on their transaction behavior, which we refer to as “poor” and “wealthy” users. Poor users who initially purchase only a small amount of Bitcoin tend to buy more Bitcoins first and then sell out all their holdings over time. The certainty of selling all their coins is higher and higher with time. In contrast, wealthy users who acquire a large amount of Bitcoin from the start tend to sell off their holdings over time. The speed at which they sell their Bitcoins is lower and lower over time. The wealthier the user, the larger the proportion of their balance and the higher the certainty they tend to sell their holdings. This research provided a new perspective to explore Bitcoin users’ behaviors which may apply to other finance markets.
Cryptocurrency-related crimes are on the rise and have a wide-ranging impact across various areas. To effectively combat and prevent such crimes, cryptocurrency forensics, which relies on blockchain analysis, is essential. Despite advancements in Bitcoin de-anonymization techniques, several challenges persist. The absence of authentic data labels introduces uncertainty in de-anonymization results, especially in the context of address clustering. This issue is further compounded by the development of privacy-enhancing technologies that obscure address linkages, thus undermining the reliability of outcomes as forensic evidence. To address these limitations, this study focuses on Bitcoin blockchain analysis and the improvement of address clustering. Specifically, the work presents an enhanced simulation model designed to accurately simulate real Bitcoin transactions, offering a stable platform for evaluating address clustering algorithms that utilize transaction details, thereby facilitating the assessment of the admissibility of clustering results. Meanwhile, we introduce a new heuristic algorithm aimed at identifying one-time change addresses, with experimental results demonstrating that it achieves more precise clustering outcomes than existing heuristic methods. Furthermore, our blockchain analysis reveals overarching patterns and recent changes in the Bitcoin blockchain, particularly following the introduction of the BRC-20 token.
Ensuring secure and efficient authentication in Vehicular Ad Hoc Networks (VANETs) is vital for real-time communication and network resilience. However, traditional authentication mechanisms, such as Elliptic Curve Cryptography (ECC) and Public Key Infrastructure (PKI), face significant challenges, including high computational overhead, complex certificate revocation, and vulnerability to quantum attacks. To overcome these limitations, we propose a lattice-based authentication protocol that integrates post-quantum cryptography (PQC), zero-knowledge proofs (ZKPs), and fog computing for secure Vehicle-to-Roadside (V2R) communication. Our protocol offers quantum resistance, decentralized authentication, and dynamic pseudonym updates, enhancing both security and privacy in VANETs. Performance evaluations demonstrate that our approach achieves lower message delay (0.8), reduced packet loss ratio (0.6), minimal communication overhead (0.7), and the fastest authentication delay (0.5) compared to ECC and Physically Unclonable Function (PUF)-based methods. Additionally, formal security analysis confirms that our scheme effectively mitigates impersonation, replay, tracking, and quantum attacks, ensuring a scalable and future-proof authentication mechanism for next-generation VANETs.
The rapid adoption of Internet of Things (IoT) devices has significantly increased cybersecurity risks, making them vulnerable to anomalies, attacks, and unauthorized access. Traditional security mechanisms struggle to handle the massive data flow, real-time processing requirements, and evolving cyber threats in IoT networks. This paper presents an integrated approach using Deep Neural Networks and Blockchain technology (DNNs-BCT) to enhance anomaly detection and prevention in IoT environments. Our proposed framework employs DNNs for intelligent anomaly detection, leveraging multi-layer feature extraction and adaptive learning mechanisms. The DNN model is trained on IoT traffic datasets to classify network behavior as normal or anomalous, effectively detecting threats such as Distributed Denial of Service (DDoS) attacks, malware injections, and insider threats. Unlike traditional rule-based intrusion detection systems (IDS), the DNN continuously learns and adapts to new attack patterns, improving detection accuracy and false-positive reduction. This study integrates Blockchain technology into the IoT ecosystem to ensure data integrity, transparency, and decentralized security. Each IoT device logs its activity onto a private blockchain network, preventing data tampering, unauthorized access, and single points of failure. The blockchain employs smart contracts for automated threat response, instantly mitigating malicious activity without human intervention. This distributed ledger approach enhances trust, authentication, and secure communication across IoT devices. The synergy between DNN-based anomaly detection and Blockchain-based security provides a robust, scalable, and adaptive solution for real-time cybersecurity threats in IoT networks. With a low false-positive rate of 15.42% and a strong detection accuracy of 99.18%, the proposed model successfully identifies malicious activity, including malware injections and Distributed Denial of Service (DDoS) assaults. Blockchain technology replaces single points of failure and forbids illegal changes by providing data integrity, openness, and decentralizing powers. Furthermore, smart contracts allow autonomous, real-time attack responses, enhancing reaction time efficiency (95.25%) and general system scalability (94.96%).
Mohamed Mahmoud Alkabir, Mohamed Taher R Nashnosh, Tarek Ayad H Shaladi
6G wireless networks introduce revolutionary features beyond 5G by providing human-focused services and extended IoT battery life and holographic telepresence and tactile Internet capabilities. 6G enables terahertz (THz) spectrum together with pervasive AI and intelligent spectrum management to deliver unmatched reliability and complete 3D coverage. The AI-native architecture and distributed intelligence of 6G networks create new security risks because they make systems more vulnerable to adversarial attacks and scalability limitations. A Swarm Intelligence-Driven Collaborative Intrusion Detection System (CIDS) for 6G-IoT networks addresses security challenges through the combination of ant colony optimization (ACO) with Edge Blockchain for decentralized adaptive threat detection. The framework includes three main components: (1) autonomous ant agents spread anomaly signatures through pheromone trails and (2) lightweight Hyperledger Fabric provides tamper-proof logging of threat intelligence and (3) smart contracts execute mitigation actions based on dynamic trust threshold values. The 50-node UAV-ground sensor testbed results show that the system detects DDoS attacks with 98.7% accuracy while achieving 47% lower latency than federated learning baselines and 73% storage efficiency through IPFS-backed hashing. The system decreases false positives by 62% while maintaining blockchain transaction rates of 420 per second at large scales. The proposed framework swarm intelligence with distributed ledger technology solves essential 6G security challenges regarding autonomy and scalability and resilience which enables trustworthy AI-driven networks.
S. M. Eswara Moorthi, G. Ravishankar, D. David Neels Ponkumar
Decentralized public ledger blockchain secures transactions across untrusted network nodes. Bitcoin systems rely on it to safeguard and decentralize transaction records, attracting attention. Over the last decade, blockchain has garnered interest from numerous businesses because of its potential to transform multiple areas, including cybersecurity. This research area is new, and blockchain's cybersecurity efficacy has to be addressed. This qualitative study examines blockchain-based security applications and their suitability in the current cybersecurity environment. A single point of failure may expose a centralized administration and validation system to malware, Distributed Denial of Services (DDoS), and Denial of Services (DoS) assaults. Blockchain technology creates secure, private decentralized networks without third-party control. Blockchain allows irreversible and verifiable storage of current and historical data in a sealed ledger distributed throughout the network. Blockchain technology distributes encrypted data throughout the network, improving data security and privacy. A decentralized e-government peer-to-peer system employing blockchain technology to secure and anonymize data and boost public sector credibility is proposed in this research. The suggested system's security and privacy consequences are theoretically and qualitatively analyzed, along with a prototype.