As cryptocurrency transactions continue to grow, detecting scams within transaction records remains a critical challenge. These transactions can be represented as dynamic graphs, where Neural Network Convolution (NNConv) models are widely used for detection. However, NNConv models suffer from model decay due to evolving transaction patterns, the introduction of new users, and the emergence of adversarial techniques designed to evade detection. To address this issue, we propose an automated, periodic hyperparameter optimization method based on proximal policy optimization (PPO), a reinforcement learning algorithm designed for dynamic environments. By leveraging PPO’s stable policy updates and efficient exploration strategies, our approach continuously refines hyperparameters to sustain model performance without frequent retraining. We evaluate the proposed method on a large-scale cryptocurrency transaction dataset containing 2,973,489 nodes and 13,551,303 edges. The results demonstrate that our method achieves an F1 score of 0.9478, outperforming existing graph-based approaches. These findings validate the effectiveness of PPO-based optimization in mitigating model decay and ensuring robust cryptocurrency scam detection.
Detecting fraudulent activities such as Ponzi schemes within smart contract transactions is a critical challenge in decentralized finance. Existing methods often fail to capture the heterogeneous, multi-faceted nature of blockchain data, and many graph-based models overlook the contextual patterns that are vital for effective anomaly detection. In this paper, we propose MVCG-SPS, a Multi-View Contrastive Graph Neural Network designed to address these limitations. Our approach incorporates three key innovations: (1) Meta-Path-Based View Construction, which constructs multiple views of the data using meta-paths to capture different semantic relationships; (2) Reinforcement-Learning-Driven Multi-View Aggregation, which adaptively combines features from multiple views by optimizing aggregation weights through reinforcement learning; and (3) Multi-Scale Contrastive Learning, which aligns embeddings both within and across views to enhance representation robustness and improve anomaly detection performance. By leveraging a multi-view strategy, MVCG-SPS effectively integrates diverse perspectives to detect complex fraudulent behaviors in blockchain ecosystems. Extensive experiments on real-world Ethereum datasets demonstrated that MVCG-SPS consistently outperformed state-of-the-art baselines across multiple metrics, including F1 Score, AUPRC, and Rec@K. Our work provides a new direction for multi-view graph-based anomaly detection and offers valuable insights for improving security in decentralized financial systems.
Cryptocurrencies have received a lot of attention from central banks, investors, and governments worldwide. The insufficiency of any method of political guideline and their market is far from "effective", so they want novel regulation methods shortly. From an econometric perspective, the technique underlying the growth of the cryptocurrencies' volatility was observed to demonstrate similarities and differences with other economic time series, e.g., foreign exchange yields. Accurate prediction of cryptocurrency price fluctuations is significant for effectual portfolio management and improves economic models by identifying potential risks and attacks. With the growing use of AI in various fields, its application in financial markets, especially cryptocurrencies and stocks, is an emerging research area. This study presents an Empirical Evaluation of Fuzzy Bidirectional Long Short-Term Memory with a Soft Computing-based Decision-Making Model for Predicting Volatility of Cryptocurrencies (FBLSTMSC-DMPVC) technique. The primary focus of the FBLSTMSC-DMPVC technique is to present a robust and intelligent framework for an advanced decision-making model to predict cryptocurrency volatility. Initially, the presented FBLSTMSC-DMPVC method performs the data preprocessing process using Z-score normalization to ensure all features are standardized and scaled. Furthermore, the fuzzy bidirectional long short-term memory (FBLSTM) method predicts cryptocurrency volatility. To enhance the hyperparameters of the FBLSTM technique, the improved carnivorous plant algorithm (ICPA) is employed. A wide range of simulation is accomplished to ensure the impact of the FBLSTMSC-DMPVC technique. The FBLSTMSC-DMPVC technique portrayed a superior MAPE value of 0.7939 for BTC, 0.8633 for ETH, 0.6187 for LTC, and 0.6667 for XRP, demonstrating its performance across various cryptocurrencies.
In the era of big data, information security and privacy protection have become important issues facing today's society. This study proposes a distributed network security architecture based on blockchain to enhance the security of information privacy protection. The proposed architecture consists of three primary levels: equipment layer, network service layer, and application layer. It also integrates smart contracts. In addition, this study also proposes a vulnerability detection method based on improved tree convolutional neural networks. The incorporation of a "continuous binary tree" approach effectively addresses the limitation inherent to conventional tree convolution, wherein the number of nodes is fixed. This refinement enables a more effective capture of the hierarchical structure and semantic nuances inherent to smart contract code. The experiment used multiple datasets, each containing multiple IoT attack types and smart contract vulnerability code snippets. These datasets were evaluated based on a set of criteria, including but not limited to accuracy, recall, F1 scores, gas costs, and execution delays. Experiments have shown that the proposed method performs well in accuracy, precision, recall, and F1 scores compared to existing state-of-the-art methods, with an accuracy range of 89.62% to 98.36%, significantly better than Oyente (about 75%) and Securify (about 85%). Specifically, the proposed method achieved 96.14% accuracy in detecting reentrant attacks, compared to 78% for Oyente and 82% for Securify. The findings indicate that the architectural design exerts a substantial influence on enhancing network security performance, thereby ensuring the stability of the system by effectively mitigating the variability in response time.
In recent years, the proliferation of malware has reached unprecedented levels, leading to escalating cybercrime costs. Signatures extracted by static analysis of files have been widely adopted for malware detection: vendors maintain databases of known malware signatures that are shared with registered users. The recent literature has proposed the use of private and consortium (thus, permissioned) blockchains for spreading signatures among blockchain users. These approaches require controlled access to enhance trust and accountability but restrict the widespread sharing of up-to-date signatures because users must be registered. In this paper, we present a novel technique that leverages a public blockchain to enable the massive dissemination of malware signatures among any users since a public blockchain is permissionless. On the other hand, the use of a public blockchain introduces new challenges related to security and data privacy, which our solution solves. The main benefit and outcome of our solution is that any users can securely access and verify malware signatures facilitating real-time detection of malicious files. We implemented our solution in Ethereum and exploited a smart contract written in Solidity to demonstrate that our approach is highly cost-effective.
The fast development and growth of blockchain technology and cryptocurrencies, but most importantly, the fast diffusion of Ethereum, opened new chances for financial innovation but aggravated the risks of illegal activities such as money laundering. This paper discusses using machine learning techniques to detect illegal transactions over the Ethereum network. The dataset used is from Kaggle and includes a record of transaction features between Ethereum accounts; it has a high degree of class imbalance. Three machine learning models were used to classify transaction legality: Logistic Regression, Random Forest, and Extreme Gradient Boosting; this is referred to as XGBoost. Class balancing and data preprocessing are ways to improve model performance. The evaluation metrics were chosen as Accuracy and Area Under the Receiver Operating Characteristic Curve (ROC AUC). Experimental results show that the best performance of the XGBoost model was 98.52% in accuracy, while Random Forest was the best on ROC AUC, showing very strong classification capabilities. This work has shown the potentiality of machine learning in the improvement of blockchain security and provided useful lessons that might be applied to the development of scalable AML systems.
Mohammed Shalan, Md Rakibul Hasan, Yan Bai, Juan Li
The increasing adoption of smart home devices has raised significant concerns regarding privacy, security, and vulnerability to cyber threats. This study addresses these challenges by presenting a federated learning framework enhanced with blockchain technology to detect intrusions in smart home environments. The proposed approach combines knowledge distillation and transfer learning to support heterogeneous IoT devices with varying computational capacities, ensuring efficient local training without compromising privacy. Blockchain technology is integrated to provide decentralized, tamper-resistant access control through Role-Based Access Control (RBAC), allowing only authenticated devices to participate in the federated learning process. This combination ensures data confidentiality, system integrity, and trust among devices. This framework’s performance was evaluated using the N-BaIoT dataset, showcasing its ability to detect anomalies caused by botnets such as Mirai and BASHLITE across diverse IoT devices. Results demonstrate significant improvements in intrusion detection accuracy, particularly for resource-constrained devices, while maintaining privacy and adaptability in dynamic smart home environments. These findings highlight the potential of this blockchain-enhanced federated learning system to offer a scalable, robust, and privacy-preserving solution for securing smart homes against evolving threats.
The increasing adoption of multi-cloud database systems has transformed enterprise data management, enabling enhanced scalability, reliability, and cost efficiency.However, managing databases across multiple cloud providers introduces significant challenges, including data fragmentation, latency, security vulnerabilities, and inconsistencies in synchronization.Traditional approaches to database management struggle to provide seamless interoperability, fault tolerance, and resilience against failures, necessitating innovative architectural solutions.This paper explores the design and implementation of resilient multicloud database systems, integrating Distributed Ledger Technology (DLT) for enhanced data integrity, fault tolerance mechanisms to ensure high availability, and cross-platform synchronization techniques for maintaining consistency across heterogeneous cloud environments.DLT, particularly blockchain, offers a decentralized approach to data validation, reducing the risk of tampering and unauthorized modifications while enabling transparent and auditable transactions.Fault tolerance strategies, including redundancy, self-healing systems, and predictive analytics, play a crucial role in mitigating system failures and ensuring business continuity.Additionally, cross-platform synchronization mechanisms, such as conflict-free replicated data types (CRDTs) and real-time consistency protocols, are explored to address latency and data consistency challenges across cloud infrastructures.By integrating these technologies, organizations can enhance the resilience, security, and operational efficiency of multi-cloud database architectures.This paper provides a comprehensive framework for implementing adaptive database management solutions, leveraging AI-driven automation, blockchain-based security, and advanced fault recovery models.The findings highlight best practices for enterprises aiming to achieve scalable, reliable, and fault-tolerant multi-cloud database environments.Future research directions include the role of edge computing in multi-cloud synchronization, quantum-safe cryptographic techniques for DLT security, and AI-driven predictive failure management in cloud-native databases.
This study aims to address the challenges and propose solutions for the Optimization of Blockchain-Based Cybersecurity Systems to Enhance Resilience Against Ransomware Attacks using a Systematic Literature Review (SLR) approach. Blockchain is increasingly recognized as a transformative technology in cybersecurity due to its decentralized structure, transparency, and robustness in securing data. Despite these advantages, its widespread adoption is hindered by several challenges, including scalability, interoperability, high energy consumption, and limited access to representative ransomware datasets. This research highlights that integrating blockchain with advanced technologies such as data analytics, machine learning, and Explainable AI (XAI) can significantly enhance its effectiveness in combating ransomware.The findings reveal that Graph Convolutional Neural Networks (GCN) enable real-time detection of ransomware patterns in network traffic with an accuracy of up to 95%. Furthermore, Layer-2 solutions like the Lightning Network and sharding effectively alleviate the load on main blockchains, thereby increasing transaction throughput. Efficient consensus mechanisms, including Proof of Stake (PoS) and Delegated Proof of Stake (DPoS), address energy consumption issues, making blockchain more adaptable to IoT and resource-constrained environments. These approaches have proven successful in enabling early detection, mitigation, and prevention of ransomware in IoT systems, cloud infrastructures, and smart grid networks. The implications of this study underscore the potential of blockchain as a critical component of proactive and adaptive cybersecurity systems. However, overcoming existing challenges requires further development of hybrid frameworks that integrate blockchain with data analytics and machine learning technologies. In addition, efforts should focus on standardizing global security protocols to enhance interoperability and creating robust, diverse ransomware datasets to support more accurate detection systems. Future research should also explore methods to minimize latency and improve blockchain efficiency in real-time cybersecurity applications.
In light of the escalating complexity of the cyber threat environment, the role of Collaborative Intrusion Detection Systems (CIDSs) in reinforcing contemporary cybersecurity defenses is becoming ever more critical. This paper presents a Blockchain-based Collaborative Intrusion Detection Framework (BCIDF), an innovative methodology aimed at enhancing the efficacy of threat detection and information dissemination. To address the issue of alert collisions during data exchange, an Alternating Random Assignment Selection Mechanism (ARASM) is proposed. This mechanism aims to optimize the selection process of domain leader nodes, thereby partitioning traffic and reducing the size of conflict domains. Unlike conventional CIDS approaches that typically rely on independent node-level detection, our framework incorporates a Weighted Random Forest (WRF) ensemble learning algorithm, enabling collaborative detection among nodes and significantly boosting the system’s overall detection capability. The viability of the BCIDF framework has been rigorously assessed through extensive experimentation utilizing the NSL-KDD dataset. The empirical findings indicate that BCIDF outperforms traditional intrusion detection systems in terms of detection precision, offering a robust and highly effective solution within the realm of cybersecurity.
Web 3.0 marks the beginning of a new era for the internet, characterized by distributed technology that prioritizes data ownership and value expression. Web 3.0 aims to empower users by providing them with ownership and control of their data and digital assets rather than leaving them in the hands of large corporations. Web 3.0 relies on decentralization, which uses blockchain technology to ensure secure user communication. However, Web 3.0 still faces many security challenges that might affect its deployment and expose users' data and digital assets to cybercriminals. This survey investigates the current evolution of Web 3.0, outlining its background, foundation, and application. This review presents an overview of cybersecurity risks that face a mature Web 3.0 application domain (i.e., decentralized finance (DeFi)) and classifies them into seven categories. Moreover, state-of-the-art methods for addressing these threats are investigated and categorized based on the associated security risks. Insights into the potential future directions of Web 3.0 security are also provided.
The trading of data is becoming increasingly important as it holds substantial value. A blockchain-based data marketplace can provide a secure and transparent platform for data exchange. To facilitate this, developing a fair data exchange protocol for digital goods has garnered considerable attention in recent decades. The Zero Knowledge Contingent Payment (ZKCP) protocol enables trustless fair exchanges with the aid of blockchain and zero-knowledge proofs. However, applying this protocol in a practical data marketplace is not trivial. In this paper, several potential attacks are identified when applying the ZKCP protocol in a practical public data marketplace. To address these issues, we propose SmartZKCP, an enhanced solution that offers improved security measures and increased performance. The protocol is formalized to ensure fairness and secure against potential attacks. Moreover, SmartZKCP offers efficiency optimizations and minimized communication costs. Evaluation results show that SmartZKCP is both practical and efficient, making it applicable in a data exchange marketplace.
Zubaida Rehman, Mark Gregory, Iqbal Gondal, Hai Dong · 5 authors
This paper presents a comprehensive study on eclipse attacks in blockchain networks by describing how eclipse attacks work, their effects, detection, and prevention. In this context, understanding and controlling network-level attacks, such as eclipse attacks, is an essential task in relation to assurance and reliability for decentralized systems that utilize blockchain technology. An eclipse attack is a sequence of network-layer attacks that monopolize the connections to a target node to isolate it from the rest of the network. Eclipse attacks that focus on node discovery manipulation, can have a substantial impact on a blockchain network, by increasing transaction computation cost, transaction censorship, and consensus disruption. We studied eclipse attacks on a blockchain network. The attack vectors were associated with node discovery manipulation, network partitioning, and information flow exploitation. This paper also reviews state-of-the-art detection methods and prevention strategies, shedding light on their effectiveness and limitations. Awareness of eclipse attacks and their effect provides the motivation for further research in developing practical and resilient security measures for blockchain networks.
William Villegas-Ch, Jaime Govea, Rommel Gurierrez, Aracely Mera-Navarrete
The exponential growth of the Internet of Things (IoT) has boosted connectivity across various sectors, such as Industry 4.0 and smart cities. However, this expansion has also exposed IoT devices to critical vulnerabilities, including spoofing, DoS attacks, and unauthorized access. Traditional security solutions, based on centralized architectures, are neither scalable nor efficient enough to handle the increasing complexity and number of IoT devices, leading to high latencies, increased energy consumption, and inadequate intrusion detection. In this work, we propose a hybrid solution that combines Blockchain and artificial intelligence (AI) to improve security and operational efficiency in IoT networks. Blockchain ensures device authentication and data integrity through a lightweight consensus protocol, while AI enables real-time intrusion detection using deep learning models. The simulations demonstrate that the proposed system improves the precision of detecting phishing attacks by up to 95.2%. At the same time, the authentication latency is reduced to 15 ms in networks with 1000 connected devices, 66.6% faster than traditional solutions. In addition, the energy consumption of the hybrid system is 31.8% lower than that of conventional approaches, validating its scalability and efficiency in large-scale IoT networks.
Muwafaq Jawad, Ali A. Yassin, Hamid asadi, Zaid Ameen Abduljabbar · 7 authors
The Internet of Health Things (IoHT) is a network of healthcare devices, software, and systems that enable remote monitoring and healthcare services by gathering real-time health data through sensors. Despite its significant benefits for modern smart healthcare, IoHT faces growing security challenges due to the limited processing power, storage capacity, and self-defense capabilities of its devices. While blockchain-based authentication solutions have been developed to leverage tamper-resistant decentralized designs for enhanced security, they often require substantial computational resources, increased storage, and longer authentication times, hindering scalability and time efficiency in large-scale, time-critical IoHT systems. To address these challenges, we propose a novel four-phase authentication scheme comprising setup, registration, authentication, and secret construction phases. Our scheme integrates chaotic-based public key cryptosystems, a Light Encryption Device (LED) with a 3-D Lorenz chaotic map algorithm, and blockchain-based fog computing technologies to enhance both efficiency and scalability. Simulated on the Ethereum platform using Solidity and evaluated with the JMeter tool, the proposed scheme demonstrates superior performance, with a computational cost reduction of 40% compared to traditional methods like Elliptic Curve Cryptography (ECC). The average latency for registration is 1.25 ms, while the authentication phase completes in just 1.50 ms, making it highly suitable for time-critical IoHT applications. Security analysis using the Scyther tool confirms that the scheme is resistant to modern cyberattacks, including 51% attacks and hijacking, while ensuring data integrity and confidentiality. Additionally, the scheme minimizes communication costs and supports the scalability of large-scale IoHT systems. These results highlight the proposed scheme’s potential to revolutionize secure and efficient healthcare monitoring, enabling real-time, tamper-proof data management in IoHT environments.
Prof. R. C. Pachhade, Shubham Gaikwad, Aditya Sawwase, Dahihande Rohan
The idea focuses on enhancing the security and reliability of data exchange between military units. Traditional methods of secure communication often involve centralized systems, which can be vulnerable to breaches and single points of failure. By utilizing blockchain technology, the implementing idea introduces a decentralized approach that ensures data integrity and security through a distributed ledger system. In this system, blockchain provides a tamper-proof record of all communications, ensuring that data is encrypted, verified, and resistant to unauthorized access. This decentralized model eliminates the need for a central authority, reducing potential vulnerabilities and increasing the resilience of the communication network. As a result, the system aims to offer a more secure, reliable, and robust solution for confidential data transmission between army stations, enhancing operational security and efficiency.
F. B. Tebueva, V. I. Petrenko, D. Zh. Satybaldina, M. G. Ogur · 5 authors
Objective: to enhance the effectiveness of monitoring and responding to multivector attacks in a decentralized Internet of Things (IoT) environment by integrating federated learning, deep autoencoders, and the distributed IOTA ledger. The priorities include accurate attack detection, minimizing false positives, reducing response time, and preserving data privacy. Method: a problem-oriented system was developed, combining local monitoring on IoT nodes with autoencoders for anomaly detection, federated learning using the FedAvg algorithm for collective model updates, and decentralized alert dissemination via the distributed IOTA ledger. The system implements secure exchange of model parameters, digital message signing, and asynchronous response through a publish/subscribe network. Results: experimental studies on the real N-BaIoT dataset simulating multivector attacks demonstrated high detection accuracy (approximately 95%), achieving an F1-score above 94%, with false positive rates around 4%. The system's response time did not exceed 5 seconds, significantly improving operational reaction to attacks. Federated learning provided steady improvement in model quality considering data distribution and heterogeneity. The architecture proved scalable, fault-tolerant, and capable of effectively detecting complex threats across multiple system levels. Practical value: the solution is implementable in industrial IoT, smart cities, and medical networks to enhance cybersecurity while maintaining privacy and reducing network load. Scientific novelty: the study presents a comprehensive synthesis of federated learning, deep autoencoders, and distributed ledger technology for effective monitoring of multivector attacks in decentralized IoT environments. The proposed approach combines the advantages of distributed learning and blockchain mechanisms to achieve high adaptability, accuracy, and security in rapidly growing and diverse IoT infrastructures
In recent years, blockchain technology, as an innovative information technology, has received widespread attention in academia and industry. However, its limitations in mechanism design and the completeness of supporting infrastructure, combined with the immaturity of security concepts, have exposed blockchain systems to severe security threats and challenges. This study aims to address critical security issues in blockchain technology by proposing a blockchain security architecture based on Web attack principles. The architecture adopts a negotiated consensus mechanism and integrates real-time protection techniques from the field of cybersecurity, designing an innovative framework capable of identifying and restricting malicious nodes. With dynamic isolation as its core strategy, the architecture detects abnormal behaviors and temporarily isolates malicious nodes, preventing further damage to the blockchain network. The results demonstrate that this architecture successfully addresses the bottlenecks of inadequate targeted defense in existing blockchain systems and significantly improves operational efficiency and security. Experimental validation indicates that the architecture exhibits substantial practical value in scenarios such as decentralized finance (DeFi) and supply chain management, laying a solid foundation for the widespread application of blockchain technology in real-world settings.
The decentralized, transparent, and immutable ledger system of blockchain has fundamentally changed data security and digital transactions. Blockchain has built-in security safeguards, yet it is still vulnerable to flaws and attacks. In this review paper, the authors will examine the threats and vulnerabilities that blockchain technology faces and the mitigation factors that can be used to overcome these issues. The authors discuss the significant threats like the 51% attack, double spending attack and many more that compromise the integrity of blockchain technology further authors discusses the vulnerabilities that are present in consensus mechanisms, smart contracts, network level, cryptography and privacy. These vulnerabilities expose blockchain networks to potential exploits and operational risks. To overcome these threats and challenges, the paper also discusses several countermeasures that are used for strengthening the blockchain network. It includes consensus mechanism enhancement through hybrid models and enhancing network-level protection against DDoS and routing attacks. This paper also discusses about the significance of quantum resistance cryptographic algorithms, privacy-enhancing technologies like zero-knowledge proofs, and scalability solutions such as layer 2 protocols and sidechains. This review paper also includes the current research and advancements in security blocks and provides a detailed understanding of the present work and future initiatives in the blockchain system.
Salabat Khan, Mansoor Khan, Muhammad Asghar Khan, Fei Luo · 9 authors
Authentication and revocation are the key mechanisms to ensure the security of the Cooperative Intelligent Transportation System (C-ITS). C-ITS relies on the Vehicular Public Key Infrastructure (VPKI) for anonymous authentication and device revocation. Several works complemented the VPKI-based authentication and revocation process. However, several security and performance issues exist in both mechanisms. This article presents PBatch: Pseudonym Certificate Batch Authentication based on Distributed Ledger Technology. PBatch addresses challenges specific to the authentication and revocation process to achieve 1000 authentications per second. PBatch relies on the concept of batching pseudonym certificates by offloading heavy validation operations such as certificate chain and revocation status validation to local edge servers. This enables vehicles to validate a batch of pseudonym certificates with a fixed number of verification operations, thus simplifying the authentication of the pseudonym certificate at the end devices. Furthermore, a caching-based message authentication mechanism is introduced to validate a relatively larger number of safety messages. We also introduced a Generative Artificial Intelligence (GAI) based cache management mechanism for safety messages caching and fetching. Finally, experiments and security analysis are conducted to investigate PBatch performance and security. The results show that PBatch is more secure, feasible, and scalable than the leading VPKI-based authentication proposals.
The integration of Internet of Things (IoT) devices into smart environments has become increasingly prevalent, resulting in the collection of valuable user and service data. However, effectively utilizing this data often requires its aggregation on a central server to train algorithms capable of identifying and preventing malicious attacks, such as reconnaissance, DoS (Denial of service), DDoS (Distributed denial of service) within IoT networks. This transmission of raw data not only incurs substantial bandwidth costs but also raises significant privacy concerns. In this paper, we propose a federated learning framework for intrusion detection on IoT networks that incorporates a distributed storage system based on the Ethereum blockchain, enhancing the security of the federated learning process. This design offers several key benefits, including scalability, high availability, redundancy, and the capacity to process large datasets. Despite these advantages, relying solely on federated learning may not yield accurate results, particularly when dealing with highly imbalanced datasets. To address this challenge, we have integrated a diffusion model for data augmentation at each local node, which strengthens model robustness. Furthermore, to protect data privacy at each local node, we utilize transmitting and averaging model parameters instead of raw data. The proposed framework is trained and evaluated in two datasets. The MNIST (Modified National Institute of Standards and Technology) dataset and BoT-IoT dataset. Our results indicate significant improvements in detecting zero-day attacks, achieving an average F1-score of 98.3% on the short version of the BoT-IoT dataset as well.
The growth of the Metaverse brings new security problems that traditional perimeter-based defenses can’t manage. This research proposes and tests an integrated Zero-Trust Architecture aimed to solve these weaknesses by merging artificial intelligence (AI)-driven behavioral threat detection, blockchain-based decentralized identification, and post-quantum cryptography. For anomaly detection, the architecture uses a federated ResNet-50 model; for data management that meets regulatory standards, it uses a Hyperledger Fabric-based identification system with Zero-Knowledge Succinct Non-Interactive Argument of Knowledge; and for key exchange that is immune to quantum attacks, it uses the CRYSTALS-Kyber algorithm. Penetration testing, a Delphi study with 20 experts, and user surveys all show that the architecture greatly improves security metrics. This system has a False Acceptance Rate (FAR) of 5.2%, which is 42.7% lower than the 9.1% FAR baseline of rule-based systems, 99.1% protection against Sybil attacks, and strong quantum resilience with a 1.2× latency penalty compared to AES-256. The approach also partially complies with the General Data Protection Regulation by using cryptographic erasure proofs. But these security improvements come at a cost: AI inference now uses 3.1 times more graphics processing unit resources. The results show that the suggested architecture creates a scalable, empirically validated basis for protecting decentralized virtual environments, striking a good balance between security, compliance, and performance trade-offs.