In response to problems such as a lack of trust, low resource utilization rates, conflicts due to multiple constraints, and security risks associated with sharing 5G wireless access network resources, this study proposes an efficient, trustworthy, and secure distributed resource sharing system and optimizes the resource allocation strategy. First, it performs virtual decoupling and atomic modeling for the three core computing resources: spectrum, security, and computing power. It also designs a five-layer distributed resource-sharing framework that integrates blockchain and software-defined networks. Additionally, it proposes an improved delegated proof-of-stake consensus mechanism, as well as an asymmetric encryption transaction authentication and resource status traceability mechanism. Second, for the multi-constraint conflict issue, it designs a multi-agent deep deterministic strategy gradient secure resource allocation algorithm integrating long-term and short-term memory state prediction. The verification experiments were carried out based on the 5G-RAN public resource scheduling dataset in accordance with the 3GPP TR38.901 protocol specification. The experimental hardware was equipped with Intel Core i9-13900K processor, NVIDIA RTX 4090 graphics card, etc. The simulation platform was built on the Ubuntu 22.04 LTS system using the PyTorch 2.1.0 deep learning framework and the NS-3 3.36 simulation tool. The comparison benchmarks were mainstream centralized resource allocation schemes, blockchain, federated deep reinforcement learning schemes, and consortium chain hierarchical cross-slice schemes. The experimental results showed that the resource utilization rate of this framework reached 89.3%, the transaction delay was only 21.8 ms, the service quality satisfaction and security compliance rate were 96.7% and 98.2% respectively, the double-spend attack resistance rate and resource status traceability accuracy rate both reached 99.9%, and all related indicators were significantly superior to the existing comparison schemes. This study provided technical support for 5G resource collaboration in scenarios such as industrial internet and vehicle networking, effectively solving the trust bottleneck and scheduling problems in distributed environments. However, the research has not fully considered the adaptability of resource scheduling in extreme network environments. The computational power consumption of the algorithm in large-scale node deployment scenarios must be optimized further. The computational cost of the blockchain and multi-agent deep reinforcement learning components is high. Additionally, the system’s scalability in ultra-dense 5G scenarios must be improved. To a certain extent, this framework’s immediate large-scale practical application in complex 5G network environments is limited.
One area of application for distributed ledger technologies is the Internet of Things. These technologies can provide an effective solution to many problems in this field. The consensus layer is a crucial architectural component of distributed ledger systems. Modern IoT networks place increased demands on the consensus mechanisms used in blockchain systems. There are many consensus protocols with different properties and purposes, including those for IoT blockchain networks. Selecting an appropriate consensus protocol for a specific IoT blockchain system is an important and complex task. Multi-criteria decision analysis methods are widely used in such problems, as they allow for the consideration of multiple conflicting criteria and provide a balanced approach to evaluating alternatives. Given the variability of network parameters and requirements of consensus mechanisms, multi-criteria decision-making methods can support more informed protocol selection. This paper presents a decision support framework for selecting a consensus protocol for blockchain-based Internet of Things networks. The system is an implementation of a previously developed conceptual model for a consensus protocol selection framework. A case study is also provided to demonstrate the application of the system.
S. Tamilselvi, Ravikumar R. N., Duggirala Aravind, Satheesh Kumar A. · 6 authors
The integration of Distributed Ledger Technologies (DLTs) with Digital Twin (DT) systems is transforming smart hospital infrastructures by facilitating secure, transparent, and real-time operational intelligence. As healthcare settings rely more on IoMT devices, AI analytics, and automated processes, it's crucial to ensure that the data shared between real and virtual systems is reliable and accurate. DLT provides decentralized validation, immutable storage, and automated smart-contract governance, ensuring trustworthy Digital Twin updates for predictive maintenance, patient-flow optimization, and resource management. By strengthening interoperability, enhancing cybersecurity resilience, and supporting transparent data-sharing mechanisms, DLT-enabled Digital Twins offer a robust foundation for next-generation intelligent hospital ecosystems. This chapter looks at different design models, real-life examples, performance details, and future research directions that are important for making healthcare changes that are scalable, secure, and ethical.
Muhammad Umar Janjua, Akshaya Mani, Uğur Şen, Daniel Kaiser
Privacy and anonymity of validators, especially regarding IP address linkability, are essential to protect the Ethereum network from various attacks. Network-level attacks, such as DoS, can interrupt validators and affect the overall security of the Ethereum network. Correlating the IP addresses of validators with their identities, along with knowledge about their action slots can be exploited by attackers to cause network delays, MEV exploitation, and finality risks. Therefore, ensuring the unlinkability of a validator's IP and identity is crucial for maintaining the network's trust and resilience. In this techreport, we first provide a review of the existing network and consensus layer techniques that have been proposed for maintaining validator privacy in the Ethereum blockchain. Secondly, we evaluate a Tor-based protocol named Tor push that helps unlink validator identities (IDs) from their nodes' IP addresses, thereby making it difficult to determine any end-to-end correlation between validator IDs and IP addresses of validators' beacon nodes. To evaluate the effectiveness of Tor push, we present a working, deployed proof-of-concept (PoC) implementation in the Nimbus Ethereum client. Our PoC deployment pushes attestations, aggregations, and block proposals over Tor to the Goerli testnet. Furthermore, we also analyse the security and latency of Tor push. Our experimental results suggest that Tor can be incorporated into the existing Ethereum network with a tolerable latency overhead of 613.82 ms on average and without compromising the overall network performance while enhancing the location privacy of validators in the Ethereum network.
Pim Keer, Ioannis Alexopoulos, Matteo Maffei, Marco Argentieri · 6 authors
Bitcoin is the cryptocurrency with the largest market capitalisation, but its widespread adoption is fundamentally limited by the scalability constraints of its consensus algorithm, which requires every transaction to be confirmed onchain. To address this, several Layer-2 scalability solutions have been proposed to move payments offchain -- most notably, the Lightning Network. However, their deployment remains hindered by cumbersome setup requirements: users must lock funds onchain to participate and engage in complex auxiliary protocols (e.g., for channel rebalancing, top-ups, and routing). Other solutions, like payment pools, sidechains and rollups, cannot be implemented in a non-custodial way on Bitcoin due to its limited scripting capabilities, or require all protocol participants to update the offchain state. In this work, we present Ark, the first Bitcoin-compatible commit-chain. Ark enables offchain transactions of virtual UTXOs (VTXOs), through an untrusted operator who aggregates them into succinct onchain commitments. A distinctive feature of Ark is its ease of deployment: users can receive offchain payments without locking any funds beforehand and Ark state updates can be performed only requiring the users involved in that update. We formally define the Ark protocol and prove its security. During this process, we identified two attacks affecting the testnet implementation, which we responsibly disclosed and proposed fixes for, which have been now integrated into the mainnet implementation. Our experimental evaluation demonstrates that Ark can commit onchain to batches of arbitrarily many VTXOs with a constant-sized footprint of approximately 200 vB. Cooperative exits add one output per user, while unilateral exits require $\mathcal{O}(\log n)$ transactions of roughly 150 vB per VTXO for a batch of $n$ VTXOs.
The continuous digitization of the modern farming sector demands secure, intelligent, privacy-preserving, and scalable infrastructures for real-time data analysis. However, existing smart farming systems face significant challenges, including cyberthreats, data authenticity issues, and the need for reliable decision support. This article proposes a secure Digital Twin (DT) architecture for smart agriculture recommendation systems, integrated with Blockchain and Federated Gated Recurrent Units (FGRU). At the perception layer, IoT sensors monitor soil, crop, and environmental data, which is gathered by a Request Control Authority (RCA) and transmitted to local models. To ensure privacy, a GRU-based Federated Learning (FL) approach is employed to detect cyberattacks—such as Sybil, Man-in-the-Middle (MITM), DDoS, and Replay attacks—without exposing raw decentralized data. Furthermore, a Blockchain-assisted Zero-Knowledge Proof-based Authority (ZKPA) mechanism is integrated to ensure data authenticity. The validated farming data is stored at the architecture’s final layer, enabling a Physical Twin to monitor real-time processes and generate precise recommendations. The architecture was evaluated using a paddy field dataset (26 features, 10,081 samples). Experimental results show that the proposed federated GRU model achieves perfect detection performance for all considered attacks, while the ZKPA-based authentication mechanism achieves a 98–99% authentication success rate with sub-10 ms verification time and only 15–25% additional computational overhead, which is better than existing works.
Wahida Mansouri, Abdulrahman M. Qahtani, M. M. Kamruzzaman, Anandhavalli Muniasamy · 6 authors
The evolution of 6G networks brings unprecedented connectivity and processing capabilities to Internet of Things environments, yet it also raises more challenges in data security, intrusion detection, and computational efficiency. In this paper, a complete end-to-end framework with the inclusion of Artificial Intelligence, blockchain technology, and novel encryption techniques is proposed to address these challenges in 6G-IoT networks. Then, the AI-Powered Cybersecurity Events Dataset is utilized first, wherein network traffic is normalized through Min-Max normalization to normalize heterogeneous features to a uniform scale. A Gated Recurrent Unit-based neural network is then trained on this normalized data to detect real-time intrusions by learning complex temporal dependencies. Upon detecting anomalies, a blockchain layer is called to execute smart contracts that apply automatic security measures, e.g., quarantining affected nodes. For efficient processing, the architecture accommodates GRU-assisted task offloading optimization on a multi-factor delay, energy, and network load optimization model. Blockchain smart contracts manage load balancing and delegation verification autonomously without any central authority. Homomorphic encryption and proxy re-encryption also ensure data confidentiality in edge computing and secure multi-party computation. All encryption and task offloading operations are permanently stored onto the blockchain, ensuring system-wide transparency and auditability. Federated learning support is built-in to enable privacy-preserving decentralized AI training from distributed edge nodes. The entire framework is implemented in Python using TensorFlow/Keras for the GRU model and web3.py for blockchain-related interactions. Experimental measurements demonstrated the efficiency and stability of the system by exhibiting a percentage of accuracy of 98.9% in intrusion detection, justifying the applicability for future 6G-IoT secure and scalable operations. The experiments utilize the AI-Powered Cybersecurity Events Dataset containing approximately 1.5 million network flow records with class distribution of 52% benign traffic, 21% DoS, 15% probing, and 12% privilege escalation events.
The fast communication networks are crucial to support the modern digital services like cloud computing, massive data transmissions and real time multimedia applications. Since network traffic is constantly increasing exponentially, a proper approach to managing congestion is required to ensure the delivery of information is stable, minimize delays, and efficiently use bandwidth. Conventional congestion control mechanisms tend to use systems that are based on fixed rules and thresholds, and may be unable to be flexible in highly dynamic network situations. A graphical congestion control model is intelligent based on a multi-agent Deep Q-Learning model in which the distributed agents are tasked with monitoring network conditions such as queue length, delay, packet loss, and available bandwidth. The agents are taught the best acting policies in traffic regulation by means of interaction with their network environment and dynamically change their rates of transmission to reduce congestion. Learning organization is decentralized and enhances adaptability and scalability within large network systems. In comparison to traditional methods that attained a throughput of 780-910 Mbps, 2.9-5.8% packet loss, and 84-120 ms end-toend delay, performance evaluation has shown to achieve better network performance of 960 Mbps throughput, 1.8 end-to-end delay, and 0.8% end-to end packet loss. These enhancements underscore the success of smart use of reinforcement learning methods in adaptive congestion control in high-speed networking settings.
This paper explores the application of distributed ledger technology to facilitate dynamic resource allocation in virtualized radio access networks (RANs). A novel architecture is presented that leverages blockchain to automate the exchange of network functions among diverse stakeholders. This approach addresses challenges related to trust and scalability in open RAN environments, enabling autonomous and secure sharing of infrastructure resources. The proposed system employs auction-based mechanisms and network virtualization to allow entities to transact RAN resources, such as virtual network functions (VNFs), in a decentralized manner. Simulation results demonstrate the potential of this framework to enhance flexibility and efficiency in next-generation mobile networks.
The correct and resilient operation of distributed systems—spanning global financial ledgers, decentralized autonomous organizations, and peer-to-peer energy microgrids—depends fundamentally on the correctness of their underlying consensus mechanisms. These protocols must guarantee agreement on shared state among a collection of potentially faulty or adversarial nodes, upholding the dual properties of safety and liveness even under hostile conditions. Despite the growing diversity of consensus algorithms, from classical crash-fault tolerant approaches such as Paxos and Raft to modern Byzantine fault-tolerant (BFT) variants and Directed Acyclic Graph (DAG)-based structures, the research community lacks a unified architecture for their systematic, cross-platform evaluation. This paper proposes a modular, extensible framework called the Consensus Evaluation and Resilience Framework (CERF) to fill this gap. CERF integrates five core components: a high-fidelity network emulation layer, a pluggable system-under-test (SUT) adapter, a fault injection engine (FIE) capable of simulating both benign and Byzantine failure modes, a multi-dimensional performance monitoring unit (PMU), and a formal consistency checker. By decoupling the testing infrastructure from protocol implementation, CERF enables fair comparisons of throughput, latency, energy efficiency, and scalability. The proposed architecture draws on insights from seminal benchmarking works including BlockBench, Jepsen, and ByzzBench, and extends evaluation criteria to address modern challenges in IoT environments, post-quantum security, and DAG-based ledgers. Graph-theoretical principles are incorporated at the topology design stage to model network vulnerabilities and guide adversarial scenario construction.
In the deployment of smart home IoT there is a critical security Loopholes that centralized management system are not able to solve. We present LiDIA, a lightweight Delegated Proof-of-Stake (DPoS) blockchain framework integrating Inter Planetary File System (IPFS) distributed storage and zeroknowledge authentication for residential IoT. we performed various simulation and result demonstrate end-to-end confirmation averaging 18 ms, throughput exceeding 1,200 Transactions Per Second (TPS), device-level energy reductions exceeding 99% over Proof-of-Work and AI-driven threat detection accuracy of 87.3% 92.5%, establishing a scalable and secure foundation for smart home environments.
Payment channel networks enable scalable off-chain payments, but their practical deployment remains constrained by a persistent tension among routing efficiency, liquidity visibility, transaction privacy, and settlement security. Existing multipath routing mechanisms can improve payment success under fragmented liquidity, yet they often expose sensitive balance information, leak structural features of payment routes, and enlarge the attack surface for probing, channel exhaustion, and selective forwarding. This paper presents a novel framework, Adaptive Multipath Proofs (AMPs), for privacy protection and security in payment channel networks. The core idea is to bind multipath routing decisions with lightweight zero-knowledge verifiability, allowing intermediate nodes to validate path feasibility, fragment consistency, and settlement constraints without learning exact channel balances, the complete payment amount, or the global route structure. AMP integrates three mechanisms: a hidden-liquidity feasibility proof that supports privacy-preserving route selection, an adaptive payment-splitting strategy that dynamically determines fragment allocation according to network congestion and balance uncertainty, and a proof-coupled settlement guard that enforces atomicity and timeout consistency across all payment fragments. Together, these mechanisms reduce information leakage while preserving robust payment execution under dynamic network conditions. Experimental evaluation on real Lightning Network topologies and synthetic stress scenarios demonstrates that AMP significantly lowers balance disclosure and endpoint inference risk, improves payment completion under skewed liquidity distributions, and introduces only moderate computational and communication overhead. The results indicate that adaptive proof-carrying multipath routing offers a practical and effective direction for building secure, privacy-preserving, and high-success payment channel networks.
The fast move toward sixth-generation (6G) distributed networks is making it possible to create highly dynamic, intelligent, and collaborative service environments for a wide range of use cases, including smart cities, autonomous systems, industrial IoT, immersive communication, and edge intelligence. But working together on a large scale in 6G environments comes with a lot of technical problems, such as the need for instant access to resources, coordinating different types of services, exchanging data that can grow, and making sure that security, trustworthiness, and privacy are all strong. Traditional centralised architectures have trouble meeting these needs because they have single points of failure, limited transparency, and problems with managing trust. Blockchain technology provides decentralisation, immutability, and the establishment of trust; however, its fundamental limitations in throughput and storage capacity impede its direct implementation in extensive 6G distributed collaboration systems. This paper presents a universal blockchain-based collaboration architecture specifically designed for 6G distributed networks, accompanied by an end-to-end collaboration mechanism aimed at delivering efficient, secure, and reliable resource-sharing functionalities. The proposed architecture combines service-oriented design ideas with adaptive blockchain improvements to get around problems with scalability. To address the throughput constraints of traditional blockchain systems, a service-oriented, capacity-adaptive blockchain sharding framework is proposed. In this framework, network nodes with different levels of consensus efficiency are dynamically split into different shards using a strategy that rates nodes based on their reputation. The assessment checks the performance of nodes by looking at things like their computational power, communication delay, reliability, and past behaviour. This is to make sure that shard formation is fair and reliable. Also, transactions are grouped by service type and sent to the right shards, which have the right level of consensus for the service. This service-aware transaction assignment makes sure that high-performance shards handle services that need to be processed quickly, while shards with moderate consensus capabilities handle services that don't need to be processed as quickly. This kind of adaptive alignment between service characteristics and shard performance greatly improves the overall throughput of the system and the efficiency of resource use. To make consensus even more efficient when workloads change, a load-sensitive Practical Byzantine Fault Tolerance (PBFT) mechanism is suggested for intra-shard consensus. The proposed load-aware enhancement dynamically changes consensus parameters based on shard load conditions, which is different from regular PBFT, which may slow down when there are a lot of transactions. This adaptive approach cuts down on communication overhead, makes the system more fault-tolerant, and keeps consensus performance stable even when many people are working together. So, the architecture makes sure that transactions are always valid while still meeting the ultra-low latency and high reliability needs of 6G apps. Along with throughput issues, storage scalability is still a big problem for blockchain-based systems because the ledger size keeps getting bigger. The paper proposes a hybrid storage policy that combines both on-chain and off-chain storage methods to get around this problem. To keep things immutable and trustworthy, important metadata, transaction proofs, and security-related records are kept on-chain. Large amounts of service data and information about sharing resources are kept off-chain using distributed storage solutions. Secure cryptographic connections between on-chain and off-chain parts make sure that data is accurate and can be verified without putting too much strain on the blockchain ledger. This mixed strategy greatly reduces the pressure on storage while keeping things clear and traceable. A lot of simulations are done to see if the proposed architecture and mechanisms are possible, can be scaled up, and will work better than other options. The results show that this new way of working together on a blockchain has a lot better throughput, less consensus latency, more balanced shard usage, and better storage efficiency than traditional blockchain-based collaboration models. Also, the proposed framework offers strong security guarantees and is resistant to bad behaviour in networks with different types of devices. In general, the suggested universal blockchain-based collaboration architecture is a scalable, secure, and adaptable way to make resource sharing in 6G distributed networks more efficient. The framework effectively solves blockchain scalability problems while also meeting the strict performance needs of next-generation distributed communication systems by combining service-aware sharding, load-sensitive consensus optimisation, and hybrid storage design. The results show that the architecture has the potential to be a key part of trustworthy and smart collaboration in future 6G ecosystems.
Jose Almarcha-Sanchez, Maria-Jesus Alba-Baena, Volodymyr Dubetskyy, Maria‐Dolores Cano
Abstract Open-source simulators let engineers stress-test blockchain ideas long before field deployment, yet few studies compare tools side-by-side. This tutorial article benchmarks two research-grade simulators, namely, BlockSim and Simewu, and the production-grade IOTA Hornet node under an identical traffic harness that runs on laptop-class hardware. Results show that consensus style dominates capacity. A DAG ledger that finalizes one milestone per second (≈ 6 tx s⁻¹) surpasses the 10 Transactions Per Second (TPS) ceiling of a six-node Bitcoin simulation, while Ethereum-style 12 s blocks lift the same mesh to approximately ~ 20TPS.BlockSim reproduces proof-of-work fairness within ± 3% of theoretical expectations, and a ten-fold increase in propagation delay cuts a miner’s reward roughly in half despite equal hash power. Hornet delivers protocol-truth execution, but at noticeably higher CPU, memory and bandwidth cost than the simulators. All scripts, Docker files and raw logs are released under an open license, providing a one-click baseline for future benchmarking of new distributed-ledger technologies.
Our suggestion is a hybrid reputation-based routing protocol in the context of decentralized routing. networks which partitions trust measurements to on-chain immutable logs and off-chain dynamic computations to attain security and scalability. The protocol incorporates a layer of sharded blockchain to store critical. trust data and local reputation model to do real-time updates, enabling routing choices that cannot be tampered with. performance. The trust ledger which operated on-chain kept the records of the impartiality of. Historical performance and node identities, whereas the off-chain reputation engine uses graph neural network to calculate dynamic. real-time scores like packet delivery rate to be trusted. and latency. Additionally, it uses rollup-based batches of synchronization off-chain. optimistic updates to zero-knowledge proofs, making on-chain efficient. checking with minimum overhead. The suggested approach replaces conventional network discovery and forwarding modules using trust-based adjacency lists and trust-based pathing, and thus enhancing routing consistency over adversarial settings. Moreover, the structure integrates Hyperledger Fabric with. Graph Attention Network-based high-throughput sharded ledger operations. to update reputation in a privacy-preserving manner, proving to be linear. network size scalability. The experimental findings indicate that the system supports 10,000 transactions per shard and produces. Under 100 ms per ZK-Rollup proof, which is appropriate to large-scale IoT. and DeFi deployments. This publication fills the gap between pure on-chain. and off-chain reputation systems, which provides a viable solution to scalable and secure decentralized routing.
Hamza Ibrahim, Love Allen Chijioke Ahakonye, Jae-Min Lee, D. Kim
The Industrial Internet of Things (IIoT) has transformed critical infrastructure but has also introduced severe security vulnerabilities, with breaches capable of causing catastrophic physical and operational damage. While blockchain technology offers a promising foundation for tamper-proof logging, existing platforms are often ill-suited for IIoT due to high latency, low throughput, and excessive energy consumption. Furthermore, most current research treats intrusion detection, secure logging, and system recovery as isolated components, lacking a unified framework for autonomous, verifiable resilience. To bridge this critical gap, this paper introduces PureChain, a holistic, secure, and resilient ecosystem. PureChain integrates a custom lightweight blockchain with a deep learning-based intrusion detection system and a novel verifiable recovery protocol, creating a closed-loop security model. The framework leverages a novel Proof of Authority and Association (PoA2) consensus mechanism, achieving high throughput (16.82 TPS), low latency (0.0594 s), and minimal energy consumption (12.43 W), demonstrating suitability for resource-constrained IIoT environments compared to general-purpose platforms like Ethereum and Hyperledger which are optimized for different use cases. Upon intrusion detection by optimized models like XGBoost (99.87% accuracy), immutable blockchain logs actively trigger and cryptographically attest to infrastructure-enforced recovery actions such as device isolation via SDN switches or state rollback through hardware management controllers. Extensive evaluation on benchmark IIoT datasets (IoT-CAD and IoTForge) demonstrates a detection-to-recovery success rate of up to 98.59% while maintaining 100% data integrity. PureChain establishes a new paradigm that unifies real-time threat intelligence, blockchain-based trust, and provable autonomous recovery for next-generation IIoT security.
This deliverable presents the design and functional validation of a Blockchain Toolkit that supports decentralised identity, privacy-preserving verification, and trust management mechanisms tailored for emerging 6G ecosystems. The toolkit addresses fundamental limitations of centralised trust infrastructures by replacing hierarchical identity and communication models with ledger-anchored, self-sovereign, and cryptographically verifiable components suitable for large-scale, heterogeneous environments.At its core, the toolkit provides a Self-Sovereign Identity (SSI) architecture based on Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and Anonymous Credentials (ACs), following W3C standards. This identity layer enables secure authentication, selective disclosure, and privacy-preserving verification without dependence on central authorities. Secure messaging and data exchange are supported through DIDComm-based communication patterns and encrypted, DID-bound storage, enabling trusted interactions across administrative and organisational boundaries.The deliverable further consolidates a set of cryptographic building blocks relevant to privacy and trust in 6G systems. These include zero-knowledge proof–based verification patterns, anonymous credential workflows, and privacy-enhancing mechanisms designed to reduce metadata leakage while preserving auditability. Together, these components enable verifiable compliance and trustworthy coordination in adversarial or untrusted environments.To demonstrate applicability, the Blockchain Toolkit is mapped to representative 6G-aligned use cases. These include specialised consensus mechanisms for dynamic spectrum environments, AI-assisted trust management to address data quality and integrity challenges, and NFT-based resource management for network slicing and dynamic spectrum sharing. In these scenarios, blockchain-based tokens and credentials act as programmable trust anchors, while the toolkit’s identity and cryptographic layers enhance privacy, accountability, and resilience against misuse and collusion.Overall, Deliverable 2.4 provides a coherent and standards-aligned toolkit for decentralised trust in 6G ecosystems. By integrating decentralised identity, privacy-preserving cryptographic verification, secure communication, and application-driven blockchain mechanisms, the toolkit supports scalable, privacy-aware, and verifiable interactions among diverse 6G stakeholders, contributing toward trustworthy next-generation wireless infrastructures.
The growing popularity of blockchain technology has underscored the need for robust network security. However, public blockchain networks remain vulnerable to attacks in which adversaries exploit numerous nonfunctional peer connections to disrupt block propagation across the entire network. In this article, we propose a practical nontargeted delay attack method and validate its feasibility, scalability, and significant impacts on blockchain networks of varying sizes, including EthereumPoW (ETHW) and premerge Ethereum Mainnet. In the ETHW network with 95 nodes, our adversarial peers introduce delays ranging from 0.33 to 2.8 s for half the nodes, with nearly one-third experiencing delays exceeding 5.9 s, derived from the 90th percentile of delay times. When in the premerge Ethereum network with 5739 nodes, over 80% of peers experience prolonged block propagation, resulting in a 77% increase in delay time, underlining the attacks' scalability and efficacy in large-scale environments. We also optimize the Ethereum client Geth by relaxing certain connection restriction, significantly reducing attack costs. Delving deeper, we analyze the implications of delay attacks on proof-of-work (PoW) and proof-of-stake (PoS) consensus mechanisms, illustrating how attackers can gain extra revenues through such attacks. Specifically, we propose a novel combined strategy to facilitate reorganization attacks under PoS. These findings highlight the urgent need to strengthen network-layer defenses and reinforce peer-to-peer (P2P) network protocol security against real-world delay exploits.
The Address Resolution Protocol (ARP) plays a critical role in the data link layer by mapping network addresses to physical hardware addresses. However, its lack of authentication mechanisms exposes it to spoofing attacks, enabling adversaries to intercept, modify, or disrupt communication within a local network. This paper proposes B-ARP (Blockchain-Secured ARP), a secure and decentralized approach to ARP leveraging blockchain technology. By treating MAC-IP bindings as verifiable transactions stored on a distributed ledger, the system ensures immutability, transparency, and resistance to tampering. A consensus-based validation mechanism prevents the propagation of forged ARP responses and enhances trust among network nodes. The proposed method not only mitigates common spoofing attacks but also introduces a scalable framework for integrating decentralized trust into foundational network protocols. Analytical evaluation demonstrates that this approach maintains strong security guarantees with minimal performance degradation, offering a viable path toward resilient and tamperproof address resolution in modern network architectures.
Michael G. Xevgenis, Maria Polychronaki, Dimitrios G. Kogias, Helen C. Leligkou · 5 authors
Zero-Touch Network (ZTN) represents a cornerstone approach of Next Generation Networks (NGNs), enabling fully automated and AI-driven network and service management. However, their distributed and multi-domain nature introduces critical security challenges, particularly regarding service identity and data integrity. This paper proposes a novel blockchain-based framework to enhance the security of ZTN through two complementary mechanisms: decentralized digital identity management and oracle-assisted network monitoring. First, a Decentralized Identity Management framework aligned with Zero-Trust Architecture principles is introduced to ensure tamper-proof authentication and authorization in a trustless environment among network components. By leveraging decentralized identifiers, verifiable credentials, and zero-knowledge proofs, the proposed Decentralized Authentication and Authorization component eliminates reliance on centralized authorities, while preserving privacy and interoperability across domains. Second, the paper investigates blockchain oracle mechanisms as a means to extend data integrity guarantees beyond the blockchain, enabling secure monitoring of Network Services and validation of Service-Level Agreements. We propose a four-dimensional framework for oracle design, based on qualitative comparison of oracle types—decentralized, compute-enabled, and consensus-based—to identify their suitability for NGN scenarios. This work proposes an architectural and design framework for Zero-Touch Networks, focusing on system integration and security-aware orchestration rather than large-scale experimental evaluation. The outcome of our study highlights the potential of integrating blockchain-based identity and oracle solutions to achieve resilient, transparent, and self-managed network ecosystems. This research bridges the gap between theory and implementation by offering a holistic approach that unifies identity security and data integrity in ZTNs, paving the way towards trustworthy and autonomous 6G infrastructures.