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.
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.
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.
Muhammad Asad, Aurora Paz-Pérez, F. Sánchez, Carlos Núñez-Gómez · 12 authors
As the limitations of the current cellular network generation have become apparent to tackle current connectivity needs in 6G, the scientific communities have started to investigate novel techniques to bolster the capabilities of the communication infrastructure. Within 6G, Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) are envisioned as key enablers to drive network performance and guarantee process integrity. However, from a security standpoint, those methods are dual-edged as they introduce a new threat surface that could be used to jeopardise the platform security, the trust in service utility and data privacy. This survey provides a consolidated review of the security, trust, and privacy impact of key 6G enabling technologies. The analysis begins by identifying the primary architectural drivers anticipated for next-generation mobile networks, systematically mapping their impact on the threat surface to identify critical resilience challenges. Conversely, we pinpoint protection methodologies enacted by these drivers, outlining concrete countermeasures that enhance the network’s security posture. Finally, we propose a unified reference architecture that integrates these benefits for holistic security, privacy, and trust management, complemented by a system-level evaluation.
L Blanco, Cristian J. Vaca-Rubio, Jorge Baranda, Farhana Javed · 40 authors
UNITY-6G introduces a AI-natively framework that unifies terrestrial (TN), non-terrestrial (NTN), and non-public networks (NPN), treating connectivity, computing, and intelligence as interdependent resources. The architecture utilizes an Inter-Domain Management Orchestrator (IDMO) based on Service-Based Management Architecture (SBMA) principles to coordinate services across heterogeneous domains. A core pillar of the framework is its AI-native design through autonomous agentic AI workflows following a standardized MS–AE–DE–ACT (Monitoring, Analytics, Decision, and Actuation) logical patterns. To enhance resource efficiency and sustainability, the architecture integrates Digital Twins (DT) for proactive system modeling and semantic communications to prioritize task-relevant information transfer. Security is addressed through a Trust Architecture leveraging Distributed Ledger Technology (DLT) for cross-domain auditability. The framework's utility is validated through proof-of-concepts targeting sustainable disaster handling, immersive XR/holographic communications, and time-sensitive services for Industry 4.0. The presented advances establish a foundation for the continuous development of high-performance, autonomous 6G systems.
This paper specifies the Inference Battery Token (IBT), an ERC-20 on Base that mints when the inference orchestrator verifies a failed AI response, compensating the subscriber and capturing a structured DPO preference pair in one operation. Minting occurs through two pathways: automatic spot-checks that re-execute sampled requests on trusted nodes, and subscriber-initiated flags confirmed through the same re-execution pipeline. Each verified failure mints tokens proportional to the compute cost wasted, ranging from 1 IBT for embeddings to 20 IBT for extended reasoning. Subscribers redeem IBT for battery credits, permanently burning tokens. A genesis allocation of 10,000,000 IBT bootstraps exchange liquidity, network operations, community grants, and early investor capital with 12-month vesting. Because minting depends on AI failure rates and redemption increases with adoption, circulating supply contracts as models improve. Contributors who serve compute earn platform credits and elect payouts in USD or IBT, with a 15% bonus for token election. Hardware contributors may also earn IBT by generating zero knowledge proofs for the verification system during idle GPU windows. Revenue from selling accumulated failure records to frontier labs through the data access tiers described in the companion paper provides fundamental backing for token value.
Carlos Nú Nez-Gómez, Víctor Garcia-Font, Helena Rifà-Pous, Muhammad Asad
The submitted work contains the following highlights: • We detail NxGenT, a decentralized reputation system for B5G and 6G networks. • We propose a three-phase reputation mechanism based on smart contracts. • We implement NxGenT and release its source code as open-source software. • We analyze NxGenT’s design and resilience against relevant trust attacks. • We evaluate NxGenT’s functionality and performance through experimental analysis. The evolution towards Beyond 5G (B5G) and 6G networks presents highly heterogeneous and dynamic scenarios in which numerous entities, including network operators, service providers and end users, interact in environments of mutual trust. However, the open nature of these networks poses significant challenges regarding security and trust, as traditional centralized mechanisms may prove inadequate or insufficient in such scenarios. In this context, decentralized trust systems are positioned as a promising solution to assess the reliability of entities participating in B5G and 6G networks, thereby enhancing decision-making processes and resilience of these environments. This paper introduces NxGenT , a decentralized reputation system based on blockchain and smart contracts for B5G/6G networks that guarantees the immutability and transparency of the collected evidence on entities’ performance and behavior, while decentralizing and automating the reputation mechanism. NxGenT is a decentralized, trustless system in which entities establish and verify compliance with Service Level Agreements (SLA) and provide feedback or subjective opinions about the entities they interact with in order to compute and assign reputation scores. To evaluate the proposal, we implement a local B5G testbed that deploys the primary components of this type of network, along with a second cloud-based testbed to analyze scalability in networks of different sizes. Finally, we contextualize NxGenT within the 6GENABLERS project as a representative use case of the proposed trust system, thus demonstrating its applicability in real scenarios.
Despite its serious consequences, network partitioning remains one of the most overlooked threats in blockchain systems. When a blockchain network splits into disconnected segments-due to routing failures, deliberate attacks, or infrastructure outages-nodes in each segment continue building their own version of the ledger independently. When connectivity is restored, one chain must be discarded, and any transactions recorded only on the discarded chain are lost. If an attacker deliberately timed payments to both sides of the partition simultaneously, those payments may succeed on both chains-a double-spend. The severity of this threat varies by consensus design. Public proof-of-work chains like Bitcoin face double-spend risk. Proof-of-stake networks like Ethereum risk finality delays and validator penalties. Permissioned systems like Hyperledger Fabric halt block production entirely. Despite these distinct consequences, the research literature treats these platforms in isolation. This survey unifies the analysis. We review over twenty peer-reviewed works (2002-2024), covering distributed systems theory, deliberate attack vectors, consensus protocol behavior, and detection and mitigation strategies. We present a comparative taxonomy, identify four open research problems, and conclude that existing mitigations are largely reactive and platform-specific-a cross-platform approach to partition resilience is still needed.
<b><i>Zombie Validator Resurrection</i></b> is a core consensus-layer exploit in Proof-of-Stake (PoS) and alternative Layer-1 networks where inactive, slashed, or economically abandoned validators regain influence without restoring proportional economic security. Through protocol gaps, state resets, or weak liveness enforcement, validators that should be neutralized re?-enter consensus, undermining safety assumptions and enabling stealth attacks. This paper formalizes the structural conditions enabling zombie validators, analyzes common resurrection mechanisms, and examines systemic risks to consensus integrity. We propose mitigation strategies to enforce validator lifecycle accountability and safeguard decentralized networks against stealth reactivation attacks.
A majority of well-known blockchains supports Non-Fungible Tokens (NFTs) operations via smartcontracts. Smart contracts often allow users to store arbitrary logic and code for a small programon the blockchain, and run it on the validator nodes when a monetary transaction is made to thecontract’s account. The use of smart contracts creates extra complexity in the blockchain protocolwhen allowing concurrent smart contract execution between shards alongside monetary transactions.To reduce the complexity of the blockchain protocol, native tokens and operations could be supportedby the blockchain, albeit restricting developers to a predefined set of functions and operations. Nativetoken operations can serve as a secure complement to the versatility of Turing-complete smartcontracts, providing robust, predefined primitives that could reduce the surface area for smart contractvulnerabilities. This project introduces a native token model that is centered on unique, non-fungible tokens thatsupport three primary use cases: ownership, rights and authenticity. By requiring bilateral transferconsent, and supporting optional clawback and freeze mechanisms, for revocable rights and non-transferable tokens respectively, the model bridges the gap between blockchains and legal frameworks.Additionally, an indirect transaction cost mechanism is implemented to protect the blockchain networkagainst Denial-of-Service attack during non-monetary operations. The project is supported by an emulator implementing monetary and token operations, alongsidea implementation design of how the token protocol could be integrated within ScaleGraph. The emulator’s throughput displays a slightly degraded performance of token transfer operations of ~150TX/s on average, compared to monetary transfers in the current implementation. Despite this, theemulator maintains a high average throughput of ~1300 TX/s with load generators of both monetaryand token transfer operations, when tested on a VPS with 2 vCPUs and 4GB of ram.If the token operation properties and performance compared to monetary transaction are transferableto ScaleGraph, native tokens could be a preferred way of implementation of tokens independentlywether or not with implementation of smart contracts.
O presente artigo investiga a otimização do throughput em redes blockchain baseadas em mecanismos de consenso Proof-of-Stake (PoS) a partir de um estudo comparativo de latência entre diferentes arquiteturas, parâmetros de configuração e arranjos de infraestrutura de rede. Partindo do desafio clássico da engenharia de sistemas distribuídos de conciliar escalabilidade, segurança e descentralização – conhecido popularmente como trilema do blockchain –, analisa-se como decisões de projeto, dentre as quais se incluem o desenho do protocolo de consenso, a duração dos intervalos (slots), o tamanho dos blocos, o número de validadores ativos e a topologia da rede P2P, afetam simultaneamente o volume de transações processadas por unidade de tempo e o tempo de confirmação percebido pelos usuários finais. A metodologia adotada combina revisão sistemática da literatura sobre modelos de desempenho em sistemas distribuídos e blockchains PoS, comparação de estudos empíricos que mensuram throughput e latência em plataformas públicas de referência – particularmente Ethereum pós-Merge, Solana e Polkadot – e análise conceitual dos trade-offs identificados. Os resultados obtidos sugerem que técnicas de otimização orientadas a parâmetros, tais como ajuste fino de tempos de slot e políticas de propagação acelerada de blocos, combinadas a inovações em camada de consenso – dentre as quais se destacam mecanismos híbridos com Proof of History e execução paralela de transações –, podem reduzir significativamente a latência de confirmação sem necessariamente comprometer a segurança criptográfica ou a robustez da rede. Entretanto, emergem limites estruturais associados à variabilidade de condições de rede em escala global, à heterogeneidade de capacidade computacional entre nós validadores e às exigências clássicas de tolerância a falhas bizantinas, os quais impõem fronteiras práticas ao ganho de throughput em ambientes permissionless. Conclui-se que uma abordagem sistêmica, que integre ajuste criterioso de parâmetros, desenho criterioso da infraestrutura de nós e mecanismos de monitoramento e benchmarking contínuos, é fundamental para o amadurecimento da infraestrutura Web3 e para o suporte a aplicações com requisitos estritos de qualidade de serviço, entre as quais se incluem protocolos de finanças descentralizadas, mercados de capitais tokenizados e aplicações sensíveis ao tempo.
Proof-of-Stake (PoS) networks rely on economic staking guarantees to align validator behavior with network security. However, a growing class of architectures permits validators to reuse, rehypothecate, or synthetically represent the same stake across multiple validation contexts. This paper defines <i>Double-Staking Validator Drift</i> as a structural vulnerability in which a validator’s effective economic exposure diverges from its apparent security contribution. We demonstrate how this drift undermines slashing guarantees, enables risk amplification, and creates false decentralization signals across PoS Layer-1 and restaking ecosystems. Existing mitigations—including slashing, social governance, and restaking policies—are shown to be insufficient due to enforcement blind spots and cross-domain opacity. We propose a logic-layer enforcement model that introduces validator-level stake exclusivity, temporal binding, and deterministic enforcement across consensus domains. This approach restores economic integrity to PoS security models and closes a critical gap in current network designs.
Proof-of-Stake (PoS) networks rely on slashing mechanisms to deter validator misbehavior and preserve consensus security. While early designs emphasized cryptographically verifiable conditions, many contemporary PoS systems increasingly incorporate social, governance, or reputation-based enforcement mechanisms to supplement protocol-level slashing. This paper identifies and formalizes a novel exploit class—<b><i>Social Slashing Exploits</i></b>—where subjective reputation signals, off-chain coordination, or governance influence are weaponized to selectively penalize honest validators or shield malicious actors. We analyze how reputation-driven enforcement undermines determinism, enables cartel behavior, and erodes consensus neutrality without requiring protocol violations. The paper argues that reputation-weighted slashing cannot provide reliable security guarantees in adversarial economic environments and proposes a logic-layer enforcement model based on execution-bound, objective misbehavior proofs. This approach restores deterministic accountability, preserves validator neutrality, and improves long-term system survivability.
This article examines the limitations of existing hybrid rollup solutions and presents an adaptive L2 architecture model that leverages artificial intelligence mechanisms. It is shown that current approaches to combining optimistic and ZK verification are largely based on static rules or manual mode selection, which prevents them from effectively accounting for load dynamics, risk profiles, and domain-specific properties of applications. Based on an analysis of optimistic, ZK, and hybrid rollups, an adaptive hybrid rollup model with AI-based transaction routing is proposed. This model combines transaction classification, GNN-based decision making, LSTM-based network condition forecasting, a dual-path execution system, and a continuous learning module. The article describes a Predictive Routing Algorithm that performs proactive selection between ZK and optimistic paths, taking into account cost, latency, security, and risk profile, as well as a Dynamic Resources Allocation mechanism that dynamically redistributes resources between the paths. The proposed multi-criteria optimization framework demonstrates the ability to tune objective weights to the specifics of different classes of DeFi and Web3 protocols. It is shown that the implementation of such a model is promising for systems with high transactional intensity, as it enables a shift from manual configurations to automated, data-driven policies for resource and risk management in hybrid rollup architectures.
Blockchain consensus mechanisms are fundamental to the security and decentralization of distributed ledgers. In Proof-of-Stake (PoS) systems, which are lauded for their energy efficiency, the fair and unpredictable selection of block proposers is paramount and relies heavily on secure random number generation. The RANDAO random number generation mechanism in the Gasper protocol is susceptible to hash collision attack, which can introduce adversarial bias in the block proposer selection process. From the perspective of resisting adversarial bias attacks, this paper examines the optimization of the Gasper consensus protocol, focusing on security issues such as vulnerabilities to hash collisions in RANDAO and high latency in asynchronous network environments. By analyzing the spatial–temporal distribution of historical block hashes, we propose a dual-round random number verification mechanism that enhances reliability through multiple validation models. We develop a dynamic game-theoretic model under incomplete information to analyze node strategy selection and interaction dynamics. Our experimental results demonstrate that the improved protocol (RABA-Gasper) offers superior resistance to attacks, fairness, and efficiency compared to conventional protocols. RABA-Gasper outperforms conventional ones, achieving a 6.8% attack success rate (vs. 32.7% for RANDAO and 18.2% for Two Look-Back) with 94.3% hash collision detection, a proposer Gini coefficient below 0.23, 2.3x higher throughput retention than RANDAO in asynchronous networks, and a slightly increased random number generation latency of 125 ms. Supported by a game-theoretic model, it guarantees security when honest nodes account for ≥2/3 of the total.
Abdullah Aljumah, Tariq Ahamed Ahanger, Imdad Ullah
Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains such as surveillance, logistics, and disaster management. However, ensuring the safety, security, and trustworthiness of UAV operations remains a significant challenge, primarily due to vulnerabilities in centralized data processing architectures. Traditional UAV systems rely on remote cloud servers to perform machine learning (ML)-based analytics, which introduces issues such as data exposure, latency, scalability bottlenecks, and susceptibility to cyberattacks during data transmission and storage. These challenges underscore the urgent need for a decentralized, verifiable, and privacy-preser ving learning mechanism that can support collaborative UAV intelligence without centralized control. To address these limitations, this study proposes a blockchain-enabled distributed ML framework that facilitates secure, peer-to-peer collaboration among UAV nodes. The framework integrates blockchain’s immutable ledger and smart contracts with decentralized ML models, enabling UAVs to share and validate trained models rather than raw data. This ensures data confidentiality, integrity, and transparency throughout the learning process. A stacking-based ensemble mechanism is employed to enhance predictive performance through collaborative knowledge aggregation. The proposed system is experimentally validated using a collaborative intrusion detection (ID) scenario using the KDD99 network attack data set and real-world implementation. The results demonstrate significant improvements in detection accuracy, latency and F1-score compared to conventional centralized ML methods, achieving an average accuracy of 97.9%, latency 198ms, and F1-score exceeding 97%. These outcomes confirm that the integration of blockchain and decentralized ML effectively mitigates cybersecurity risks while enabling scalable, trustworthy UAV intelligence.
Recently, the need for unified orchestration frameworks that can manage extremely heterogeneous, distributed, and resource-constrained environments has emerged due to the rapid development of cloud, edge, and IoT computing. Kubernetes and other traditional cloud-native orchestration systems are not built to facilitate autonomous, decentralized decision-making across the computing continuum or to seamlessly integrate non-container-native devices. This paper presents the Distributed Adaptive Cloud Continuum Architecture (DACCA), a Kubernetes-native architecture that extends orchestration beyond the data center to encompass edge and Internet of Things infrastructures. Decentralized self-awareness and swarm formation are supported for adaptive and resilient operation, a resource and application abstraction layer is established for uniform resource representation, and a Distributed and Adaptive Resource Optimization (DARO) framework based on multi-agent reinforcement learning is integrated for intelligent scheduling in the proposed architecture. Verifiable identity, access control, and tamper-proof data exchange across heterogeneous domains are further ensured by a zero-trust security framework based on distributed ledger technology. When combined, these elements enable increasingly autonomous workload orchestration, trading centralized control for adaptive, decentralized operation with enhanced interoperability, scalability, and trust. Thus, the proposed architecture enables self-managing and context-aware orchestration systems that support next-generation AI-driven distributed applications across the entire computing continuum.
Blockchain networks have revolutionized decentralized applications but remain vulnerable to evolving security threats due to their reliance on static consensus mechanisms that cannot adapt to changing threat landscapes. This paper addresses this critical security gap by proposing Autonomous Defense-Adaptive Consensus Optimisation for Blockchain Networks (ADACON), an original framework for the dynamic adjustment of consensus mechanisms based on Bayesian threat detection. The research investigates how real-time adaptation between multiple consensus protocols can enhance blockchain resilience while maintaining performance. The approach integrates a Bayesian Threat Detector, Consensus Adapter, and Network State monitor in a modular architecture that continuously assesses network conditions and switches between five consensus mechanisms (PoW, PoS, PBFT, PoA, DPoS) as threats emerge. The framework was evaluated through comprehensive simulations involving 1,000 nodes, testing response to six distinct attack vectors, including Sybil, DoS, Byzantine, Eclipse, Majority, and Routing attacks. Results demonstrate that ADACON effectively identifies and responds to varied attacks with a latency of 29.7 ms and throughput of 833 TPS). Statistical validation across five independent simulation runs (seeds 5-9) confirmed framework reliability with consistent performance metrics (CV < 7.1% for latency, 5.4% for throughput). Delegated Proof of Stake emerged as the most frequently selected mechanism (23.2%) due to its balanced performance across multiple security dimensions. Significantly, the system exhibited greater adaptability and attack coverage than existing hybrid approaches. The previous high switching frequency was reduced by using hysteresis, i.e., by providing dwell time and an improved threshold that avoids unnecessary switching. The study concludes that dynamic consensus adaptation offers substantial security advantages for blockchain networks, particularly in high-security environments like financial systems and critical infrastructure. However, further research must focus on optimizing switching frequency and developing secure transition protocols to maximize effectiveness. ADACON represents an incremental extension tested toward more resilient blockchain systems that can autonomously respond to emerging threats while balancing security, performance, and resource utilization.
Vikash Singh, Little, Barrett, Phil Hayes, Fang, Max · 7 authors
Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capacity. Current methods often lack robustness against a malicious or compromised node operator. This paper introduces a methodology for the verification of LN channel balances. The core contribution is a framework that combines Trusted Execution Environments (TEEs) with Zero-Knowledge Transport Layer Security (zkTLS) to provide strong, hardware-backed guarantees. In our proposed method, the node's balance-reporting software runs within a TEE, which generates a remote attestation quote proving the software's integrity. This attestation is then served via an Application Programming Interface (API), and zkTLS is used to prove the authenticity of its delivery. We also analyze an alternative variant where the TEE signs the report directly without zkTLS, discussing the trade-offs between transport-layer verification and direct enclave signing. We further refine this by distinguishing between "Hot Proofs" (verifiable claims via TEEs) and "Cold Proofs" (on-chain settlement), and discuss critical security considerations including hardware vulnerabilities, privacy leakage to third-party APIs, and the performance overhead of enclaved operations.