Nelli Yaswanth Kumar, Dr. Singothu Jhansi Rani, Setti Sarika
The rapid proliferation of Internet of Things (IoT) devices under sixth-generation (6G) networks introduces a highly dynamic, decentralized environment in which static, perimeter-based security models are no longer adequate. This paper proposes AZTM-v3 an adaptive Zero Trust framework that couples behavior-driven trust management with a Random Forest classifier to identify and isolate malicious nodes in real time. The framework is evaluated on an NS-3 simulation of a 150-node 6G IoT network subjected to Sybil, Denial-of-Service (DoS), spoofing, replay and ON-OFF attacks. Unlike prior trust-management proposals that report only qualitative or partial outcomes this work quantifies performance across five dimensions i.e detection accuracy, F1-score, false-positive rate, end-to-end latency and consensus-convergence time and benchmarks AZTM-v3 against PKI-based, centralized-trust and static-blockchain baselines. AZTM-v3 attains a 98.1% overall detection accuracy with a 1.6% false-positive rate at 150 nodes and sustains 95.4% accuracy at 200 nodes outperforming the PKI baseline by 12–18 percentage points across all tested loads. These results indicate that combining tiered trust evaluation with machine learning based classification yields a measurably more scalable and resilient security layer for 6G-enabled IoT deployments than existing static or purely cryptographic approaches.
Internet of Vehicles (IoV) security mechanisms often classify isolated messages or assign node-level trust scores, yet these decisions do not explain whether a malicious but authenticated event has distorted the intended evolution of traffic. This paper proposes IntentProv-IoV, a causally grounded provenance framework for traffic-intent preservation in V2X environments. Traffic intent is modeled as the short-horizon collective state expected under non-adversarial conditions, and deviation is measured between predicted and observed traffic states. The framework constructs temporal provenance graphs linking vehicles, roadside units (RSUs), cooperative perception outputs, prediction nodes, and traffic-control decisions. To remove the ambiguity of marginal contribution, node contribution is formalized as an interventional effect in a structural causal model and estimated through Monte Carlo counterfactual edge-weight attenuation, with a linear sensitivity fallback for real-time edge deployment. A calibrated composite score integrates anomaly evidence, traffic-intent deviation, trust risk, and provenance contribution. The evaluation design compares IntentProv-IoV with detection, trust, blockchain trust, graph anomaly, Granger causal, structural causal, and counterfactual GNN baselines and includes predictor sensitivity, adaptive adversaries, prediction noise, packet loss, trajectory-only real-data validation, and edge overhead. Simulation-scale results indicate improved attribution precision, stronger traffic-intent deviation reduction, and edge-suitable latency. By shifting V2X security from message-level detection to causally explainable traffic-intent assurance, IntentProv-IoV provides a more accountable security objective for cooperative vehicular systems.
Roadside edge infrastructure is becoming an active execution layer in connected transportation. Roadside units, edge servers, and vehicular gateways not only relay safety messages, but also run local services and support traffic-control decisions. Once these nodes are compromised, data authentication, blockchain logging, or forensic evidence management cannot by itself guarantee service trustworthiness, because the node that senses, signs, forwards, or processes the information may already be running an unauthorized software stack. This paper presents RISE-CT, a runtime-integrity assurance and self-healing recovery framework for roadside edge infrastructure. RISE-CT models each node through a layered security-state graph covering hardware roots, boot chains, firmware images, runtime processes, key usage, and service status. It combines boot-time, periodic, and event-triggered attestation with runtime drift diagnosis to detect firmware deviation, process injection, abnormal key access, replayed evidence, and service-behavior changes. When the risk score exceeds policy thresholds, a risk-aware admission state machine moves the affected node into degraded, quarantined, or recovering states, and coordinates service migration, key renewal, firmware rollback, and re-attestation before safe re-admission. Seed-controlled emulation under firmware tampering, malicious OTA updates, runtime injection, key misuse, replayed attestation, and roadside-service hijacking shows that, under the evaluated emulation settings, RISE-CT reduces the representative detection delay to 0.98 s and service interruption to 0.82 s, while achieving a 0.94 re-admission success rate with controlled attestation overhead. The results suggest that runtime node integrity can provide a potential security mechanism for improving the trustworthiness of roadside infrastructure under the evaluated conditions.
Network slicing and resource provisioning in 6G focus on creating multiple customized virtual networks over a shared infrastructure. However, these approaches also introduce challenges, like increased architectural complexity, higher implementation costs, security vulnerabilities between slices in resource optimization across highly dynamic and heterogeneous network environments. In this work, Exponentially Tactical Unit Algorithm (ETUA) is devised for network slicing in 6G. Initially, blockchain-enabled 6G network is simulated, and the set of features, like user device type, delay rate and packet loss rate are collected from various devices. Moreover, network slicing is done by ETUA that integrates Exponentially Weighted Moving Average (EWMA) and Tactical Unit Algorithm (TUA). Finally, resource allocation is performed using Attention High-order Deep Network (AHoNet) by considering the parameters that includes bit error probability, sum rate and trust. The efficacy of ETUA is examined by bit error probability, utility and latency with 0.012, 0.950 and 0.509 Sec.
Mahdiyeh Velaei, Mehdi Aminian, Seyyed Amir Asghari, Mohammad Faraji‐Mehmandar
Abstract With the increasing number of Industrial Internet of Things (IIoT) networks, critical infrastructures are now more vulnerable to cyberattacks. In this context, the need for distributed and privacy-preserving intrusion detection systems has become essential. In this paper, we introduce a secure federated learning framework for intrusion detection in IIoT networks that supports model training in non-IID environments without sharing raw data. In this system, each client maintains a lightweight MLP model locally, and a client-level DP-SGD is used to enhance privacy and hashing to maintain update integrity. Also, to consciously select clients and reduce the impact of malicious clients, a reputation-based mechanism is proposed that leverages the ideas of trust management in blockchain, but can be implemented without the need for a full blockchain implementation. The performance of the proposed model on the Edge-IIoTset dataset in binary and multi-class classification and in 3, 5, and 7 clients shows that the proposed model achieves an accuracy of over 98% in all scenarios, which is close to the results of the centralized approach.
Oleksandr Pidpalyi, Олександр Романов, Larysa Globa, Антон Романов · 6 authors
The subject matter of the article is the iTZBEI (Integrated Trust–ZTA–Blockchain SDN Efficiency Index) – a novel composite metric for quantitative security assessment of software-defined networks (SDN) integrating Zero Trust Architecture (ZTA) and Blockchain technologies. The relevance of the research is determined by the fact that the centralized SDN control model generates critical vulnerabilities, including DDoS attacks, unauthorized routing manipulation, and insider threats – for which no unified quantitative evaluation framework currently exists. The study introduced a formalized aggregated security metric that enables continuous monitoring and comparative assessment across all components of the SDN–ZTA–Blockchain architecture. The tasks to be solved include: (1) identification of principal SDN attack vectors; (2) formalization of a transaction-processing algorithm covering the full access lifecycle; (3) definition of nine local security indicators; and (4) construction of the iTZBEI index with justified weighting coefficients. The methods used combine mathematical formalization of access control processes, cryptographic transaction verification, and experimental emulation of attack scenarios in a Mininet–OpenDaylight–Hyperledger Fabric environment. Conclusions. The obtained results of the article consist in the development of a functional algorithm that performs dynamic verification of user requests, makes adaptive authorization decisions according to the principles of least privilege, and records these decisions in an immutable distributed ledger. A metrics system is proposed, including local indicators such as the Continuous Authorisation Integrity Score (CAIS), the Blockchain Audit Integrity Score (BAIS), and the Local Policy Integrity (LPI). On this basis, the generalized Integrated Trust and Zero-Trust Blockchain Evaluation Index (iTZBEI) is described as an aggregated metric for comparative evaluation and continuous monitoring of the network’s security state. Scientific novelty. This study introduces a unified SDN + ZTA + Blockchain framework for network security, formalizes a transaction-level algorithm that directly links access decisions with distributed audit procedures, and proposes the iTZBEI metric as the first integral indicator for evaluating the integration’s effectiveness in dynamic network environments.
Kapil K. Jajulwar, Priya Dasarwar, Uma Yadav, Bhakti Prasad Thakre · 6 authors
Blockchain consensus mechanisms are important to ensure the safe validation of transactions. However, the limitations of high computational complexity, energy consumption, and mining latency restrict the scalability of blockchain in large-scale IP-based and wireless network environments. Current methods mainly rely on single optimization methods without jointly optimizing miner selection and hash computation, resulting in inferior performance under dynamic network conditions. To fill this gap, this study presents a new hybrid bioinspired optimization framework for efficient blockchain mining, integrating Genetic Algorithm (GA), Firefly optimization, and Particle Swarm Optimization (PSO) into a unified architecture to take advantage of their complementary strengths. The proposed method uses both historical and real-time performance metrics to determine the best mining nodes. The Firefly algorithm is used to optimize the selection of hash ranges to reduce CPU workload. PSO is used to select high-performance neighboring nodes for collaborative mining. The model is implemented using the NS-2 simulator and tested over a network of 1000 wireless nodes under different consensus protocols. The experimental results illustrate 4.3% decrease in computational complexity, 4% decrease in energy consumption, and 5% decrease in mining delay. The model further reduces mining complexity by up to 34.2% and reduces the energy utilization from 24.5% to 16.6%, demonstrating its effectiveness for scalable and energy-efficient blockchain deployment.
This perspective examines whether nuclear fusion can provide a scalable, low-carbon power source for rapidly growing AI-driven data center demand. As large language models, cloud computing, and cryptocurrency mining accelerate electricity consumption growth, data centers are projected to account for a substantially larger share of U.S. and global electricity use in the coming decades, creating significant pressure on grid reliability and decarbonization goals. We evaluate the technical and economic alignment between data center load profiles and nuclear power, particularly fusion, through a comparative analysis of capacity factors, levelized cost of electricity, grid interconnection constraints, and deployment pathways. Unlike intermittent renewables, nuclear fission and fusion offer high-capacity-factor, firm baseload generation suited to AI training and inference workloads that require continuous, reliable power. Preliminary techno-economic analysis suggests that several Nth-of-a-kind fusion concepts, particularly magnetic confinement systems, may become cost-competitive with firmed renewable systems and advanced fission for hyperscale data center applications. Co-location of fusion plants with data centers further reduces transmission bottlenecks, improves resilience, and aligns with emerging hyperscaler procurement strategies. We also assess recent regulatory developments and argue that fusion's favorable safety profile and reduced waste burden improve its long-term social and political viability relative to fission. We conclude that fusion represents a strategically important pathway for sustainably powering next-generation computing infrastructure and should be prioritized in both policy and industrial deployment planning.
Multi-cloud adoption has widened the enterprise attack surface to a degree that perimeter-based defence can no longer address. Traffic is now flowing continuously across AWS, Azure, and GCP, and the majority of deployed Zero Trust Architecture (ZTA) systems are still using static rule tables, with no ability to provide an audit trail of the reasoning behind decisions, and with logs stored in datastores that can be modified by an insider without detection. This paper proposes ZT-ChainGuard, a framework that overcomes these three limitations in one architecture that integrates an ensemble machine learning trust-scoring engine, ZTA policy enforcement and a blockchain-based audit trail. The trust-scoring engine is a two-layer stacking ensemble, with XGBoost and Random Forest as base learners, and Logistic Regression as a meta-learner, and it returns a continuous trust score, P(Attack | flow), for each network flow, which is then used to trigger the ZT policy decision at a threshold of 0.5. The explanation of each decision is provided by SHAP values at both the global and per-flow level, and each decision is stored as an immutable, SHA-256 hash-chained block. On CICIDS2017 (2.83 million flows, 14 attack classes) the framework achieves 99.90% accuracy, 99.71% F1-score, and 99.99% ROC-AUC; on ToN-IoT (2.23 million IoT records, 9 attack types) it achieves 99.81% accuracy, 99.88% F1-score, and 100% ROC-AUC. The latency of inferences is 0.006ms per sample, and the overhead of auditing the blockchain is 0.019ms per block. This performance is not just a quirk of a particular split, as it is shown to be stable across the three folds of three-fold cross validation.
An increasing number of special Internet of Things (IoT) applications are being deployed within federated and zero-trust (ZT) environments. These ad-hoc networks consist of heterogeneous, resource-constrained devices from various administrative domains, all of which are susceptible to compromise. The dynamic nature of these environments necessitates near-real-time Situational Awareness (SA), where processed data varies with its sensitivity and reliability, without dependence on a central authority. Examples include NATO and non-NATO coalitions engaged in hybrid military operations or humanitarian aid scenarios. To address the challenges of security, reliability, and context-aware data dissemination, we propose FedM, a multi-level formal model designed for context-aware and policy-driven data dissemination in federated IoT environments. This model is built upon various access control models and Denning’s research on information flow control (IFC), prioritizing the protection and reliability of data flows. A crucial element of this model is the distributed ledger, which facilitates the dynamic modification of label expressiveness, enhances resilience against disruption attacks, and separates policy logic from application functionality to mitigate risks associated with the benevolent developer. Additionally, we delineate a deterministic and history- and precedence-aware policy enforcement procedure to resolve conflicting actions and introduce processing primitives for the ongoing Data Quality Assessment (DQA) process. Our model also aligns with the concepts of Ubiquitous and Continuum Computing. Furthermore, in our paper we illustrate a policy-based dissemination pipeline, incorporating a bounded trustworthiness dimension. Additionally, we present a refined multi-layered framework that proposes the deployment of Information Flow Control (IFC) components, such as the Open Policy Agent decision engine, to facilitate policy-driven contextual data dissemination. We provide preliminary benchmarks for resource-constrained platforms, along with a formal threat model that addresses implicit flows, the benevolent developer problem, and the behavior of a distributed ledger under degraded network conditions. Finally, we conduct a formal verification of our model using the P framework.
Layer 2 scaling solutions—including payment-channel-based Lightning Networksand rollup-based off-chain execution environments—are commonly understood aslinear scaling projects for blockchain transaction throughput. This paper proposesan alternative structural interpretation: the emergence of Layer 2 is not a continuous increase in system capacity, but a percolation phase transition that occurswhen the density of off-chain channels or cross-rollup connections crosses a critical threshold. During this phase transition, the system shifts from a fragmentedlocally connected state to a globally routable giant connected state. The paperanalyzes the Lightning Network and the rollup ecosystem as comparative cases.Empirical studies of the Lightning Network show that its scale-free topology forcescritical hub nodes to bear a disproportionate connection load, thereby binding thenetwork’s global connectivity to the survival of a few high-centrality nodes. Therollup ecosystem faces the structural predicament of liquidity fragmentation, and itsevolution toward cross-rollup interoperability likewise exhibits a phase-transitionlogic from quantitative change to qualitative change in network effects. Based onthe above analysis, this paper distills three design principles for Layer 2 scalability:facilitating the institutionalization of cross-domain connections, avoiding overlyhomogenized cognitive convergence, and implementing differentiated verificationrouting among tasks with different security requirements.
Hassan Cessi Ibrahim, Damilare Timothy Ogunjobi, Philip Mensah
The networks that run operational technology (OT) substations, water treatment plants, oil and gas pipelines, and manufacturing lines are moving from a centralized control to a federated, multi-stakeholder architecture coordinated by permissioned distributed ledgers. Protection and control loops in the electrical grid and other critical infrastructure have protection-relay tripping times, IEC 61850 GOOSE message classes, and SCADA/PMU polling cycles that impose multi-millisecond to sub-second deadlines on protection and control operations, while Byzantine fault-tolerant (BFT) consensus protocols like PBFT, Tendermint, HotStuff, and HoneyBadgerBFT were designed for settlement workloads that can tolerate hundreds of milliseconds to seconds of latency. In this paper, we survey four representative BFT families, discuss their structural latency and scalability constraints for OT deployment, and introduce a hybrid consensus algorithm called IsoBFT (Isochronous Byzantine Fault Tolerance), which combines an optimistic single-round-trip fast path with a PBFT-style fallback mechanism based on a network-stability monitor, and elects a small rotating committee using a verifiable random function (VRF). A formal system model, safety/liveness/termination proof, and security analysis for eight attack classes are provided, with a proposition quantifying the degradation of the practical availability of the safety guarantee when the global Byzantine fraction is approaching one-third. Using realistic Modbus/DNP3/IEC 61850 OT traffic, the discrete-event simulation of the design IsoBFT managed to execute realistic workloads with median consensus latency ranging from 4.90ms at n = 10-50 to 9.17-11.26ms at n = 100 and n = 500, remaining competitive with or better than PBFT and Tendermint across this range. Committee-bounded communication overhead stayed essentially flat with respect to the number of validators from n = 10 to n = 50, but newly completed runs at n = 100 and n = 500 (n = 200 still outstanding) show overhead growing faster than the quadratic scaling of PBFT and Tendermint over that range, together with a heavy P95/P99 latency tail not present at smaller scale; this discrepancy with the theoretical scale-independence result is reported and discussed rather than resolved. IsoBFT could reduce the median latency by approximately 81% and 56% under up to 33% Byzantine faults compared to HotStuff and HoneyBadgerBFT, respectively, at n = 10-50, while maintaining the safety of the system; a Byzantine-resilience sweep at n = 100 shows a narrower advantage over PBFT/Tendermint than at smaller scale.
Abstract Cross-chain bridges have become indispensable in frastructure for asset and data portability across heterogeneous blockchains, yet they remain the single most exploited category of decentralized-finance infrastructure, with cumulative publicly reported losses exceeding two billion US dollars since 2021. Existing literature addresses this problem from two largely disconnected angles: broad interoperability surveys that cat alogue protocol families, and narrower security studies that catalogue historical incidents. Neither strand provides a reusable, transparent instrument that lets a developer or auditor estimate a candidate bridge’s exposure to known attack classes before deployment, using only publicly observable design parameters. This paper addresses that gap. We conduct a PRISMA-guided systematic literature review of blockchain-interoperability and bridge-security research, from which we derive a unified tax onomy cross-tabulating bridge architectures, verification models, communication models, trust assumptions, and attack categories. Building on this taxonomy, we propose the Bridge Security Risk Score (BSRS), a rule-based, auditable scoring framework– deliberately not a machine-learning model– that maps seven publicly inspectable architectural parameters to a quantified risk estimate. We retrospectively validate BSRS against eight publicly documented bridge incidents (Ronin, Wormhole, Poly Network, Nomad, Harmony Horizon, Multichain, ChainSwap, Qubit) and two non-exploited comparators (LayerZero, Axelar), and find that the framework consistently assigns higher risk bands to the exploited designs. We discuss the limitations of retrospective vali dation, threats to validity, and directions for extending this work toward automated, PhD-level formal-verification and disclosure standardization research.
Q-PROOF is an experimental blockchain architecture based on adaptive consensus, aperiodic topology, and quadratic governance. The model integrates Aperiodic Consensus Relaxation (ACR), distributed reputation, Sybil attack defense, coordinated attack detection, and post-quantum migration pathways. This technical white paper outlines the core consensus engine, mathematical modeling of system tension, correlation-aware consensus mechanics, and benchmark comparisons demonstrating enhanced finality and resilience against coordinated network capture.
Distributed Ledger Technologies (DLTs) have turned out to be an underlying enabler of trust, security, and automation in the next-generation wireless networks (6G). Contrasting centralized control models, the DLTs offer decentralized coordination, record keeping which is immutable, and programmable logic, which is consistent with the ultra-dense and intelligent heterogeneous ecosystems of 6G. The paper has discussed the performance implications of incorporation of the SDLTs with 6G networks in blockchain, directed acyclic graph based ledger and hybrid DLT architectures. There was an integrated DLT-6G framework where cross-layer communication between radio access, core, edge computing, and distributed ledgers was highlighted. To model the latency of transactions, their throughput, energy usage, and consensus overhead were modeled based on the 6G communication characteristics including ultra-low latency, massive connectivity, and edge intelligence. A large-scale set of simulations was done to test the DLT-based network slicing, secure resource orchestration, and AI-assisted ledger management and compared the results to that of traditional non-DLT methods. The results have shown that lightweight and DAG-based DLTs were much more cost-effective in terms of confirmation delay and energy usage, whereas in dense 6G operation, hybrid designs were more scalable and dependable. Moreover, ledger management with the help of AI improved flexibility in changing the conditions of traffic and mobility.
The growing interconnectivity of industrial systems has intensified the need for secure, intelligent, and scalable data transfer mechanisms within Industrial Internet of Things (IIoT) environments. Despite rapid IIoT adoption, industrial data transfer remains vulnerable to high-volume, dynamic cyber anomalies and consensus-level attacks, while existing security mechanisms struggle to jointly deliver low-latency, scalable, and trustworthy communication under large-scale adversarial deployments. This study introduces a Secure Dual-Consensus Blockchain-Enabled Deep Learning Framework (SD-BDL) that unifies blockchain security and adaptive anomaly detection to ensure trustworthy and efficient IIoT communication. The framework employs a hybrid consensus mechanism, integrating Delegated Proof of Stake (DPoS) and Practical Byzantine Fault Tolerance (PBFT) to achieve enhanced fault tolerance, reduced latency, and protection against collusion and Sybil attacks. To address the dynamic and high-volume nature of IIoT data streams, a CNN–LSTM model is deployed for real-time anomaly detection, with hyperparameters optimized using the Adaptive Aquila Optimization (AAO) algorithm—identified as the most effective technique for achieving rapid convergence, high detection accuracy, and balanced exploration–exploitation. The proposed SD-BDL framework is evaluated on an IIoT dataset, incorporating preprocessing steps to mitigate class imbalance, missing values, and noise interference. Experimental outcomes demonstrate a significant improvement in performance metrics, achieving an R² score of 0.985, throughput enhancement of 25.2%, and latency reduction of 19.4% compared with benchmark models using PSO, GA, and Bayesian optimization. The hybrid consensus blockchain further ensures transaction integrity, tamper resistance, and low-energy overhead, validating its robustness under adversarial and large-scale deployment scenarios involving over 1,000 nodes. This research contributes a novel, energy-efficient, and scalable architecture for industrial data protection, setting a foundation for future integration with 6G-enabled IIoT systems, federated trust networks, and lightweight transformer-based threat detection frameworks.
CapIX is a decentralized physical infrastructure network (DePIN) designed to aggregate volatile, untrusted consumer-grade hardware and public container fleets into a single high-performance compute fabric. This technical report presents a practical dual-runtime execution model that addresses the core multi-tenant security challenges on untrusted permissionless hosts. On hardware-virtualization-capable bare-metal nodes, CapIX deploys lightweight micro-VMs isolated by hardware Trusted Execution Environments (TEEs). On standard unprivileged edge containers, it utilizes strict process-level sandboxing, syscall filtering allow-lists, and direct GPU passthrough to achieve native CUDA/ROCm execution performance. The system coordinates fleet routing using a transparent, multi-factor weighted geometric scoring engine resistant to economic manipulation. To achieve scalable, lightweight state verification without the prohibitive overhead of full zero-knowledge proofs on every job, the architecture introduces an interactive bisection fraud game that isolates deterministic instruction disputes to a single step for targeted zkVM proof generation, relying on probabilistic canary spot-checking for non-deterministic workloads.
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Security and Verification in Computing
Physical Unclonable Functions (PUFs) and Hardware Security
Thomas Bakaysa, Ahmet Kurt, Abdul-Salem Beibitkhan, J E Hernández Leon · 9 authors
Bitcoin's Lightning Network (LN) can be exploited as a covert, low-cost command-and-control (C&C) channel for botnets, as demonstrated by the LNBot and D-LNBot designs. However, both remain proof-of-concept prototypes evaluated only through simulation, leaving key questions about real-world topology formation, propagation complexity, and resilience to takedowns unanswered. We present LNTest, the first reusable testbed for LN-based botnets, built from Core Lightning nodes containerized with Docker over a shared Bitcoin Core regtest chain. LNTest supports three overlay topology modes (a deterministic chain, autonomous peer discovery, and user-supplied graphs), enabling controlled experiments across different botnet structures. Using LNTest, we report three main findings. First, D-LNBot's autonomous formation protocol does not produce the uniform chain from its design; instead, it creates a clustered chain in which cliques are linked by bridge nodes whose removal fragments the network. Second, command propagation scales linearly with botnet size ($Θ(n)$), not the $O(m \log n)$ previously claimed, and gains nothing from higher neighbor connectivity. Third, the overlay topology determines the effectiveness of takedown strategies: uniform-degree chains resist targeted removal but fragment under random failure, scale-free topologies show the opposite pattern, and the autonomous clustered chain is fragile under both, making it the most vulnerable of the three. LNTest is released as open source, with a script that reproduces all our experiments, to support reproducible research on LN-based botnet defenses.
Traditional distributed consensus mechanisms rely on probabilistic assumptions, economic weighting (Proof-of-Stake), or arbitrary computational work (Proof-of-Work) to secure ledger state transitions. These models leave the application layer inherently vulnerable to Man-in-the-Middle (MITM) attacks, Maximal Extractable Value (MEV) extraction, and semantic exploits against critical infrastructure (SCADA/PLC). This manuscript introduces Proof-of-Rigidity (PoR), a deterministic state-validation framework that locks the consensus machine within a continuous 150-decimal-place geometric manifold ($G_{24}$ volume space). The paper formalizes three core components: The Brittle Acceptance Predicate: A Coq-verified mathematical boundary that enforces an absolute $10^{-80}$ validation tolerance, structurally denying unauthorized state mutations. Mantissa Tail Parity (The MEV Sieve): A mechanism utilizing Canonical Decimal Arithmetic ($\mathbb{D}_{150}$) to mathematically neutralize routing interception and front-running. Capability-Constrained Semantic Policies: A bipartite matrix that structurally subordinates LLM-based ontological analysis to strict cryptographic Role-Based Access Control (RBAC), preventing adversarial paraphrasing against industrial endpoints. By enforcing strict geometric determinism, PoR transforms network security from probabilistic difficulty into mathematical brittleness. Included in this deposit are the Coq formal verification proofs, a Python reference implementation of the Layer-1 substrate, and a computational benchmarking harness demonstrating throughput scalability. LEGAL, ETHICAL, AND SAFE HARBOR DISCLAIMER The mathematical models, formal Coq proofs, and Python reference implementations contained within this deposit are published strictly for academic research, cryptographic peer review, and educational purposes. The architectures described herein represent a theoretical substrate and an experimental prototype. They have not undergone formal, independent security auditing for production deployment. No Warranty (As-Is): The mathematical models and reference code are provided "AS IS", without warranty of any kind, express or implied. The continuous geometric bounds and mechanisms detailed herein are theoretical thresholds; physical hardware limitations, truncation errors, or implementation flaws may affect real-world execution. Limitation of Liability: Under no circumstances shall the author, contributors, or affiliated research entities be held liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, loss of use, data, stablecoin assets, or profits; business interruption; or industrial infrastructure failure) arising in any way out of the use, deployment, or misconfiguration of this protocol. Assumption of Risk: Any entity choosing to implement the $G_{24}$ volume space boundaries, the Topological Shatter mechanics, or any variant of the PoR consensus layer within a live environment does so entirely at their own risk, and is solely responsible for ensuring compliance with all applicable cybersecurity and financial regulations.
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.
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 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.