Search Results (129)

As cloud computing proliferates, outsourced data faces severe security threats, yet existing searchable encryption (SE) schemes rely on classical hardness assumptions, centralized trust authorities, and static access control, leaving critical gaps in quantum resistance, single-point-of-failure prevention, and dynamic permission management. To address these limitations, we propose BL-ABSE, a blockchain-enhanced, lattice-based attribute-based searchable encryption framework. BL-ABSE employs the Ring Learning With Errors (RLWE) problem as its security foundation and applies the Number Theoretic Transform (NTT) to reduce polynomial multiplication from $O\left(n^2\right)$ to $O(nlogn)$. To eliminate single-point trust risks, the framework further integrates a $(t,n)$ threshold key protocol across an edge-node consortium governed by Practical Byzantine Fault Tolerance (PBFT) consensus. A smart-contract-maintained on-chain revocation list enables permission withdrawal via a single blockchain transaction without re-encryption. Experimental evaluation demonstrates that commitment generation requires approximately 23 ms at $n=1024$, search latency scales linearly at roughly 29 µs per record, and revocation completes in approximately 2 s regardless of system scale. Formal security proofs under the quantum polynomial-time (QPT) adversary model reduce six security properties—index indistinguishability, query privacy, threshold key security, Byzantine fault tolerance, audit immutability, and revocation immediacy—to the hardness of RLWE and the Short Integer Solution (SIS) problems. To the best of our knowledge, BL-ABSE is the first framework to simultaneously achieve post-quantum security, attribute-based access control, decentralized key management, instant revocation, and immutable auditing within a single unified framework. We further conduct threshold parameter verification, end-to-end revocation latency decomposition, blockchain throughput stress testing, search-pattern leakage quantification, and communication/storage overhead analysis, providing a comprehensive evaluation of both performance and security trade-offs. We explicitly characterize the search-pattern leakage inherent in the deterministic commitment design as a correctness–privacy trade-off and discuss mitigation directions. Full article

The classic design of the Practical Byzantine Fault Tolerance (PBFT) protocol relies on a centralized primary node, which not only creates a performance bottleneck but also introduces severe data censorship risks, threatening the data integrity and security of Edge Computing networks. To address this challenge, this paper proposes DC-PBFT (Decoupled PBFT), a censorship-resistant consensus protocol for Edge-Internet of Things (Edge-IoT) environments. The core innovation of DC-PBFT lies in the decoupling of the Proposer and Primary roles, supplemented by Verifiable Random Function (VRF)-based dynamic role rotation, which fundamentally eliminates the arbitrary power of a single node. Building on this, the protocol introduces a parallel group consensus mechanism: an elected Consensus Committee (CC) composed of Active Edge Nodes leads the consensus, while an independent Replica Network (RN) performs parallel validation. When a disagreement arises, the protocol triggers a global disagreement arbitration process involving all nodes to guarantee final consistency and attribute fault. To ensure long-term incentive compatibility, we also designed a hybrid election mechanism combining Proof-of-Stake and dynamic reputation, along with corresponding economic incentives and a tiered penalty system. Theoretical analysis proves that DC-PBFT satisfies Consistency and Liveness, and achieves strong censorship resistance guarantees. Simulation results demonstrate that DC-PBFT’s scalability significantly outperforms PBFT and RepChain; its reputation mechanism effectively improves long-term performance under sustained Byzantine attacks; and, compared to asynchronous censorship-resistant protocols like HoneyBadgerBFT, DC-PBFT achieves censorship resistance with over 45% lower transaction confirmation latency. Full article

Large-scale smart city Internet of Things (IoT) infrastructures must simultaneously provide strong cybersecurity protection, real-time anomaly detection, and energy-efficient operation despite the strict resource limitations of sensing devices. The current body of research typically addresses secure data management, edge intelligence, or energy optimization in isolation, leaving a practical gap in unified frameworks that jointly optimize these objectives. This paper proposes a jointly co-designed energy-aware cybersecurity framework that integrates lightweight secure sensing, hybrid edge-based anomaly detection, Practical Byzantine Fault Tolerance (PBFT)-enabled blockchain integrity, and Grey Wolf Optimization (GWO)-driven edge deployment within a single end-to-end architecture. The practical contribution of the proposed framework lies in enabling tamper-evident trusted sensing, real-time detection of both data and energy anomalies, and communication-efficient operation suitable for scalable smart city deployments. The simulation results demonstrate that the proposed method achieves strong operational efficiency, reaching up to 234.6 transactions per second while maintaining end-to-end latency of approximately 140–194 ms and reducing total energy consumption to about 1.68 J under high-load conditions. In addition, the hybrid anomaly detection mechanism achieves an F1-score of 0.985 and ROC-AUC of 0.992, confirming strong detection capability under realistic sensing and attack scenarios. Full article

While the practical Byzantine fault tolerance (PBFT) consensus algorithm provides excellent theoretical fault tolerance, its performance in practical blockchain applications is often constrained by high communication overhead, especially in scenarios with limited node resources and high mobility, such as Vehicular Ad hoc Networks (VANETs). To address these blockchain-specific limitations without sacrificing the fundamental safety guarantees against arbitrary Byzantine failures, this paper proposes a novel PBFT-optimized consensus algorithm based on a dynamic reputation tree (DRT-PBFT). First, to address the issue of limited storage resources, we propose a block synchronization method based on differentiated storage of reputation values. The lower-reputation nodes retain only “micro-blocks” that contain essential information of the complete block, while the higher-reputation nodes store and synchronize complete blocks, significantly reducing the storage overhead. Second, on the basis of the reputation values, we construct a tree communication topology from the leaf node layer in a bottom-up manner. Messages are transmitted from multiple child nodes to their parent node, resolving the problem of a single message source in the tree structure. Additionally, we optimize the consensus process, reducing the number of mutual communications between nodes to a linear level. Finally, to address the problem of malicious nodes in the tree structure, we introduce a dynamic reconstruction mechanism for the reputation tree. When child node messages are inconsistent, the parent node splits the child nodes to mitigate the influence of malicious nodes, enhancing both the security and scalability of the consensus process. The experimental results show that, compared with typical improved PBFT algorithms, the proposed algorithm improves the average throughput by 34.1% and reduces the average latency by 27.4%. Moreover, compared with the full replication block synchronization method, the differentiated storage method reduces the storage overhead by 26.3%, making it potentially more suitable for large-scale VANET scenarios. Full article

In increasingly interconnected tactical environments, cybersecurity, trust, and interoperability must evolve in tandem. Federated Coalition Networks (FCNs) enable multinational cooperation while preserving national sovereignty; however, the secure management of identities, policies, and configurations across coalition domains remains a critical challenge, particularly under adversarial and resource-constrained conditions. This paper proposes a blockchain-enabled management framework aligned with the defense-in-depth paradigm, focusing on management-plane functions such as policy enforcement, public key infrastructure (PKI) management, and auditable governance, rather than time-critical tactical communications. The solution relies on a permissioned blockchain architecture with Byzantine Fault Tolerant consensus, avoiding energy-intensive Proof-of-Work mechanisms and supporting operation under Disconnected, Intermittent, and Low-bandwidth (DIL) conditions. A coalition-level trust-and-governance model is introduced to prevent unilateral control while preserving national autonomy. A realistic use case and a proof-of-concept implementation demonstrate the feasibility of the approach, showing bounded latency, limited energy overhead, and sufficient throughput for FCN management. These results indicate that appropriately tailored blockchain solutions can effectively enhance resilience, trust, and compliance in federated defense networks. Full article

The integration of blockchain technology into Cyber–Physical Systems (CPS) offers decentralized resilience against data manipulation. This also introduces stochastic consensus latencies that threaten real-time control stability. We present a Stochastic-Aware Blockchain Predictive Control (SAB-PC) framework, which models blockchain-induced jitter as a state-dependent Markovian process, and embeds it within a Markovian Jump Linear System (MJLS) formulation. Using mode-dependent Linear Matrix Inequalities (LMIs), we derive Mean Square Stability (MSS) conditions, which capture the interaction between decentralized consensus dynamics and closed-loop control behavior. The framework is validated on the Tennessee Eastman Process (TEP) benchmark, using a calibrated stochastic delay model that reflects realistic blockchain congestion patterns. Our results show that standard blockchain-mediated control architectures become unstable under Practical Byzantine Fault Tolerance (PBFT)-induced quadratic latency growth, whereas SAB-PC maintains stable operation across decentralized networks up to 60 validator nodes. The predictive Safety Runway effectively masks long-tail delay distributions, ensuring real-time feasibility and preserving safe Reactor Pressure trajectories. Under coordinated False Data Injection (FDI) attacks, SAB-PC limits pressure deviations to only 1.2 kPa despite an 8.0 kPa adversarial bias, demonstrating cryptographic and control-theoretic resilience. Full article

Byzantine fault tolerance (BFT) protocols serve as the cornerstone of data consistency in permissioned blockchains; however, their scalability is inherently constrained by stochastic leader-centric bottlenecks and rigid, non-adaptive timeout mechanisms. Existing rule-based heuristics often fail to capture high-entropy and time-varying network latency, leading to frequent view changes and severe performance degradation under network volatility. To mitigate this epistemic uncertainty, this paper proposes TimesNet-BFT, a novel entropy-aware optimization framework. By leveraging TimesNet’s transformation of one-dimensional time series into two-dimensional tensors for multi-periodicity analysis, the framework accurately characterizes stochastic nodal latency patterns to facilitate entropy-minimized dynamic leader election and adaptive timeout strategies. Extensive evaluations conducted on simulated and real-world trace-driven Internet of Vehicles (IoV) scenarios validate the proposed approach, achieving a prediction MAPE below 5% alongside robust zero-shot generalization. Notably, under high-entropy network conditions, the framework demonstrates up to a 191.9% increase in throughput and mitigates latency variance by 73.3%, effectively neutralizing the structural bottlenecks inherent to traditional information-agnostic protocols. Crucially, by mathematically decoupling consensus safety from AI prediction errors, the system introduces an aggressive liveness paradigm that maintains minimal control plane overhead while significantly enhancing the entropic stability of the consensus process. Full article

As network attack methods continue to evolve, flooding attacks remain a major threat that causes network paralysis and service disruption. Statically configured systems are particularly vulnerable, as attackers can exploit reconnaissance information to launch large-scale attacks, while conventional defense mechanisms often fail under high-intensity traffic. To address this problem, this paper introduces Moving Target Defense (MTD) within a decentralized framework and proposes a blockchain-based decentralized End Hopping system. The system employs the Practical Byzantine Fault Tolerance (PBFT) consensus protocol for dynamic controller election and incorporates a disaster recovery mechanism, which eliminates single points of failure while ensuring reliable controller transitions and rapid service restoration. Experimental results demonstrate that the proposed system achieves satisfactory performance in terms of availability, effectiveness, and security, providing a practical approach to constructing robust proactive defense networks. Full article

With the rapid development of the Internet of Vehicles (IoV), the explosive growth of data traffic within the system has led to a surge in demand for spectrum resources. However, the strict limitations on spectrum supply make the construction of an efficient and reasonable resource allocation scheme crucial for IoV. To maximize social benefits and improve security in the resource allocation process under IoV spectrum scarcity, this paper proposes a dynamic spectrum allocation (DSA) scheme based on a consortium blockchain framework. In this scheme, we design a demand-based vehicle priority classification method and propose a novel hybrid consensus mechanism—PhDPoR—which integrates practical byzantine fault tolerance (PBFT) and Hierarchical Delegated Proof of Reputation. Furthermore, we construct a multi-leader, multi-follower (MLMF) Stackelberg game model and utilize smart contracts to implement an immutable on-chain record of spectrum resource allocation, thereby deriving the optimal spectrum pricing and purchase strategy. Experimental results show that the proposed scheme not only effectively optimizes the utility of both supply and demand sides and improves overall social benefits while ensuring efficiency, but also significantly outperforms baseline algorithms in identifying and mitigating malicious nodes, thus verifying its feasibility and application value in complex IoV environments. Full article

The entire system collapses due to the issues of inadequate centralized storage capacity, poor scalability, low storage efficiency, and susceptibility to single point of failure brought on by huge power consumption data in the smart grid; thus, an alliance chain-driven multi-cloud storage and verifiable deletion method for smart grid data is proposed. By leveraging the synergy between alliance blockchain and multi-cloud architecture, the encrypted power data originating from edge nodes is dispersed across a decentralized multi-cloud infrastructure, which effectively mitigates the danger of data loss resulting from single-point failures or malicious intrusions. The removal of expired and user-defined data is guaranteed through a transaction deletion algorithm integrated into the indexed storage deletion chain and strengthens the flexibility and security of the storage architecture. Based on the Practical Byzantine Fault-Tolerant Consensus Protocol with Ultra-Low Storage Overhead (ULS-PBFT), by the hierarchical grouping of nodes, the system communication overhead and storage overhead are reduced. Security analysis proves that the scheme can resist tampering attacks, impersonation attacks, collusion attacks, double spend attacks, and replay attacks. Performance evaluation shows that the scheme improves compared to similar methods. Full article

Dynamic heterogeneous redundancy (DHR) architectures combine heterogeneity, redundancy, and dynamism to create security-centric frameworks that can be used to mitigate network attacks that exploit unknown vulnerabilities. However, conventional DHR architectures rely on centralized control modules for scheduling and adjudication, leading to significant single-point failure risks and trust bottlenecks that severely limit their deployment in security-critical scenarios. To address these challenges, this paper proposes a decentralized DHR architecture based on the Raft consensus algorithm. It deeply integrates the Raft consensus mechanism with the DHR execution layer to build a consensus-centric control plane and designs a dual-log pipeline to ensure all security-critical decisions are executed only after global consistency via Raft. Furthermore, we define a multi-dimensional attacker model—covering external, internal executor, internal node, and collaborative Byzantine adversaries—to analyze the security properties and explicit defense boundaries of the architecture under Raft’s crash-fault-tolerant assumptions. To assess the effectiveness of the proposed architecture, a prototype consisting of five heterogeneous nodes was developed for thorough evaluation. The experimental results show that, for non-Byzantine external and internal attacks, the architecture achieves high detection and isolation rates, maintains high availability, and ensures state consistency among non-malicious nodes. For stress tests in which a minority of nodes exhibit Byzantine-like behavior, our prototype preserves log consistency and prevents incorrect state commitments; however, we explicitly treat these as empirical observations under a restricted adversary rather than a general Byzantine fault tolerance guarantee. Performance testing revealed that the system exhibits strong security resilience in attack scenarios, with manageable performance overhead. Instead of turning Raft into a Byzantine-fault-tolerant consensus protocol, the proposed architecture preserves Raft’s crash-fault-tolerant guarantees at the consensus layer and achieves Byzantine-resilient behavior at the execution layer through heterogeneous redundant executors and majority-hash validation. To support evaluation during peer review, we provide a runnable prototype package containing Docker-based deployment scripts, pre-built heterogeneous executors, and Raft control-plane images, enabling reviewers to observe and assess the representative architectural behaviors of the system under controlled configurations without exposing the internal source code. The complete implementation will be made available after acceptance in accordance with institutional IP requirements, without affecting the scope or validity of the current evaluation. Full article

As supply chains expand in scale and the number of participating nodes increases, existing consensus algorithms are increasingly showing limitations in scalability, communication complexity, and handling complex network environments. To address the shortcomings of blockchain consensus mechanisms in master node selection, scalability, and communication complexity in supply chain traceability scenarios, this paper proposes a blockchain hybrid consensus optimization algorithm named Node Rating-Based and Grouping Raft cluster Practical Byzantine Fault Tolerance (NG-RPBFT) for supply chain traceability. This algorithm builds a multi-index comprehensive rating model for nodes to comprehensively evaluate consensus nodes, reasonably groups consensus nodes, adopts an inter-group and intra-group dual consensus mechanism to achieve efficient data synchronization, and introduces Brotli data compression technology to optimize message load, effectively enhancing system performance. Experimental results confirm that this algorithm significantly improves the scalability of the consensus mechanism and performs exceptionally well in consensus efficiency, making it suitable for complex application scenarios such as supply chain traceability under CPS scenarios. Full article

With the rise of computation-intensive and latency-sensitive applications in the Internet of Vehicles (IoV), vehicles face increasing computational pressure. Computation offloading has become a key strategy for enhancing processing capabilities. Meanwhile, growing IoV data traffic raises security and reliability concerns. Existing blockchain-based solutions secure data transmission but overlook added delay and energy costs, increasing overall system cost. To address this issue, a blockchain-assisted computation offloading strategy based on Twin Delayed Deterministic Policy Gradient (TD3) (B-COTD) is proposed. Specifically, the offloading strategy selection is formulated as a multi-objective optimization problem considering latency, energy consumption, and blockchain costs, with the Delegated Byzantine Fault Tolerance (DBFT) algorithm ensuring the security of the offloading process. The TD3 algorithm solves this optimization problem, achieving efficient task offloading. Extensive experiments show that B-COTD improves overall performance, with the total system cost reduced by approximately 23.89% on average and the offloading success rate increased by about 11.02%. Full article

Blockchain systems have been widely adopted in today’s society, with consensus algorithms serving as their core component to ensure all participants in the network agree on a specific data state. Existing consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and the Practical Byzantine Fault-Tolerant Algorithm (PBFT) exhibit certain limitations in terms of scalability, security, and efficiency. To address these limitations, this paper proposes a novel Network-based Reputation Consensus (NRC) algorithm. The main research contributions of this work include the following: (1) An intelligent grouping mechanism that dynamically groups nodes based on network awareness, forming consensus groups with low internal latency and high bandwidth utilization, significantly reducing intra-group communication overhead. (2) A dynamic reputation system incorporating a “diminishing returns” reward function and a “multiplicative penalty” mechanism, effectively incentivizing honest node participation while preventing power monopoly. (3) A two-phase model of “intra-group BFT consensus + global communication committee ordering” that decomposes complex global consensus into parallel intra-group processing and coordination among a small set of elite nodes, thereby drastically improving efficiency. (4) Comprehensive simulations comparing the NRC algorithm with mainstream consensus algorithms, demonstrating its superior performance in communication overhead, throughput, latency, and tolerance to malicious nodes, thereby laying the foundation for large-scale applications. Full article

Multi-robot systems are increasingly deployed in critical applications such as search and rescue, precision agriculture, and autonomous transportation. However, the presence of Byzantine robots—agents that intentionally transmit false or misleading information—can severely compromise mission success and system safety, highlighting the urgent need for robust fault-tolerant coordination mechanisms. To address the challenge of Byzantine faults in multi-robot systems, we propose a novel approach utilizing a blockchain-based framework, termed RobotOBchain (Robot Observation Blockchain). RobotOBchain permanently records each robot’s own state information and its observed neighboring robots’ states at every time step. By leveraging smart contracts encoded within the blockchain, our method automatically detects state inconsistencies or conflicts among recorded observations, enabling early identification of intentionally deceptive Byzantine robots. Experimental validation demonstrates that RobotOBchain achieves 100% consistent Byzantine identification across all robots, maintains estimation errors within 3% of ground-truth, and exhibits robust tolerance to up to 50% malicious agents. These results significantly surpass the performance of classical W-MSR algorithms, while eliminating the dependency on predefined fault bounds. The framework’s demonstrated capabilities indicate strong potential for practical deployment in dynamic and safety-critical multi-robot applications. Full article