Journal of Network and Computer Applications最新文献_第2页

Dynamic dependency-aware vulnerability and patch management for critical interconnected systems 关键互联系统的动态依赖感知漏洞和补丁管理

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2026-01-27 DOI: 10.1016/j.jnca.2026.104436

Umar Sa’ad , Woongsoo Na , Nhu-Ngoc Dao , Sungrae Cho

Critical infrastructure systems characterized by complex interdependencies face significant challenges in vulnerability management due to cascading risk propagation through interconnected components. Traditional approaches that individually prioritize vulnerabilities inefficiently manage these dependency structures, leading to suboptimal security outcomes. This paper introduces an adaptive dependency-aware patching technique (ADAPT), a dynamic vulnerability and patch management framework that integrates formal dependency modeling with reinforcement learning to optimize patching strategies for critical interconnected systems. The proposed approach employs a mathematical formulation to capture direct and transitive dependencies via reachability matrices, enabling precise quantification of cascading risk propagation. The framework dynamically adapts patching decisions under resource constraints using proximal policy optimization within a constrained Markov decision process formulation. Comprehensive evaluation across 954 system configurations and six baseline strategies demonstrates consistent performance improvements, with 5.5% advantage over state-of-the-art NSGA-II multi-objective optimization while achieving 1513× computational speedup. Optimality gap analysis reveals 4.33% average deviation from theoretical bounds, validating the framework’s near-optimal solution quality. A critical infrastructure case study confirms practical applicability, with ADAPT achieving 89.7% risk reduction compared to 86.4% for sophisticated baseline methods, while enabling real-time decision-making through sub-second computation times. The results demonstrate superior performance under high dependency density and resource constraints, highlighting the framework’s suitability for environments where cascading failures pose operational threats.

具有复杂相互依赖关系的关键基础设施系统由于风险通过互联组件级联传播，在漏洞管理方面面临重大挑战。单独对漏洞进行优先级排序的传统方法无法有效地管理这些依赖关系结构，从而导致次优的安全性结果。本文介绍了一种自适应依赖感知补丁技术（ADAPT），这是一种动态漏洞和补丁管理框架，它将形式化依赖建模与强化学习相结合，以优化关键互联系统的补丁策略。所提出的方法采用数学公式通过可达性矩阵捕获直接和传递的依赖关系，从而实现级联风险传播的精确量化。该框架利用约束马尔可夫决策过程公式中的近端策略优化来动态适应资源约束下的补丁决策。对954个系统配置和6个基线策略的综合评估显示了一致的性能改进，在实现1,513×computational加速的同时，比最先进的NSGA-II多目标优化优势5.5%。最优性差距分析显示，与理论边界的平均偏差为4.33%，验证了框架的近最优解质量。关键基础设施案例研究证实了其实际适用性，与复杂基线方法相比，ADAPT的风险降低率为89.7%，同时通过亚秒级的计算时间实现实时决策。结果显示了在高依赖密度和资源约束下的卓越性能，突出了框架对级联故障构成操作威胁的环境的适用性。

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引用次数: 0

ILAD: A hardware-efficient authenticated encryption scheme for VANET applications based on Ascon ILAD：基于Ascon的VANET应用程序的硬件高效身份验证加密方案

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2026-01-22 DOI: 10.1016/j.jnca.2026.104428

Jiali Tang, Lang Li, Xingqi Yue

The rapid expansion of Vehicular Ad Hoc Networks (VANETs) has intensified the need for secure and efficient communication protocols, particularly in resource-constrained environments. Conventional encryption schemes offer strong security but frequently underperform in terms of computational efficiency and resource utilization due to their high complexity and resource demands. Ascon, a widely adopted lightweight encryption algorithm, provides robust security but poses challenges in hardware optimization. This paper introduces ILAD, a hardware-efficient authenticated encryption algorithm specifically designed for VANET applications. ILAD improves security and reduces resource overhead by optimizing key components within the round function and restructuring the sponge construction to enhance module reusability and hardware efficiency. The S-box is constructed using a Tent map-based chaotic system and refined through iterative optimization to ensure strong cryptographic properties and minimal resource usage, implemented with area-saving MOAI1 logic gates. Experimental results demonstrate that ILAD achieves a 36.1% reduction in S-box area and a 15.2% reduction in total area under UMC

0.18 μ m

process technology. The algorithm has been successfully deployed on the i.MX6ULL_PRO development board, a Cortex-A7-based low-power processor platform, where it achieved stable performance with low latency and energy consumption. Comprehensive security evaluations confirm ILAD’s robustness against various cryptanalytic attacks, making it a strong candidate for secure and lightweight VANET deployments.

车辆自组织网络（vanet）的迅速扩展，加强了对安全和高效通信协议的需求，特别是在资源受限的环境中。传统的加密方案具有较强的安全性，但由于其较高的复杂性和资源需求，在计算效率和资源利用率方面往往表现不佳。Ascon是一种被广泛采用的轻量级加密算法，它提供了强大的安全性，但在硬件优化方面提出了挑战。本文介绍了一种专为VANET应用而设计的硬件高效认证加密算法ILAD。ILAD通过优化round功能内的关键组件，重构海绵结构，提高模块的可重用性和硬件效率，提高了安全性，降低了资源开销。S-box使用基于Tent映射的混沌系统构建，并通过迭代优化进行改进，以确保强大的密码特性和最小的资源使用，并使用节省面积的MOAI1逻辑门实现。实验结果表明，在UMC 0.18μm工艺下，ILAD的s盒面积减小了36.1%，总面积减小了15.2%。该算法已成功部署在基于cortex - a7的低功耗处理器平台i.MX6ULL_PRO开发板上，实现了低延迟和低能耗的稳定性能。全面的安全评估证实了ILAD对各种密码分析攻击的稳健性，使其成为安全和轻量级VANET部署的有力候选者。

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引用次数: 0

Redefining resilience: A hybrid quantum-fuzzy Deep Q-Network paradigm for perpetual wireless rechargeable sensor networks 重新定义弹性：用于永久无线可充电传感器网络的混合量子模糊深度q -网络范式

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2026-01-20 DOI: 10.1016/j.jnca.2026.104427

Riya Goyal, Abhinav Tomar

Wireless Rechargeable Sensor Networks (WRSNs) offer a transformative solution to energy constraints in remote and mission-critical Internet of Things (IoT) environments by leveraging Wireless Energy Transfer (WET). However, current state-of-the-art approaches often suffer from limited scalability, inefficient energy scheduling, and inadequate adaptability to dynamic network states. Key challenges such as optimal mobile charger (MC) deployment, cooperative multi-agent scheduling, and intelligent threshold determination for on-demand charging remain insufficiently addressed—particularly in large-scale, real-time WRSNs. This paper proposes a novel hybrid framework that integrates Deep Q-Networks (DQNs) with a Quantum-Inspired Fuzzy Logic (QIFL) model for resilient and perpetual energy replenishment. To overcome spatial and load imbalances, an Enhanced Black Hole Optimization (EBHO) technique is used for partitioning the network and deploying MCs optimally. Unlike prior work, the proposed approach dynamically adapts charging thresholds using QIFL, capturing nonlinear energy consumption patterns and spatial heterogeneity. A multi-agent DQN model is deployed to handle high-dimensional state–action spaces, facilitating decentralized decision-making under uncertainty. Further, a proximity-aware charging mechanism, empowered by an improved Adaptive Genetic Algorithm (AGA), ensures real-time task redistribution among MCs, maintaining network longevity and zero sensor node failure. Experimental results demonstrate a 19.59% improvement in energy utilization and complete elimination of dead nodes compared to leading benchmarks, establishing the superiority of the proposed scheme for large-scale, adaptive, and sustainable WRSN operations.

无线可充电传感器网络（WRSNs）通过利用无线能量传输（WET）技术，为远程和关键任务物联网（IoT）环境中的能源限制提供了一种变革性解决方案。然而，目前最先进的方法往往存在可扩展性有限、能量调度效率低下以及对动态网络状态适应性不足的问题。诸如优化移动充电器（MC）部署、协作式多智能体调度和按需充电的智能阈值确定等关键挑战仍然没有得到充分解决，特别是在大规模、实时的WRSNs中。本文提出了一种新的混合框架，该框架将深度q网络（DQNs）与量子启发模糊逻辑（QIFL）模型相结合，用于弹性和永久能量补充。为了克服空间和负载不平衡，采用增强黑洞优化（EBHO）技术对网络进行分区和优化部署。与之前的工作不同，该方法使用QIFL动态调整充电阈值，捕捉非线性能量消耗模式和空间异质性。采用多智能体DQN模型处理高维状态-动作空间，促进不确定情况下的分散决策。此外，通过改进的自适应遗传算法（AGA），就近感知收费机制确保了mc之间的实时任务重新分配，保持了网络寿命和零传感器节点故障。实验结果表明，与领先基准相比，该方案在能源利用率和完全消除死节点方面提高了19.59%，证明了该方案在大规模、自适应和可持续WRSN运行方面的优势。

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引用次数: 0

The DPC-LSTM-MD scheme for detecting selective forwarding attack under variable environment in event-driven wireless sensor networks 事件驱动无线传感器网络中可变环境下选择性转发攻击检测的DPC-LSTM-MD方案

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-12-29 DOI: 10.1016/j.jnca.2025.104411

Yilun Ma, Yuanming Wu

In an event-driven wireless sensor network (EWSN), events occur randomly, prompting sensor nodes within the event area to detect and transmit data packets to a sink via router nodes (RNs) through multi-hop communication. Some RNs, referred to as malicious nodes, may launch selective forwarding attacks by selectively dropping part or all of the received packets. Additionally, harsh environmental conditions can degrade channel quality, sometimes forcing RNs to abandon forwarding data packets. Under these conditions, it becomes challenging to distinguish malicious nodes from normal nodes solely based on their packet forwarding rates. To address this issue, we propose the DPC-LSTM-MD scheme to detect selective forwarding attacks. This approach utilizes the time series of nodes’ packet forwarding behaviors as a dataset. The Density Peaks Clustering (DPC) method is employed to extract features representative of normal node behavior. Subsequently, a Long Short-Term Memory (LSTM) network predicts the single round forwarding rate (SFR) of nodes in the next time series interval. Based on the prediction error, we apply the minimum density (MD) method combined with the 3-sigma rule to identify and isolate malicious nodes. Our results demonstrate that the DPC-LSTM-MD scheme achieves a low false detection rate (FDR) of 2% and a low missed detection rate (MDR) of 3%, significantly improving network throughput.

在事件驱动的无线传感器网络（EWSN）中，事件是随机发生的，事件区域内的传感器节点通过多跳通信的方式检测数据包，并通过路由器节点（RNs）将数据包发送到sink。有些rn被称为恶意节点，可能会选择性地丢弃部分或全部接收到的报文，从而发起选择性转发攻击。此外，恶劣的环境条件会降低信道质量，有时会迫使RNs放弃转发数据包。在这种情况下，仅根据报文转发速率来区分恶意节点和正常节点变得很有挑战性。为了解决这个问题，我们提出了DPC-LSTM-MD方案来检测选择性转发攻击。该方法利用节点数据包转发行为的时间序列作为数据集。采用密度峰聚类（DPC）方法提取正常节点行为的特征。随后，LSTM （Long - short - Memory）网络预测下一个时间序列间隔内节点的单轮转发速率（SFR）。基于预测误差，我们采用最小密度（MD）方法结合3-sigma规则来识别和隔离恶意节点。我们的研究结果表明，DPC-LSTM-MD方案实现了2%的低误检率（FDR）和3%的低漏检率（MDR），显著提高了网络吞吐量。

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引用次数: 0

A piecewise chaotic starfish optimization algorithm for energy-efficient coverage in wireless sensor networks 一种用于无线传感器网络节能覆盖的分段混沌海星优化算法

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-12-25 DOI: 10.1016/j.jnca.2025.104410

Muhammad Suhail Shaikh , Shuwei Qiu , Xiaoqing Dong , Chang Wang , Wulfran Fendzi Mbasso

Enhancing coverage and reducing energy consumption are fundamental challenges in wireless sensor networks (WSNs) for high-volume and data-intensive deployment. WSNs play an important role in emerging technologies and face practical limitations, particularly related to coverage and energy consumption. Strategical placement of these sensor nodes is important to ensure service quality; however, many existing optimization algorithms for sensor node placement struggle with low coverage rate and high energy consumption. A significant issue lies in determining the optimal sensor node locations, as these significantly influence the network's coverage and energy consumption. This work presented a Piecewise Chaotic Starfish Optimization Algorithm (CSFOA) for addressing the challenge of optimizing the sensor node placement to maximize coverage and minimize energy consumption in WSNs. The integration of the piecewise chaotic map enhances the convergence and exploration capacity of the algorithm in identifying better solutions. The effectiveness of CSFOA is confirmed by a range of diverse benchmark functions as unimodal, multimodal, fixed, and variable, proving its excellence in optimization performance. CSFOA obtained better results for sensor node deployment in real test cases. For instance, in Test System 1 with 20 nodes, the coverage rate is 97.4757 % and the energy consumption is 0.29967 nJ/bit. In Test System 2 with 30 nodes, the coverage is 99.9713 % and the energy consumption is 3.2193 nJ/bit. Test System 3 with 40 nodes has a 98.8690 % coverage rate and energy consumption of 5.1107 nJ/bit. Compared to CMFO, CSSA, CPSO, SFOA, MFO, SSA, and PSO algorithms, CSFOA realizes an average improvement of 16.41 %, 5.36 %, 3.45 %, 2.371 %, 2.80 %, and 2.18 % on various evaluation metrics. These results underscore the algorithm's capability in balancing coverage and energy efficiency enhancement, and they confirm the algorithm's value as a more effective solution to sensor node deployment issues in different applications.

增强覆盖和降低能耗是无线传感器网络（wsn）在大容量和数据密集型部署中的基本挑战。无线传感器网络在新兴技术中发挥着重要作用，但也面临着实际的限制，特别是在覆盖和能耗方面。这些传感器节点的战略布局对于确保服务质量非常重要；然而，现有的传感器节点布局优化算法存在着低覆盖率和高能耗的问题。一个重要的问题在于确定传感器节点的最佳位置，因为这些位置会显著影响网络的覆盖范围和能耗。本文提出了一种分段混沌海星优化算法（CSFOA），用于解决优化传感器节点位置以最大化覆盖范围和最小化能量消耗的挑战。分段混沌映射的集成增强了算法的收敛性和探索能力，从而识别出更好的解。通过单峰、多峰、固定和可变等多种基准函数，验证了CSFOA的有效性，证明了其优化性能的优越性。在实际的测试用例中，CSFOA获得了更好的传感器节点部署结果。例如，在20个节点的测试系统1中，覆盖率为97.4757%，能耗为0.29967 nJ/bit。在30个节点的测试系统2中，覆盖率为99.9713%，能耗为3.2193 nJ/bit。测试系统3有40个节点，覆盖率为98.8690%，能耗为5.1107 nJ/bit。与CMFO、CSSA、CPSO、SFOA、MFO、SSA和PSO算法相比，CSFOA算法在各项评价指标上平均提高了16.41%、5.36%、3.45%、2.371%、2.80%和2.18%。这些结果强调了该算法在平衡覆盖范围和提高能效方面的能力，并证实了该算法作为不同应用中传感器节点部署问题的更有效解决方案的价值。

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引用次数: 0

DACS: Distributed adjustable computation scheme in highly scalable data center networks based on multi-protection routing DACS：基于多保护路由的高可扩展数据中心网络分布式可调计算方案

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-12-24 DOI: 10.1016/j.jnca.2025.104425

Wanling Lin , Jou-Ming Chang , Xiao-Yan Li

In data center networks (DCNs), many value-added services involving multiple tenants and distributed sites can be naturally modeled as multi-party communication (MPC) processes, where participants exchange information across infrastructures to support collaborative computation. For MPC, it usually ensures that the private data of the multiple parties involved in the collaborative computation are safe, the computational results maintain acceptable accuracy, and all participants are in the same fair position in a distributed environment. This article considers an unexplored application based on MPC called the distributed adjustable computation scheme (DACS), which allows computation to be invoked when the collected data reaches a specified threshold in the communication. We developed a distributed algorithm using secure multi-protection routing to enable DACS. The proposed algorithm guarantees that each private data can be successfully delivered to the desired recipient even if any faulty component (including server or link) exists in the network. Also, no other than the destination can receive the complete private data. We implement DACS on highly scalable data center networks. Through simulation, experimental results show that DACS is highly reliable and achieves high security efficiency.

在数据中心网络（dcn）中，许多涉及多个租户和分布式站点的增值服务可以自然地建模为多方通信（MPC）过程，其中参与者跨基础设施交换信息以支持协作计算。对于MPC来说，通常要保证参与协同计算的多方的私有数据是安全的，计算结果保持可接受的准确性，并且在分布式环境中所有参与者都处于同样公平的地位。本文考虑一种未开发的基于MPC的应用程序，称为分布式可调计算方案（DACS），它允许在收集的数据达到通信中的指定阈值时调用计算。我们开发了一种使用安全多保护路由的分布式算法来实现DACS。该算法保证了即使网络中存在任何故障组件（包括服务器或链路），每个私有数据也能成功地传递到期望的接收方。此外，只有目的地才能接收到完整的私有数据。我们在高度可扩展的数据中心网络上实现DACS。仿真实验结果表明，DACS具有较高的可靠性和安全效率。

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引用次数: 0

Smart parking optimization with software defined networking and blockchain: SPOSChain 基于软件定义网络和b区块链：SPOSChain的智能停车优化

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-12-22 DOI: 10.1016/j.jnca.2025.104414

Huseyin Ozgur Kamali , Ali Berkay Gorgulu , Murat Karakus , Evrim Guler , Suleyman Uludag

The growing pressures of urbanization, vehicular proliferation, and fragmented parking infrastructure pose significant sustainability and mobility challenges in modern cities. In response, we present SPOSChain (Smart Parking Optimization with SDN and Blockchain), a novel Blockchain-enhanced and Software-Defined Networking (SDN)-based smart parking system that unifies independent parking providers under a decentralized, intelligent coordination framework. SPOSChain introduces a four-layer architecture integrating IoT, data, control, and blockchain layers, to ensure transparency, scalability, and real-time responsiveness. The core parking assignment task is formulated as a fairness-driven optimization problem, which is mathematically equivalent to a parallel job scheduling problem, known to be NP-hard, thereby necessitating the development of efficient heuristic strategies. To this end, we propose, adopt, and evaluate multiple heuristic and hybrid algorithms, including Local Search, Branch-and-Bound, and Genetic Search, culminating in a time-aware Hybrid Search model. Simulation results under diverse vehicle arrival distributions (uniform, normal, and exponential) demonstrate that our approach significantly reduces load imbalance, quantified via the Total of Differences metric, while improving responsiveness and maintaining scalability. SPOSChain not only enables equitable and efficient parking allocation but also supports sustainable urban mobility by reducing driver search time, CO

_{2}

emissions, and network overhead. These results underscore the transformative potential of programmable, decentralized parking systems in future smart city infrastructures.

城市化、车辆激增和零散的停车基础设施带来的日益增长的压力，对现代城市的可持续性和流动性构成了重大挑战。作为回应，我们提出了SPOSChain（基于SDN和区块链的智能停车优化），这是一种新型的基于区块链增强和软件定义网络（SDN）的智能停车系统，它将独立的停车提供商统一在一个分散的智能协调框架下。SPOSChain引入了集成物联网、数据、控制和区块链层的四层架构，以确保透明度、可扩展性和实时响应能力。核心停车分配任务是一个公平驱动的优化问题，它在数学上相当于一个并行作业调度问题，被称为NP-hard，因此需要开发有效的启发式策略。为此，我们提出、采用并评估了多种启发式和混合算法，包括局部搜索、分支定界和遗传搜索，最终形成了具有时间意识的混合搜索模型。不同车辆到达分布（均匀分布、正态分布和指数分布）下的仿真结果表明，我们的方法显著降低了负载不平衡（通过Total of Differences度量进行量化），同时提高了响应能力并保持了可扩展性。SPOSChain不仅能实现公平高效的停车分配，还能通过减少驾驶员搜索时间、二氧化碳排放和网络开销来支持可持续的城市交通。这些结果强调了可编程的、分散的停车系统在未来智慧城市基础设施中的变革潜力。

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引用次数: 0

Multi-objective parrot optimizer with improved Lévy flight and adaptive elliptical segmentation - based screening mechanism for layout optimization of wireless sensor networks 基于改进lsamvy飞行和自适应椭圆分割筛选机制的多目标鹦鹉优化器无线传感器网络布局优化机制

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-12-22 DOI: 10.1016/j.jnca.2025.104413

Yun-Hao Zhang, Jie-Sheng Wang, Yu-Xuan Xing, Yu-Feng Sun, Si-Wen Zhang, Xue-Lian Bai

With the rapid development of science and technology, wireless sensor networks (WSN) are increasingly applied in environmental monitoring, industrial control, and smart cities. However, WSN deployment faces three core challenges that existing algorithms fail to address comprehensively. (1) Insufficient coverage precision. Traditional optimization algorithms (e.g., NSGA-II, MOPSO) often leave local coverage holes due to limited fine-grained search capability. (2) High resource redundancy. Fixed grid or weight-based methods (e.g., MOEA/D) cannot dynamically adjust node distribution according to environmental density, leading to redundant deployment. (3) Unbalanced energy consumption. Single-objective or simplified multi-objective approaches ignore the trade-off between coverage, waste rate, and energy consumption, shortening network lifetime. To tackle these issues, a multi-objective parrot optimizer (MOPO) based on improved Lévy flight and an adaptive elliptical segmentation screening mechanism is proposed for WSN deployment optimization. The randomness of original Lévy flight causes large step-length jumps, making fine-grained searches difficult. Thus, a Sigmoid perturbation mechanism is integrated into Lévy flight to enhance local search accuracy while preserving global exploration. Based on this improvement, an elite non-dominated strategy is combined with an adaptive grid (dynamic adjustment by solution density) and elliptical segmentation selection—this ensures retention of optimal individuals in high-density areas, maintains population diversity, and accelerates exploration of sparse regions. An external archive further preserves a uniform and diverse Pareto solution set. MOPO is tested in obstacle-free/obstacle WSN models with coverage, waste rate, and energy consumption rate as objectives. Comparative experiments with NSGA-II, MOPSO, and MOGWO in different monitoring areas show MOPO ranks first in all Friedman tests. A real-world test (41°10′20″N, 29°04′30″E, 1320 × 610 m²) achieves 94 % target coverage. This proves MOPO effectively solves the three core challenges of WSN deployment, providing a practical and efficient optimization method for large-scale, resource-constrained WSN scenarios.

随着科学技术的飞速发展，无线传感器网络（WSN）在环境监测、工业控制、智慧城市等领域的应用越来越广泛。然而，无线传感器网络的部署面临着现有算法无法全面解决的三个核心挑战。(1)覆盖精度不够。传统的优化算法（如NSGA-II、MOPSO）由于细粒度搜索能力有限，往往会留下局部覆盖漏洞。(2)资源冗余度高。固定网格或基于权重的方法（如MOEA/D）无法根据环境密度动态调整节点分布，导致冗余部署。(3)能源消耗不平衡。单目标或简化的多目标方法忽略了覆盖率、浪费率和能源消耗之间的权衡，缩短了网络的生命周期。针对这些问题，提出了一种基于改进lsamvy飞行和自适应椭圆分割筛选机制的多目标鹦鹉优化器（MOPO），用于WSN部署优化。原始lsamvy飞行的随机性导致了较大的步长跳跃，使得细粒度搜索变得困难。因此，将Sigmoid摄动机制集成到lsamvy飞行中，以提高局部搜索精度，同时保持全局搜索。在此基础上，将精英非支配策略与自适应网格（根据解密度动态调整）和椭圆分割选择相结合，确保在高密度区域保留最佳个体，保持种群多样性，并加速对稀疏区域的探索。外部存档进一步保存统一和多样的Pareto解集。MOPO在无障碍物/障碍物WSN模型中进行测试，以覆盖率、浪费率和能耗率为目标。与NSGA-II、MOPSO和MOGWO在不同监测区域的对比实验表明，MOPO在所有Friedman测试中均排名第一。实际测试（41°10 ' 20″N, 29°04 ' 30″E, 1320×610 m2）达到94%的目标覆盖率。这证明MOPO有效解决了WSN部署的三大核心挑战，为大规模、资源受限的WSN场景提供了一种实用高效的优化方法。

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引用次数: 0

A robust eclipse attack detection framework for Ethereum networks 一个健壮的eclipse攻击检测框架用于以太坊网络

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-12-19 DOI: 10.1016/j.jnca.2025.104416

Zubaida Rehman , Iqbal Gondal , Hai Dong , Mengmeng Ge , Mark A. Gregory , Ikram ul Haq

Eclipse attacks, which isolate victim nodes by monopolizing their peer connections, remain a critical threat to Ethereum’s consensus mechanism. To address this, we present a principled framework for detecting Eclipse attacks in Ethereum peer-to-peer networks, grounded in a formal adversarial model. Existing defenses are either ad-hoc or lack provable guarantees, leaving open questions about their reliability under adaptive adversaries. Our work aims to bridge this gap by formally defining eclipse attack detection as a security property. We specify soundness, completeness, and robustness theorems under bounded adversarial drift, and derive formal guarantees within false positive and false negative bounds, resilience to adversarial manipulation, and multi-node compositional reliability. We then instantiate a lightweight detection framework that maps packet-level traffic features to predictions using ensemble classifiers (Random Forest, XGBoost). The system was validated using a controlled Ethereum testbed and extended with CTGAN-generated synthetic traces to emulate networks of up to 100 nodes. Empirical evaluation shows that our framework achieves up to 96% F1-score with sub-second inference latency, well within Ethereum’s 12-second Proof-of-Stake validator time slots. These findings demonstrate that lightweight statistical features, when coupled with formal analysis, enable accurate, efficient, and scalable detection of network-level partitioning attacks. Our work establishes a deployable and theoretically grounded defense foundation for securing modern blockchain systems against eclipse adversaries.

Eclipse攻击通过垄断受害节点的对等连接来隔离受害节点，这仍然是对以太坊共识机制的严重威胁。为了解决这个问题，我们提出了一个原则性框架，用于检测以太坊点对点网络中的Eclipse攻击，该框架以正式的对抗模型为基础。现有的防御要么是临时的，要么缺乏可证明的保证，这使得它们在自适应对手下的可靠性问题悬而未决。我们的工作旨在通过将eclipse攻击检测正式定义为一种安全属性来弥合这一差距。我们指定了有界对抗漂移下的稳健性、完备性和鲁棒性定理，并推导了假正和假负边界内的形式保证、对抗操作的弹性和多节点组成可靠性。然后，我们实例化了一个轻量级检测框架，该框架使用集成分类器（Random Forest, XGBoost）将数据包级流量特征映射到预测。该系统使用受控的以太坊测试平台进行了验证，并使用ctgan生成的合成轨迹进行了扩展，以模拟多达100个节点的网络。经验评估表明，我们的框架在亚秒级推理延迟下达到了96%的f1得分，完全在以太坊12秒的权益证明验证器时间段内。这些发现表明，轻量级统计特性与形式化分析相结合，能够准确、高效和可扩展地检测网络级分区攻击。我们的工作为保护现代区块链系统免受eclipse对手的攻击建立了一个可部署的和理论上的防御基础。

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引用次数: 0

Seamless service migration for the Internet of Vehicles in edge computing: A dynamic dirty page filtering and two-stages compression technique 基于边缘计算的车联网无缝服务迁移：动态脏页过滤和两阶段压缩技术

IF 8 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Network and Computer Applications

Pub Date : 2025-12-19 DOI: 10.1016/j.jnca.2025.104412

Kaifeng Hua , Shengchao Su , Nannan Zhang

With the growing demand for dynamic resources in the Internet of Vehicles, service migration has become essential for maintaining user service continuity. However, existing techniques often transfer redundant dirty page data during operation state file transfers, leading to high network traffic and significant migration delays, which are unsuitable for the low latency and low traffic requirements of intelligent transportation scenarios. To overcome this issue, this paper proposes a Intelligent Adaptive Container Migration Technique called IACMT, which is based on dynamic filtering of dirty pages with two-stage compression. IACMT features a dirty page filtering mechanism that intelligently identifies active dirty pages by monitoring the frequency of page accesses and modification patterns in real time. This mechanism facilitates the delayed transmission of less critical dirty pages, effectively reducing the data size during the iterative transmission phase. Furthermore, it incorporates a two-stage data compression algorithm that employs run-length encoding (RLE) followed by dynamic Huffman coding. In the initial stage, RLE eliminates redundant byte sequences in the state file. The subsequent output is then adaptively compressed using a dynamic Huffman tree, improving compression efficiency while managing computational overhead. Experimental results show that IACMT reduces data transmission volume by approximately 35 % for typical in-vehicle workloads, while cutting migration time and service interruption duration by around 24 % and 34 %, respectively.

随着车联网对动态资源的需求日益增长，服务迁移成为保持用户服务连续性的必要条件。然而，现有技术在运行状态文件传输过程中经常传输冗余的脏页数据，导致网络流量大，迁移延迟大，不适合智能交通场景的低延迟、低流量要求。为了克服这一问题，本文提出了一种基于两阶段压缩的脏页动态过滤的智能自适应容器迁移技术IACMT。IACMT提供了一个脏页面过滤机制，通过实时监控页面访问频率和修改模式，智能地识别活动脏页面。这种机制有助于延迟传输不太关键的脏页，有效地减少了迭代传输阶段的数据大小。此外，它还结合了一种采用运行长度编码（RLE）和动态霍夫曼编码的两阶段数据压缩算法。在初始阶段，RLE消除状态文件中的冗余字节序列。然后使用动态霍夫曼树自适应压缩后续输出，在管理计算开销的同时提高压缩效率。实验结果表明，对于典型的车载工作负载，IACMT将数据传输量减少了约35%，同时将迁移时间和服务中断时间分别减少了约24%和34%。

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引用次数: 0