Journal of Network and Computer Applications最新文献

{"title":"ArtPerception: ASCII art-based jailbreak on LLMs with recognition pre-test","authors":"Guan-Yan Yang ,&nbsp;Tzu-Yu Cheng ,&nbsp;Ya-Wen Teng ,&nbsp;Farn Wang ,&nbsp;Kuo-Hui Yeh","doi":"10.1016/j.jnca.2025.104356","DOIUrl":"10.1016/j.jnca.2025.104356","url":null,"abstract":"<div><div>The integration of Large Language Models (LLMs) into computer applications has introduced transformative capabilities but also significant security challenges. Existing safety alignments, which primarily focus on semantic interpretation, leave LLMs vulnerable to attacks that use non-standard data representations. This paper introduces ArtPerception, a novel black-box jailbreak framework that strategically leverages ASCII art to bypass the security measures of state-of-the-art (SOTA) LLMs. Unlike prior methods that rely on iterative, brute-force attacks, ArtPerception introduces a systematic, two-phase methodology. Phase 1 conducts a one-time, model-specific pre-test to empirically determine the optimal parameters for ASCII art recognition. Phase 2 leverages these insights to launch a highly efficient, one-shot malicious jailbreak attack. We propose a Modified Levenshtein Distance (MLD) metric for a more nuanced evaluation of an LLM’s recognition capability. Through comprehensive experiments on four SOTA open-source LLMs, we demonstrate superior jailbreak performance. We further validate our framework’s real-world relevance by showing its successful transferability to leading commercial models, including GPT-4o, Claude Sonnet 3.7, and DeepSeek-V3, and by conducting a rigorous effectiveness analysis against potential defenses such as LLaMA Guard and Azure’s content filters. Our findings underscore that true LLM security requires defending against a multi-modal space of interpretations, even within text-only inputs, and highlight the effectiveness of strategic, reconnaissance-based attacks.</div><div>Content Warning: This paper includes potentially harmful and offensive model outputs.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104356"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning based multi-agent system for smart microgrid","authors":"Niharika Singh ,&nbsp;Kishu Gupta ,&nbsp;Ashutosh Kumar Singh ,&nbsp;Perumal Nallagownden ,&nbsp;Irraivan Elamvazuthi","doi":"10.1016/j.jnca.2025.104339","DOIUrl":"10.1016/j.jnca.2025.104339","url":null,"abstract":"<div><div>Smart microgrid (SMG) communication networks face significant challenges in maintaining high Quality of Service (QoS) due to dynamic load variations, fluctuating network conditions, and potential component faults, which can increase latency, reduce throughput, and compromise fault recovery. The growing integration of distributed renewable energy resources demands adaptive and intelligent routing mechanisms capable of operating efficiently under such diverse and fault-prone conditions. This paper presents a Q-Reinforcement Learning-based Multi-Agent Bellman Routing (QRL-MABR) algorithm, which enhances the traditional MABR approach by embedding a Q-learning module within each network agent. Agents dynamically learn optimal routing policies, balance exploration and exploitation action selection with adaptive temperature scaling, and jointly optimize latency, throughput, jitter, convergence speed, and fault resilience.</div><div>Simulations on IEEE 9, 14, 34, 39, and 57 bus SMG testbeds demonstrate that QRL-MABR significantly outperforms conventional routing protocols (MABR, RIP, OLSR, OSPFv2) and advanced RL-based algorithms (SN-MAPPO, DDQL, MDDPG, SARSA-<span><math><mi>λ</mi></math></span>, TD3), achieving 16%–28% delay reduction, 14%–16% throughput gains, 17%–21% jitter improvement, and superior fault recovery. Thus, QRL-MABR provides a robust, scalable, and intelligent framework for next-generation smart microgrids.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104339"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mamba-NTP: Mamba-based network traffic prediction with sparse measurements","authors":"Chengzhe Xu ,&nbsp;Yingya Guo ,&nbsp;Huan Luo ,&nbsp;Yue Yu ,&nbsp;Zebo Huang","doi":"10.1016/j.jnca.2025.104364","DOIUrl":"10.1016/j.jnca.2025.104364","url":null,"abstract":"<div><div>Accurate network traffic prediction is critical for efficient network planning and routing, especially in large-scale and dynamic environments. Traditional approaches rely on full-scale measurements, which are often impractical due to cost, scalability, and privacy concerns. Sparse measurements offer a more feasible alternative but lead to incomplete traffic data, posing significant challenges for accurate prediction. To address this, we propose Mamba-NTP, a novel end-to-end Mamba-based Network Traffic Prediction framework designed for sparse measurement settings. Leveraging the recent Mamba state-space model, Mamba-NTP captures long-range spatiotemporal dependencies with linear time complexity, enabling efficient and scalable prediction. Furthermore, Mamba-NTP employs a multi-task learning paradigm — comprising Traffic Completion, Graph Learning, and Traffic Prediction tasks — to extract shared traffic representations and enhance prediction robustness. In addition, the graph learning task in Mamba-NTP leverages graph learning techniques to infer intrinsic traffic correlations and model the inner traffic dependencies among network nodes. Extensive experiments on real-world datasets demonstrate that Mamba-NTP consistently outperforms state-of-the-art methods in both accuracy and efficiency under various levels of measurement sparsity.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104364"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A protocol-independent in-network security service for cloud applications","authors":"Bin Song ,&nbsp;Bin Sun ,&nbsp;Qiang Fu ,&nbsp;Hao Li","doi":"10.1016/j.jnca.2025.104368","DOIUrl":"10.1016/j.jnca.2025.104368","url":null,"abstract":"<div><div>Cloud services are increasingly generating a large amount of Internet traffic. Much of it such as rich media and gaming traffic is not highly sensitive but prefers some protection. The traditional end-to-end encryption such as Transport Layer Security Protocol (TLS) is costly and has its own issues, such as increased latency, while the simple anonymity solutions cannot resist traffic analysis attacks. In this paper, we propose FlowShredder, a protocol-independent and in-network service to secure such traffic in the cloud. FlowShredder aims to break the association between the packets, the data flow, and the hosts by obfuscating the packet header (and some payload if needed). Without the context of the flow and the hosts, these packets are of little value to the adversary. The operation is carried out at cloud gateways, without encrypting the payload. Its simple logic can therefore be executed within a single pipeline of the Tofino programmable switch, to ensure wire-speed performance without the scalability issue. Being protocol-independent and operating in-network at wire speed make FlowShredder a practical and generic security service to protect the cloud traffic. In addition, FlowShredder can work with end-to-end encryption such as 0-RTT TLS (<em>e.g.</em>, Quick UDP Internet Connections Protocol, QUIC) for enhanced protection, ideal for web browsing or real-time communications. We implement FlowShredder in Programming Protocol-Independent Packet Processors Language (P4) switches. Experiments in synthetic and real scenarios show that FlowShredder can effectively resist the traffic analysis attack with supervised learning techniques, and realize the wire-speed performance over a 100Gbps network while incurring minor overhead.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104368"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph neural network enhanced Internet of Things node classification with different node connections","authors":"Mohammad Abrar Shakil Sejan ,&nbsp;Md Habibur Rahman ,&nbsp;Md Abdul Aziz ,&nbsp;Rana Tabassum ,&nbsp;Iqra Hameed ,&nbsp;Nidal Nasser ,&nbsp;Hyoung-Kyu Song","doi":"10.1016/j.jnca.2025.104363","DOIUrl":"10.1016/j.jnca.2025.104363","url":null,"abstract":"<div><div>Internet of Things (IoT) has profoundly impacted human life by providing ubiquitous connectivity and unique advantages. As the demand for IoT applications continues to grow, the number of connected devices is increasing at a rapid pace. This growth poses challenges in identifying data sources and managing data in large networks. The graph data structure offers a meaningful way to represent IoT networks, where nodes represent devices and edges represent their connections. In this study, we convert IoT networks into graph representations, considering two approaches: fully connected node graphs and randomly connected node graphs. Graph neural networks (GNNs) are highly effective for processing graph data, as they can capture relationships within graph structures based on their topological properties. We utilize GNNs to perform node classification tasks for IoT networks. Seven different GNN models were investigated to perform node classification tasks on both complete and random graphs. The experimental results indicate that the SAGEConv model achieves high classification accuracy under dense network conditions. Additionally, the CHEBYSHEVConv model performs well with fully connected graphs, while the TAGConv model demonstrates strong performance with randomly connected graphs.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104363"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Next-generation AI for advanced threat detection and security enhancement in DNS over HTTPS","authors":"Basharat Ali,&nbsp;Guihai Chen","doi":"10.1016/j.jnca.2025.104326","DOIUrl":"10.1016/j.jnca.2025.104326","url":null,"abstract":"<div><div>The widespread adoption of DNS over HTTPS(DoH) has inaugurated a new paradigm of network privacy through the encryption of DNS queries; paradoxically, this very mechanism has been weaponized by malicious actors to orchestrate convert cyberattacks ranging from polymorphic malware delivery and data exfiltration to command-and-control (C2) operations. Classic signature-based solutions that rely on static security policies and packet-depth inspection are rendered useless in the face of encrypted DoH traffic, and today’s AI-driven defense solutions typically fail to achieve adversarial robustness, explainability, and real-time scalability. Bridging these gaps, this paper proposes an AI framework that integrates the best practices in machine learning together with secure execution environments to offer resilience, transparency, and low-latency DoH threat detection. Specifically, Capsule Networks (CapsNets) are used to learn hierarchical traffic flow patterns, Graph Transformers to uncover temporal anomalies, and Contrastive Self-Supervised Learning (CSSL) to leverage massive unlabeled datasets. Adversarial robustness is reinforced through perturbation-aware training and mutation-driven fuzzing simulations, while interpretability is enhanced via SHAP and LIME, rendering AI decision-making processes more intelligible to analysts. A distributed Apache Flink/Kafka pipeline enables real-time processing of DoH streams at scale, reducing detection latency by 50% compared to batch-oriented systems. Furthermore, Trusted Execution Environments(TEEs) safeguard model inference against tempering, mitigating insider threats and runtime exploitation. Empirical evaluation on the doh_real_world_2022 dataset demonstrates 99.1% detection accuracy with CapsNets, 98.8% with Graph Transformers, and an 80% improvement in adversarial resilience. These developments collectively propel the discipline of encrypted traffic analysis and establish a benchmark for safeguarding cybersecurity protocols such as QUIC and HTTP/3 that are gaining traction. The findings validate the feasibility of AI-driven, privacy-augmented security systems during an era of escalating cyber-attacks and demands algorithmic transparency.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104326"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A robust fault-tolerant framework for VM failure predication and efficient task scheduling in dynamic cloud environments","authors":"S. Sheeja Rani ,&nbsp;Oruba Alfawaz ,&nbsp;Ahmed M. Khedr","doi":"10.1016/j.jnca.2025.104340","DOIUrl":"10.1016/j.jnca.2025.104340","url":null,"abstract":"<div><div>Due to the dynamic nature of cloud computing, maintaining fault-tolerance is essential to ensure the reliability and performance of virtualized environments. Failures in Virtual Machines (VMs) disrupt the seamless operation of cloud-based services, making it vital to implement a strong failure prediction system. As a solution, this work proposes a Segmented Regressive Learning-based Multivariate Raindrop Optimized Lottery Scheduling (SRL-MROLS) for dynamic cloud environments. Initially, the VM failure prediction is carried out using a Segmented Regressive Q-learning algorithm, where a set of VMs is provided as input. Segmented regression analyzes the average failure rate of VMs, while a reward-based framework guides the decision-making process for accurate failure prediction. Once a failure is predicted, a relocation process is triggered, involving the migration of workloads or tasks from the failing VM to an alternate VM. Next, a Multivariate Elitism Raindrop Optimization approach is employed to identify the optimal VM for task migration. Finally, a Deadline-Aware Stochastic Prioritized Lottery Scheduling is employed for efficient allocation of tasks to the selected VMs, maintaining seamless operations even in the event of VM failures. This process significantly improves task scheduling by maximizing throughput and minimizing response time in cloud environments. Experimental results demonstrate the superior performance of SRL-MROLS across different metrics. Specifically, it achieves an average improvement of 6.4% in failure prediction accuracy, 27.4% in throughput, and a 13% reduction in response time. Additionally, it reduces failure prediction time by 15%, migration cost by 14.3%, and makespan by 15%, significantly outperforming conventional techniques.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104340"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain for the metaverse: Recent advances, taxonomy, and future challenges","authors":"Abdullah Yousafzai ,&nbsp;Muhammad Mohsan Sheeraz ,&nbsp;Ganna Pogrebna ,&nbsp;Jon Crowcroft ,&nbsp;Ibrar Yaqoob","doi":"10.1016/j.jnca.2025.104355","DOIUrl":"10.1016/j.jnca.2025.104355","url":null,"abstract":"<div><div>The metaverse is a shared virtual 3D space that combines immersive experiences with applications in gaming, social interactions, commerce, and more. It is rapidly becoming a reality, driven by advances in virtual reality, augmented reality, artificial intelligence, blockchain, and other emerging technologies. Among these, blockchain technology enables secure and decentralized ownership as well as seamless interoperability of virtual assets. Non-fungible tokens ensure verifiable ownership and fraud prevention, while smart contracts facilitate automated peer-to-peer transactions. Blockchain’s security and transparency promote trust and innovation, laying the foundation for a connected and user-driven metaverse ecosystem. In this paper, we explore the role of blockchain technology as a key enabler for the metaverse, providing solutions for decentralization, governance through decentralized autonomous organizations, interoperable mechanisms, digital asset ownership, traceability, auditing, and identity management. We present the key difference between traditional virtual worlds and the metaverse, and why blockchain is preferred over other decentralized technologies for the metaverse. We comprehensively review recent advances in metaverse system architectures, focusing on state-of-the-art solutions and lessons learned. We compare the existing literature based on key parameters; namely, contributions, advantages, limitations, and applications. We present key challenges, including deepfake threats, identity theft and brand infringement risks, mental health risks, digital safety and gambling risks, virtual world laws and regulations, and privacy and data security concerns. We outline future recommendations for enabling a sustainable and user-friendly metaverse ecosystem.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104355"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anonyma: Anonymous invitation-only registration in malicious adversarial model","authors":"Sanaz Taheri Boshrooyeh,&nbsp;Alpteki̇n Küpçü,&nbsp;Öznur Özkasap","doi":"10.1016/j.jnca.2025.104337","DOIUrl":"10.1016/j.jnca.2025.104337","url":null,"abstract":"<div><div>In invitation-based systems, a new user can register only after obtaining a threshold number of invitations from existing members. The newcomer submits these invitations to the system administrator, who verifies their legitimacy. In doing so, the administrator inevitably learns who invited whom. This inviter–invitee relationship is itself privacy-sensitive information, since knowledge of it can enable inference attacks in which an invitee’s profile (e.g., political views or location) is deduced from the profiles of their inviters. To address this problem, we propose <span><math><mrow><mi>A</mi><mi>n</mi><mi>o</mi><mi>n</mi><mi>y</mi><mi>m</mi><mi>a</mi></mrow></math></span>, an anonymous invitation-based system in which even a corrupted administrator, colluding with a subset of members, cannot determine inviter–invitee relationships. We formally define the notions of <em>inviter anonymity</em> and <em>invitation unforgeability</em>, and provide formal proofs that <span><math><mrow><mi>A</mi><mi>n</mi><mi>o</mi><mi>n</mi><mi>y</mi><mi>m</mi><mi>a</mi></mrow></math></span> achieves both against a <em>malicious</em> and <em>adaptive adversary</em>. Our design ensures constant cost for authenticating new registrations, unlike existing approaches where invitation generation and verification incur overhead linear in the total number of members. Moreover, <span><math><mrow><mi>A</mi><mi>n</mi><mi>o</mi><mi>n</mi><mi>y</mi><mi>m</mi><mi>a</mi></mrow></math></span> scales efficiently: once a user joins, the administrator can immediately issue credentials enabling the newcomer to act as an inviter without re-keying existing members. We also design <span><math><mrow><mi>A</mi><mi>n</mi><mi>o</mi><mi>n</mi><mi>y</mi><mi>m</mi><mi>a</mi><mi>X</mi></mrow></math></span>, a cross-network extension that supports anonymous third-party authentication, allowing invitations issued in one system to be used for registration in another.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104337"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancement and optimization of FlexE technology within metro transport networks","authors":"Mu Liang ,&nbsp;Chen Zhang ,&nbsp;Tao Huang","doi":"10.1016/j.jnca.2025.104365","DOIUrl":"10.1016/j.jnca.2025.104365","url":null,"abstract":"<div><div>Flexible Ethernet (FlexE) technology represents a groundbreaking solution for addressing diverse service requirements and network slicing demands in 5G networks, enabling high-bandwidth, low-latency, and efficient multi-service transmission. However, the current FlexE technology suffers from inefficient bandwidth adjustment, primarily due to its slow overhead insertion mechanism, particularly evident in metro transport networks (MTNs). This inefficiency not only prolongs service reconfiguration time but also leads to significant bandwidth resource wastage along end-to-end network paths. Furthermore, the latency overhead configuration necessitates substantial buffer capacity at network nodes to store pending data, imposing considerable storage pressure on network equipment. In this study, we propose an innovative overhead frame insertion mechanism that addresses these critical limitations while maintaining full compliance with FlexE standards. The proposed method features a streamlined overhead block structure that enables simultaneous and continuous transmission of all overhead information, significantly accelerating service-to-timeslot mapping and reducing link establishment time. Moreover, the proposed mechanism seamlessly integrates with the alignment marker insertion in Physical Coding Sublayer (PCS) and maintains full compatibility with IEEE 802.3 standard, simplifying overhead block extraction and data processing at the receiving end. Simulation results demonstrate that compared to existing FlexE technology, our solution achieves up to a 20-fold improvement in bandwidth adjustment time while substantially reducing buffer requirements and optimizing bandwidth utilization across the entire network infrastructure.</div></div>","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"244 ","pages":"Article 104365"},"PeriodicalIF":8.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}