[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3743":3,"related-3743":35},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":22,"tags":24,"search_phrases":29,"slug":32,"view_count":33,"doi":8,"paper":8,"created_at":34},3743,"安徽省农科院举办网络安全专题培训会","https:\u002F\u002Fwww.aaas.org.cn\u002F4303133\u002F71238548.html","9月20日下午安徽省农科院网信办在合肥城市云数据中心股份有限公司举办网络安全专题培训会。培训采取实地观摩与专题授课相结合的方式，走进数据中心集群现场察看机房基础设施运行环境，了解集群供配电、制冷、机柜布局、网络接入等基础保障情况。",null,"为深入贯彻习近平总书记关于网络强国的重要思想，落实2026年国家网络安全宣传周工作要求，进一步强化全院网络安全意识，9月20日下午，省农科院网信办在合肥城市云数据中心股份有限公司举办网络安全专题培训会，院办公室、机关党委负责同志出席会议，院属各单位网络安全管理员、办公室主任30余人参加培训。\n\n培训采取实地观摩与专题授课相结合的方式进行，参训人员走进合肥城市云数据中心股份有限公司数据中心集群，现场察看机房基础设施运行环境，了解集群供配电、制冷、机柜布局、网络接入等基础保障情况，观摩运营中心对设备状态、网络流量、运行告警的集中监控和运维值守流程。观摩过程中，参训人员结合本单位网络设备管理、系统运行维护和数据存储实际，就日常运维中遇到的难点问题与现场技术人员深入交流。参观结束后，合肥城市云数据中心股份有限公司网络安全专家围绕网络安全法律法规、安全防范意识、典型网络安全事件及单位安全管理行为规范等内容进行专题授课，通过法规解读与勒索病毒等案例分析，帮助参训人员进一步提升网络安全意识和防护能力。\n\n参训人员表示，此次培训把课堂搬到数据中心一线，既有实地观摩的直观感受，又有专题授课的系统讲解，进一步增强了网络安全责任意识和风险防范意识。一致认为，网络安全无小事，必须把责任落实到岗、落实到人，把制度要求转化为具体行动，切实守好农业科研数据安全底线。","安徽省农业科学院","2026-09-20T12:00:00Z","报道",10,false,43,{"impact":17,"substance":13,"depth":17,"authority":18,"freshness":19,"relevant":20,"comment":21},8,11,6,1,"省级农科院网络安全培训，属内部安全意识教育，与农业信息化关联较弱，信息增量有限，不建议进入每日精选。",[23],{"name":10,"url":6},[25,26,27,28],"安徽农科院","网络安全","数据安全","农业科研数据",[30,31],"安徽省农科院 网络安全 培训","合肥城市云数据中心 网络安全","安徽省农科院网络安全培训-3743",0,"2026-09-29T00:08:25.050236Z",{"total":19,"page":20,"page_size":19,"items":36},[37,98,120,162,212,266],{"id":38,"title":39,"url":40,"summary":41,"summary_zh":42,"content":8,"source_name":43,"source_url":40,"published_at":44,"category":45,"cover_url":8,"hotness":13,"is_selected":46,"score":47,"score_detail":48,"sources":55,"tags":57,"search_phrases":62,"slug":65,"view_count":33,"doi":66,"paper":67,"created_at":97},3548,"Cybersecurity and Privacy in AI-Enabled Agricultural IoT Ecosystems: A Systematic Review of Threats, Safeguards, and Resilience Gaps","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fa19100827","Agricultural Internet of Things (IoT) ecosystems increasingly connect sensors, drones, edge devices, and cloud platforms to support precision farming, yet cybersecurity, privacy, and the real-world readiness of proposed safeguards remain fragmented across the literature. This study systematically reviewed cybersecurity threats, privacy concerns, AI-driven and traditional safeguards, and evidence gaps in agricultural IoT research published between 2015 and 2025. Following the Kitchenham and Charters methodology, 103 studies were selected from 2535 records retrieved across five databases. STRIDE and LINDDUN were retrospectively applied as complementary frameworks for threat and privacy classification. Because the coding scheme was multi-label, reliability was assessed at the category level using presence\u002Fabsence decisions on a 20-study sample and observed agreement ranged from 75% to 95% for STRIDE and 95% to 100% for LINDDUN, with interpretable Cohen’s κ values ranging from 0.348 to 0.794 and 0.875 to 1.000, respectively. All included studies also underwent quality appraisal and a supplementary ecological-validity assessment. Denial-of-service, tampering, and spoofing were the most frequently reported threats, concentrated at the device, network, and cloud layers, while the edge layer remained underexamined. AI- and machine-learning-based intrusion detection and privacy-preserving methods such as federated learning emerged as prominent safeguards, but adversarial manipulation of agricultural AI models received limited attention. Privacy research remained oriented toward confidentiality, with 90.3% of studies referencing no applicable regulatory framework. Most importantly, only 8 of 103 studies (7.8%) received a High ecological-validity rating, showing how rarely the evidence base is grounded in real agricultural field conditions. The review identifies field-grounded evaluation, adversarially robust AI, privacy governance, and cyber resilience as priorities for future agricultural IoT security research.","农业物联网（IoT）生态系统日益将传感器、无人机、边缘设备和云平台连接起来，以支持精准农业，然而网络安全、隐私以及所提出保障措施的现实适用性在文献中仍呈现碎片化状态。本研究系统综述了2015年至2025年间发表的农业物联网研究中的网络安全威胁、隐私问题、人工智能驱动及传统保障措施以及证据缺口。遵循Kitchenham和Charters方法论，从五个数据库检索到的2535条记录中筛选出103项研究。STRIDE和LINDDUN被回溯性应用为威胁与隐私分类的互补框架。由于编码方案为多标签，可靠性在类别层面通过20项研究样本的存在\u002F缺失判定进行评估，STRIDE的观察一致率为75%至95%，LINDDUN为95%至100%，可解释的Cohen's κ值分别为0.348至0.794和0.875至1.000。所有纳入研究还接受了质量评价和补充性生态效度评估。拒绝服务、篡改和欺骗是报告最频繁的威胁，集中在设备层、网络层和云层，而边缘层仍未被充分考察。基于人工智能和机器学习的入侵检测以及联邦学习等隐私保护方法成为突出的保障措施，但农业人工智能模型的对抗性操纵受到的关注有限。隐私研究仍以保密性为导向，90.3%的研究未引用任何适用的监管框架。最重要的是，103项研究中仅有8项（7.8%）获得高生态效度评级，表明证据基础鲜有扎根于真实农业田间条件。本综述将田间实证评估、对抗鲁棒人工智能、隐私治理和网络韧性确定为未来农业物联网安全研究的优先事项。","Algorithms","2026-09-25T00:00:00Z","论文",true,82,{"impact":49,"substance":50,"depth":51,"authority":52,"freshness":53,"relevant":20,"comment":54},18,23,19,13,9,"系统综述103项研究，揭示农业物联网安全证据多脱离田间实际，对智慧农业安全研究有较高参考价值。",[56],{"name":43,"url":40},[58,59,60,61,27],"智慧农业","农业人工智能","农业物联网","隐私保护",[63,64],"农业物联网 网络安全","农业AI 隐私保护 联邦学习","农业物联网网络安全-3548","10.3390\u002Fa19100827",{"doi":66,"openalex_id":68,"authors":69,"venue":43,"cited_by_count":33,"oa_url":40,"card":90,"direction":94,"ingested_from":96},"W7214403401",[70,73,76,79,82,85,87],{"name":71,"orcid":72},"Emmanuel Kojo Gyamfi","https:\u002F\u002Forcid.org\u002F0009-0002-0441-2830",{"name":74,"orcid":75},"Jess Kropczynski","https:\u002F\u002Forcid.org\u002F0000-0002-7458-6003",{"name":77,"orcid":78},"Jacques Bou Abdo","https:\u002F\u002Forcid.org\u002F0000-0002-3482-9154",{"name":80,"orcid":81},"Joseph Samuel Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-2555-8142",{"name":83,"orcid":84},"Mustapha Awinsongya Yakubu","https:\u002F\u002Forcid.org\u002F0009-0005-6623-0858",{"name":86,"orcid":8},"Anthony Tsetse",{"name":88,"orcid":89},"Gertrude Kaneah Abagale","https:\u002F\u002Forcid.org\u002F0009-0009-1502-3188",{"tldr":91,"method":92,"finding":93,"direction":94,"opportunity":95},"系统综述2015-2025年农业物联网的网络安全、隐私威胁与防护措施及证据缺口。","Kitchenham系统综述法，筛选103项研究，用STRIDE与LINDDUN","拒绝服务、篡改、欺骗威胁最多，边缘层研究不足，仅7.8%研究具高生态效度。","智慧农业 \u002F 农业物联网","农业AI模型的对抗鲁棒性、边缘层安全、隐私治理与真实田间条件下的韧性评估是明显空白。","openalex","2026-09-26T23:30:15.415998Z",{"id":99,"title":100,"url":101,"summary":102,"summary_zh":8,"content":8,"source_name":10,"source_url":8,"published_at":103,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":104,"score_detail":105,"sources":108,"tags":110,"search_phrases":115,"slug":118,"view_count":33,"doi":8,"paper":8,"created_at":119},3108,"安徽省农科院作物所\"一种小麦抗倒伏肥料及其制备方法\"等 2 项专利赋权转化公示——转让总金额 5.3 万元","https:\u002F\u002Fwww.aaas.org.cn\u002F4303171\u002F71129984.html","9-16 安徽省农科院作物所发布小麦栽培与耕作团队张向前、杜世州、乔玉强、曹承富、李玮、陈欢、赵竹等 7 位完成人 2 项职务科技成果赋权转化公示：专利\"一种小麦抗倒伏肥料及其制备方法\"（ZL202211176053X）、\"一种大豆高产肥料及其制备方法\"（ZL202410297486.3），赋权方式均为职务科技成果长期使用权（10 年），完成人占 70%、转化方占 20%、院占 10%；分别以 5 万元、0.3 万元转让给垦浩农业科技（广州）有限公司。公示时间 9-16 至 10-2 共 15 个工作日。","2026-09-16T00:00:00Z",53,{"impact":17,"substance":106,"depth":13,"authority":52,"freshness":19,"relevant":20,"comment":107},16,"省级科研机构职务成果赋权转化公示，含具体专利号、金额与权益分配，信息实在但影响限于行业层面。",[109],{"name":10,"url":101},[25,111,112,113,114],"专利转化","小麦抗倒伏","大豆高产","成果赋权",[116,117],"安徽省农科院 小麦抗倒伏肥料 专利","小麦抗倒伏肥料 大豆高产肥料 赋权转化","安徽省农科院小麦抗倒伏肥料专利-3108","2026-09-22T00:05:35.824526Z",{"id":121,"title":122,"url":123,"summary":124,"summary_zh":125,"content":8,"source_name":126,"source_url":123,"published_at":127,"category":45,"cover_url":8,"hotness":13,"is_selected":14,"score":128,"score_detail":129,"sources":134,"tags":136,"search_phrases":140,"slug":143,"view_count":33,"doi":144,"paper":145,"created_at":161},2927,"CD-KLM: A Secure Storage Architecture for Agricultural Big Data","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202609.1541.v1","With the development of smart agriculture, agricultural big data typically exhibit characteristics such as long-term accumulation and multi-source heterogeneity, which place higher demands on secure storage and sustained decryptability in long-running environments. To address this issue, this paper proposes CD-KLM, a secure storage architecture for Hadoop-based agricultural big data platforms. Specifically, we adopt a client-driven, server-lightweight-collaborative design, in which the reading and decryption capabilities of historical files are independently controlled by the client, while the server only issues collaborative salt values to authenticated clients during new-file creation. On this basis, a dynamic working-key derivation mechanism is designed based on HKDF and file-related contextual information, so that working keys no longer need to be persistently stored over the long term, thereby reducing the risk of malicious data decryption caused by prolonged exposure of critical key material. Meanwhile, threshold-based key recovery is unified at the root-key level, enabling the system to restore its overall decryption capability even when critical keys are damaged or lost. Experimental results in a Hadoop distributed environment show that, while ensuring key recoverability and continuous decryptability, the proposed architecture imposes only a low impact on system throughput, making it suitable for long-running secure storage scenarios for agricultural big data.","随着智慧农业的发展，农业大数据通常呈现出长期积累、多源异构等特征，这对长运行环境下的安全存储与持续可解密性提出了更高要求。针对这一问题，本文提出了CD-KLM，一种面向基于Hadoop的农业大数据平台的安全存储架构。具体而言，我们采用客户端驱动、服务端轻量协同的设计，其中历史文件的读取与解密能力由客户端独立控制，而服务端仅在新文件创建时向已认证客户端下发协同盐值。在此基础上，基于HKDF与文件相关上下文信息设计了动态工作密钥派生机制，使工作密钥不再需要长期持久化存储，从而降低关键密钥材料长期暴露所导致的恶意数据解密风险。同时，在根密钥层面统一了基于阈值的密钥恢复机制，使系统在关键密钥损坏或丢失时仍能恢复整体解密能力。在Hadoop分布式环境中的实验结果表明，在保证密钥可恢复性与持续可解密性的同时，所提架构对系统吞吐量的影响较低，适用于农业大数据的长期运行安全存储场景。","Preprints.org","2026-09-18T00:00:00Z",63,{"impact":130,"substance":131,"depth":132,"authority":19,"freshness":17,"relevant":20,"comment":133},12,20,17,"面向农业大数据长期安全存储的预印本论文，方法设计有新意但尚未经同行评审，属细分技术进展。",[135],{"name":126,"url":123},[58,137,27,138,139],"农业大数据","Hadoop","密钥管理",[141,142],"CD-KLM 农业大数据 安全存储","Hadoop 农业大数据 密钥恢复","CD-KLM农业大数据安全存储-2927","10.20944\u002Fpreprints202609.1541.v1",{"doi":144,"openalex_id":146,"authors":147,"venue":126,"cited_by_count":33,"oa_url":123,"card":156,"direction":94,"ingested_from":96},"W7213553318",[148,150,152,154],{"name":149,"orcid":8},"Bochun Cao",{"name":151,"orcid":8},"Hanbing Deng",{"name":153,"orcid":8},"Yuncheng Zhou",{"name":155,"orcid":8},"Teng Miao",{"tldr":157,"method":158,"finding":159,"direction":94,"opportunity":160},"提出CD-KLM架构，为Hadoop农业大数据提供长期安全存储与持续解密能力。","客户端驱动、服务端轻量协作，基于HKDF和文件上下文动态派生工作密钥，阈值根密钥","在保证密钥可恢复与持续解密的同时，对Hadoop系统吞吐量影响很小。","可探索动态密钥派生与访问控制结合，适配边缘农业物联网的轻量级安全存储。","2026-09-19T23:30:11.094294Z",{"id":163,"title":164,"url":165,"summary":166,"summary_zh":167,"content":8,"source_name":168,"source_url":165,"published_at":169,"category":45,"cover_url":8,"hotness":13,"is_selected":14,"score":170,"score_detail":171,"sources":175,"tags":177,"search_phrases":179,"slug":182,"view_count":33,"doi":183,"paper":184,"created_at":211},2279,"Communication-efficient Federated Transfer Learning for real-time intrusion detection in agricultural IoT systems","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112411","The rapid deployment of Agricultural Internet of Things (Ag-IoT) networks has introduced significant security challenges, mainly due to their limited computational resources and operation in remote environments. These constraints, combined with the critical nature of agricultural data and operations, make Ag-IoT systems particularly vulnerable to cyber threats, thus requiring robust, efficient security mechanisms. In this context, Intrusion Detection Systems (IDS) play a crucial role in monitoring network activities and detecting malicious behavior. However, conventional IDS solutions often suffer from high computational overhead, increased latency, and substantial communication costs. Although Federated Learning (FL) enhances data privacy by enabling distributed model training, its efficiency remains constrained in resource-limited Ag-IoT scenarios. To overcome these limitations, we propose a novel IDS based on Federated Transfer Learning (FTL) for multiclass network intrusion classification, which integrates FL with lightweight pretrained image models. The key idea of our approach is to transform network traffic data into grayscale images, allowing the reuse of efficient computer vision models. This transformation reduces data complexity, which is expected to support low-resource training and inference on edge devices. The FTL framework ensures that only model updates are exchanged, minimizing communication overhead while preserving data privacy. Experimental results, validated over three independent runs (Mean ± SD), demonstrate that our approach achieves 99.96% ± 0.00% accuracy on the Edge-IIoTset and 99.96% ± 0.01% accuracy on the Farm-Flow dataset. These findings highlight the strong theoretical potential of our FTL-based IDS as a promising candidate to enable intrusion detection in real-time, resource-efficient, and privacy-preserving Ag-IoT networks.","农业物联网(Ag-IoT)网络的快速部署带来了显著的安全挑战，这主要源于其有限的计算资源和在偏远环境中的运行条件。这些限制，加之农业数据和操作的关键性，使Ag-IoT系统特别容易受到网络威胁，因此需要稳健、高效的安全机制。在此背景下，入侵检测系统(IDS)在监控网络活动和检测恶意行为方面发挥着至关重要的作用。然而，传统IDS解决方案往往面临高计算开销、延迟增加和大量通信成本的问题。尽管联邦学习(FL)通过支持分布式模型训练增强了数据隐私，但其效率在资源受限的Ag-IoT场景中仍然受到制约。为克服这些局限，我们提出了一种基于联邦迁移学习(FTL)的新型IDS，用于多类网络入侵分类，该方案将FL与轻量级预训练图像模型相结合。我们方法的核心思想是将网络流量数据转换为灰度图像，从而能够复用高效的计算机视觉模型。这种转换降低了数据复杂度，有望支持边缘设备上的低资源训练和推理。FTL框架确保仅交换模型更新，在保持数据隐私的同时最大限度地减少通信开销。实验结果表明，在三次独立运行验证下(均值±标准差)，我们的方法在Edge-IIoTset上达到99.96%±0.00%的准确率，在Farm-Flow数据集上达到99.96%±0.01%的准确率。这些发现凸显了基于FTL的IDS在实现实时、资源高效且隐私保护的Ag-IoT网络入侵检测方面具有强大的理论潜力。","Computers and Electronics in Agriculture","2026-09-12T00:00:00Z",81,{"impact":49,"substance":172,"depth":49,"authority":173,"freshness":53,"relevant":20,"comment":174},22,14,"提出联邦迁移学习入侵检测方法，将流量转为灰度图复用轻量视觉模型，在Edge-IIoTset与Farm-Flow上达99.96%精度，兼顾实时性、低通信开销与隐私保护，对农业物联网安全有较强参考价值。",[176],{"name":168,"url":165},[58,59,60,178,26],"联邦学习",[180,181],"农业人工智能 农业物联网 智慧农业 网络安全","农业人工智能 农业物联网","农业人工智能农业物联网智慧农业网络安全-2279","10.1016\u002Fj.compag.2026.112411",{"doi":183,"openalex_id":185,"authors":186,"venue":168,"cited_by_count":33,"oa_url":165,"card":206,"direction":94,"ingested_from":96},"W7212387370",[187,190,193,196,199,201,203],{"name":188,"orcid":189},"Amina Khacha","https:\u002F\u002Forcid.org\u002F0009-0009-3299-8622",{"name":191,"orcid":192},"Zibouda Aliouat","https:\u002F\u002Forcid.org\u002F0000-0002-7007-7607",{"name":194,"orcid":195},"Yasmine Harbi","https:\u002F\u002Forcid.org\u002F0000-0001-6731-7895",{"name":197,"orcid":198},"Chirihane Gherbi","https:\u002F\u002Forcid.org\u002F0000-0002-0551-3978",{"name":200,"orcid":8},"Rafika Saadouni",{"name":202,"orcid":8},"Ado Adamou ABBA ARI",{"name":204,"orcid":205},"Hakim Mabed","https:\u002F\u002Forcid.org\u002F0000-0001-8358-4029",{"tldr":207,"method":208,"finding":209,"direction":94,"opportunity":210},"提出联邦迁移学习入侵检测方法，将流量转灰度图复用轻量视觉模型，实现农业物联网实时检测。","联邦迁移学习+轻量预训练图像模型，流量转灰度图，Edge-IIoTset与Far","在Edge-IIoTset和Farm-Flow上均达99.96%准确率，通信开销低且保护隐私。","可探索真实Ag-IoT边缘设备部署与动态异构流量下的联邦迁移学习鲁棒性及通信压缩优化。","2026-09-13T23:30:01.631534Z",{"id":213,"title":214,"url":215,"summary":216,"summary_zh":217,"content":8,"source_name":218,"source_url":215,"published_at":219,"category":45,"cover_url":8,"hotness":13,"is_selected":14,"score":220,"score_detail":221,"sources":223,"tags":225,"search_phrases":228,"slug":231,"view_count":33,"doi":232,"paper":233,"created_at":265},2144,"Smart Farming Cybersecurity: Key Risks and Security Principles","https:\u002F\u002Fdoi.org\u002F10.3390\u002Felectronics15184087","By combining digital sensing, network connectivity, data-driven analyses, cloud services, and automated controls, smart farming has been increasingly adopted in agricultural production. Although these technologies have improved the precision and efficiency of farm management, they also increase cybersecurity exposure as agricultural facilities are connected to external networks, platforms, and remote-control environments. This review seeks to clarify why cybersecurity should be considered a fundamental requirement in smart farming and details the major system components, cybersecurity risks, and network design considerations required for secure operation. This review first explains the concept and application scope of smart farming, and then examines how sensors, communication networks, gateways, control systems, data platforms, user interfaces, cloud infrastructure, and physical support systems contribute to farm management and cybersecurity exposure. The review also emphasizes that smart farming differs from ordinary information systems because digital data and control commands can directly affect physical processes, such as irrigation, ventilation, heating, nutrient supply, and livestock management. Based on these cyber-physical characteristics, the review summarizes the key architectural considerations for reducing cybersecurity risks, including network segmentation, data and command flow mapping, gateway and wireless security, remote access management, cloud access control, device inventory, logging, monitoring, resilience, and local fail-safe operation. Overall, ensuring cybersecurity in smart farming requires an integrated approach that protects not only data and accounts but also the reliability and continuity of agricultural production.","通过融合数字传感、网络连接、数据驱动分析、云服务和自动化控制，智慧农业（smart farming）在农业生产中日益得到采用。尽管这些技术提高了农场管理的精准性和效率，但随着农业设施与外部网络、平台和远程控制环境相连，它们也增加了网络安全暴露风险。本综述旨在阐明为何网络安全应被视为智慧农业的基本要求，并详细阐述安全运行所需的主要系统组件、网络安全风险和网络设计考量。本综述首先阐释智慧农业的概念和应用范围，继而考察传感器、通信网络、网关、控制系统、数据平台、用户界面、云基础设施和物理支撑系统如何共同作用于农场管理和网络安全暴露。本综述还强调，智慧农业不同于普通信息系统，因为数字数据和控制指令能够直接影响物理过程，如灌溉、通风、加热、养分供给和畜牧管理。基于这些信息物理特征，本综述总结了降低网络安全风险的关键架构考量，包括网络分段、数据和指令流映射、网关与无线安全、远程访问管理、云访问控制、设备清单、日志记录、监控、韧性和本地故障安全运行。总体而言，确保智慧农业的网络安全需要一种综合性方法，不仅保护数据和账户，还要保护农业生产的可靠性和连续性。","Electronics","2026-09-10T00:00:00Z",76,{"impact":49,"substance":131,"depth":132,"authority":52,"freshness":17,"relevant":20,"comment":222},"系统梳理智慧农业网络安全的架构性风险与防护原则，对农业信息化建设有实质参考价值，但属综述性论文，产业影响偏间接。",[224],{"name":218,"url":215},[58,60,226,227,27],"精准农业","农业网络安全",[229,230],"农业网络安全 农业物联网 数据安全 智慧农业","农业网络安全 农业物联网","农业网络安全农业物联网数据安全智慧农业-2144","10.3390\u002Felectronics15184087",{"doi":232,"openalex_id":234,"authors":235,"venue":218,"cited_by_count":33,"oa_url":215,"card":260,"direction":94,"ingested_from":96},"W7212146111",[236,238,240,243,246,249,251,253,255,257],{"name":237,"orcid":8},"Sunmi Kong",{"name":239,"orcid":8},"Chang Ha Park",{"name":241,"orcid":242},"Kyung Jun Lee","https:\u002F\u002Forcid.org\u002F0000-0002-8972-0391",{"name":244,"orcid":245},"Tae‐Su Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6402-8139",{"name":247,"orcid":248},"Yeong‐Seon Won","https:\u002F\u002Forcid.org\u002F0009-0000-0950-7834",{"name":250,"orcid":8},"Min-Ho Jo",{"name":252,"orcid":8},"SongYi Han",{"name":254,"orcid":8},"Ju Eun Ko",{"name":256,"orcid":8},"Hyeon Ju Nam",{"name":258,"orcid":259},"Hyeon Ji Yeo","https:\u002F\u002Forcid.org\u002F0000-0002-5474-8204",{"tldr":261,"method":262,"finding":263,"direction":94,"opportunity":264},"综述智慧农业网络安全风险，提出保障生产可靠性的架构原则。","文献综述，分析传感器、网关、云平台等系统组件与网络设计。","智慧农业网络安全需保护物理生产过程，而非仅数据与账户。","可研究面向灌溉、通风等物理过程的轻量级入侵检测与本地失效安全机制。","2026-09-11T23:30:10.445737Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":8,"source_name":272,"source_url":269,"published_at":273,"category":45,"cover_url":8,"hotness":13,"is_selected":14,"score":274,"score_detail":275,"sources":277,"tags":279,"search_phrases":282,"slug":285,"view_count":33,"doi":286,"paper":287,"created_at":308},2043,"Bridging Governance and Empirical Threat Intelligence: An Integrated Framework for Cybersecurity in Smart Farming","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp16188943","Modern agriculture’s integration of Internet of Things (IoT), Industrial Control Systems (ICSs), and data analytics boosts productivity but introduces significant cybersecurity and data governance challenges. Existing scholarship is divided between policy-focused governance and technical attack analyses, hindering the development of comprehensive, enforceable defenses. This paper introduces an integrated framework that bridges normative data governance in smart farming (SF) with empirical, honeynet-derived threat intelligence. Drawing on the authors’ previous systematic review of SF data governance and a honeynet simulating agricultural IoT\u002FICS, the study maps governance challenges to quantitative attack indicators from honeynet logs, classifying each pairing as directly supported by telemetry, indirectly supported by telemetry, or not observable using the current methodology. The findings show that the services flagged as governance concerns face sustained attack pressure: the honeynet recorded brute-force attempts against SSH\u002FTelnet on simulated irrigation controllers (149,000 events), connection and login attempts targeting SMB (Server Message Block) and MQTT (Message Queuing Telemetry Transport) on automated machinery (67,156), credential-guessing attempts against management services (14,937), and ICS protocol probes (11,532). Geographic and protocol distributions reveal that legacy industrial protocols and weakly authenticated management interfaces, both highlighted as governance concerns, constitute the primary attack surface. This evidence supports a tiered governance model integrating protocol-level controls, identity governance, and data-sharing policy, demonstrating that effective SF cybersecurity requires empirically calibrated rather than purely policy-driven frameworks. The proposed framework offers actionable guidance for aligning technical defenses with data governance obligations. This work contributes a new methodological protocol (cross-evidentiary mapping), an empirically calibrated tiered framework, and a coherent research agenda at the intersection of governance and measurement, serving as a template for similar analyses in other critical infrastructure sectors.","现代农业对物联网（IoT）、工业控制系统（ICS）和数据分析的整合提升了生产力，但也带来了显著的网络安全与数据治理挑战。现有学术研究在侧重政策的治理研究与侧重技术的攻击分析之间各执一端，阻碍了全面且可执行的防御体系的构建。本文提出一个集成框架，将智慧农业（SF）中的规范性数据治理与基于蜜网的经验性威胁情报相衔接。基于作者此前对智慧农业数据治理的系统综述以及一个模拟农业物联网\u002F工业控制系统的蜜网，本研究将治理挑战与蜜网日志中的量化攻击指标进行映射，并将每一组对应关系分类为：由遥测数据直接支持、由遥测数据间接支持，或使用当前方法无法观测。研究结果表明，被标记为治理关切的服务面临持续的攻击压力：蜜网记录到针对模拟灌溉控制器上SSH\u002FTelnet的暴力破解尝试（149,000次事件）、针对自动化机械上SMB（服务器消息块）和MQTT（消息队列遥测传输）的连接与登录尝试（67,156次）、针对管理服务的凭证猜测尝试（14,937次），以及ICS协议探测（11,532次）。地理与协议分布揭示，传统工业协议和弱认证管理接口——二者均被列为治理关切——构成了主要攻击面。这些证据支持一个整合协议级控制、身份治理和数据共享政策的分层治理模型，表明有效的智慧农业网络安全需要基于经验校准而非纯粹政策驱动的框架。所提出的框架为将技术防御与数据治理义务对齐提供了可操作的指导。本研究贡献了一套新的方法论协议（交叉证据映射）、一个经经验校准的分层框架，以及在治理与测量交叉领域的一项连贯研究议程，为其他关键基础设施领域的类似分析提供了模板。","Applied Sciences","2026-09-09T00:00:00Z",79,{"impact":49,"substance":172,"depth":49,"authority":52,"freshness":17,"relevant":20,"comment":276},"以蜜网实测数据将智慧农业数据治理与网络攻击威胁对接，方法新颖、数据规模可观，对农业信息化安全建设有参考价值。",[278],{"name":272,"url":269},[58,60,280,26,281],"数据治理","农业工控",[283,284],"农业物联网 农业工控 数据治理 智慧农业","农业物联网 农业工控","农业物联网农业工控数据治理智慧农业-2043","10.3390\u002Fapp16188943",{"doi":286,"openalex_id":288,"authors":289,"venue":272,"cited_by_count":33,"oa_url":269,"card":303,"direction":94,"ingested_from":96},"W7212044568",[290,292,295,297,299,301],{"name":291,"orcid":8},"Radwan Rouzky",{"name":293,"orcid":294},"Abdolhossein Sarrafzadeh","https:\u002F\u002Forcid.org\u002F0000-0003-0816-589X",{"name":296,"orcid":8},"Evelyn Sowells-Boone",{"name":298,"orcid":8},"Jason Green",{"name":300,"orcid":8},"Hannaneh B. Pasandi",{"name":302,"orcid":8},"Gregory Goins",{"tldr":304,"method":305,"finding":306,"direction":94,"opportunity":307},"提出融合数据治理与蜜网威胁情报的智慧农业网络安全框架。","基于蜜网模拟农业IoT\u002FICS，将治理挑战映射到攻击日志指标。","治理关注的服务遭持续攻击，需实证校准的分层治理而非纯政策框架。","可跨关键基础设施复制该跨证据映射方法，并扩展真实农田部署验证。","2026-09-10T23:30:09.503231Z"]