[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5555":3,"related-5555":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":22,"primary_topic":27,"secondary_topics":33,"classification":34,"search_phrases":62,"slug":65,"view_count":15,"doi":66,"paper":67,"created_at":79},5555,"IoT Security Challenges and Solutions: Threats, Standards and Layered Protection","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23542095","The Internet of Things (IoT) has become an important part of modern life. Smart homes, wearable devices, healthcare systems, industrial machines, and smart agriculture use connected devices to collect and exchange information. Although IoT makes many activities easier, it also introduces security and privacy risks. Weak passwords, insecure communication, outdated firmware, unauthorized access, and data leakage can expose devices and users to cyberattacks. This paper examines major security challenges faced by IoT systems and discusses practical solutions based on established cybersecurity guidance. It synthesizes three complementary perspectives: academic survey research on IoT security, the National Institute of Standards and Technology (NIST) IoT security baseline, and the OWASP Internet of Things security guidance. The study explains common threats, reviews relevant standards, and proposes a layered protection model covering devices, networks, applications, data, and system management. It concludes that IoT security should be considered throughout the device lifecycle rather than added only after a problem occurs","物联网（IoT）已成为现代生活的重要组成部分。智能家居、可穿戴设备、医疗保健系统、工业机械和智慧农业均利用互联设备采集和交换信息。尽管物联网使许多活动更加便捷，但也带来了安全与隐私风险。弱密码、不安全的通信、过时的固件、未经授权的访问以及数据泄露，都可能使设备和用户暴露于网络攻击之中。本文考察了物联网系统面临的主要安全挑战，并基于既有的网络安全指南讨论了切实可行的解决方案。研究综合了三个互补视角：关于物联网安全的学术调查研究、美国国家标准与技术研究院（NIST）物联网安全基线，以及OWASP物联网安全指南。本研究阐释了常见威胁，梳理了相关标准，并提出了一种涵盖设备、网络、应用、数据和系统管理的分层防护模型。研究结论认为，物联网安全应在设备全生命周期中加以考虑，而非仅在问题出现后才予以补加。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z","论文",25,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"通用物联网安全综述，仅泛泛提及智慧农业，与三农及农业信息化主题关联薄弱，不建议入选。",[19,20],{"name":10,"url":6},{"name":10,"url":21},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23541648",[23,24,25,26],"智慧农业","网络安全","物联网安全","NIST标准",{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[],{"status":35,"locked":14,"version":36,"method":37,"evidence":38},"classified","2026-10-22.2","rules",{"candidates":39,"agricultural_evidence":42},[40,49,56],{"code":28,"score":41,"supported":42,"title":43,"abstract":45,"tags":48},17,true,[44],"IoT",[46,47,44],"物联网","internet of things",[],{"code":50,"score":51,"supported":42,"title":52,"abstract":53,"tags":55},"I1",7,[],[23,54],"smart agriculture",[23],{"code":57,"score":58,"supported":14,"title":59,"abstract":60,"tags":61},"I6",1,[],[],[24],[63,64],"物联网安全 智慧农业 网络安全 NIST标准","物联网安全 智慧农业","物联网安全智慧农业网络安全NIST标准-5555","10.5281\u002Fzenodo.23542095",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":15,"oa_url":6,"card":72,"direction":76,"ingested_from":78},"W7223144955",[70],{"name":71,"orcid":9},"himesh suryawanshi",{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"综述物联网安全挑战，结合学术与NIST、OWASP标准提出分层防护模型。","综合学术综述、NIST物联网安全基线与OWASP指南，构建分层防护模型。","物联网安全需覆盖设备全生命周期，而非事后补救。","智慧农业 \u002F 农业物联网","可将通用分层防护模型落地到农业物联网场景，研究低成本设备与田间网络的轻量安全方案。","openalex","2026-10-22T11:20:18.423667Z",{"total":81,"page":58,"page_size":81,"items":82},6,[83,159,256,331,400,465],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":9,"source_name":89,"source_url":86,"published_at":90,"category":12,"cover_url":9,"hotness":91,"is_selected":14,"score":92,"score_detail":93,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":109,"classification":111,"search_phrases":130,"slug":133,"view_count":15,"doi":134,"paper":135,"created_at":158},5236,"A cross-domain review of artificial intelligence techniques for iot security, computer networks, image processing and smart agriculture in connected environments","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02500-9","The rapid digitization of modern systems has increased the demand for intelligent, adaptive, and data-driven solutions across connected systems. However, these domains face persistent challenges such as heterogeneous datasets, limited labeled data, real-time processing constraints, privacy concerns, model interpretability, and poor generalization across environments. This review examines the application of major Artificial Intelligence (AI) techniques for solving classification, prediction, optimization, anomaly detection, routing, image analysis, and decision-support problems. The empirical patterns across the literature are summarized cautiously across studies. General lessons that emerged across the reviewed domains were also presented. Overall, the review provides researchers with a concise foundation for understanding current AI methods, their practical limitations, and future research directions in the aspect of connected environments. This article has tried to clarify various aspects and overall issues related to the use of AI techniques in computer networks, image processing and computer vision, smart agriculture and related intelligent systems. The authors believe that this is a comprehensive and integrated survey of these AI techniques in a number of application domains, with an emphasis on their practical suitability, their current empirical limitations, and future research directions. Thus, the article is informative to researchers in the field and may be a guide to future research on the development of robust, interpretable, scalable, and context-aware AI-based connected systems.","现代系统的快速数字化增加了对互联系统中智能、自适应和数据驱动解决方案的需求。然而，这些领域面临诸如异构数据集、标注数据有限、实时处理约束、隐私问题、模型可解释性以及跨环境泛化能力差等持续挑战。本综述考察了主要人工智能（Artificial Intelligence, AI）技术在解决分类、预测、优化、异常检测、路由、图像分析和决策支持问题中的应用。本文谨慎地总结了文献中的实证规律。文中还提出了在所综述领域中浮现的一般性经验教训。总体而言，本综述为研究人员提供了一个简明的基础，以理解当前AI方法、其实际局限性以及互联环境方面的未来研究方向。本文试图阐明与AI技术在计算机网络、图像处理与计算机视觉、智慧农业及相关智能系统中的应用有关的各个方面和总体问题。作者认为，这是对若干应用领域中这些AI技术的全面且综合的调查，重点在于其实际适用性、当前实证局限性和未来研究方向。因此，本文对该领域的研究人员具有参考价值，并可为开发稳健、可解释、可扩展且具有情境感知能力的基于AI的互联系统的未来研究提供指导。","Discover Artificial Intelligence","2026-10-17T00:00:00Z",10,51,{"impact":94,"substance":95,"depth":91,"authority":95,"freshness":96,"relevant":58,"comment":97},8,12,9,"跨领域AI综述，涉及智慧农业但仅为其一分支，方法层面无突破性新结论，公共价值有限，不宜进入每日精选。",[99],{"name":89,"url":86},[23,101,102,25,103],"农业人工智能","计算机视觉","智能系统",{"code":105,"slug":106,"name":107,"path":108,"parent_code":32},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[110],{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},{"status":35,"locked":14,"version":36,"method":37,"evidence":112},{"candidates":113,"agricultural_evidence":42},[114,121,126],{"code":105,"score":115,"supported":42,"title":116,"abstract":118,"tags":120},19,[117],"artificial intelligence",[102,117,119],"computer vision",[101,102],{"code":50,"score":122,"supported":42,"title":123,"abstract":124,"tags":125},15,[54],[23,54],[23],{"code":28,"score":94,"supported":42,"title":127,"abstract":128,"tags":129},[44],[],[],[131,132],"AI 物联网安全 智慧农业","农业人工智能 物联网安全 计算机视觉 智慧农业","AI物联网安全智慧农业-5236","10.1007\u002Fs44163-026-02500-9",{"doi":134,"openalex_id":136,"authors":137,"venue":89,"cited_by_count":15,"oa_url":86,"card":153,"direction":76,"ingested_from":78},"W7222847048",[138,141,144,147,150],{"name":139,"orcid":140},"Bashir Olaniyi Sadiq","https:\u002F\u002Forcid.org\u002F0000-0001-7868-4076",{"name":142,"orcid":143},"Mohammed Dahiru Buhari","https:\u002F\u002Forcid.org\u002F0000-0002-1076-216X",{"name":145,"orcid":146},"IBRAHIM ADABARA","https:\u002F\u002Forcid.org\u002F0000-0001-8949-7540",{"name":148,"orcid":149},"Enerst Edozie","https:\u002F\u002Forcid.org\u002F0000-0002-7616-0962",{"name":151,"orcid":152},"Asaba Shabiluh","https:\u002F\u002Forcid.org\u002F0000-0001-6342-9099",{"tldr":154,"method":155,"finding":156,"direction":76,"opportunity":157},"综述AI在物联网安全、计算机网络、图像处理与智慧农业中的应用与局限。","跨领域文献综述，归纳分类、预测、优化、异常检测等方法。","各领域普遍受限于异构数据、标注少、实时性与可解释性不足。","可聚焦农业物联网中少标注、跨环境泛化的可解释实时AI模型研究。","2026-10-18T23:30:07.908595Z",{"id":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":165,"source_url":162,"published_at":166,"category":12,"cover_url":9,"hotness":91,"is_selected":14,"score":167,"score_detail":168,"sources":173,"tags":175,"primary_topic":179,"secondary_topics":185,"classification":187,"search_phrases":227,"slug":230,"view_count":15,"doi":231,"paper":232,"created_at":255},5077,"A Trust-Boundary Reference Architecture for Secure Cyber-Physical Agriculture: Design and Software-Based Proof-of-Concept for Smart Irrigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206545","Smart irrigation is an operational technology (OT) problem as much as an Internet of Things (IoT) one: software decisions act directly on valves, pumps, pressure and water delivery, so a spoofed reading or a replayed command has physical consequences. This paper presents a technology-neutral reference architecture that makes those consequences controllable. It is organised into seven functional layers and three cross-cutting planes, six trust-boundary classes that fix which properties must be re-established at each crossing, and an auditable crown-asset inventory with an explicit trust-level rubric. Two governing execution principles enforce this structure: local safety authority, which keeps final actuation permission at the field gateway, remote terminal unit (RTU) or programmable logic controller (PLC), and feedback-aware execution, which treats a command as complete only when independent physical feedback confirms it. Building on process-aware and safety-supervision ideas from industrial control, the architecture makes physical confirmation a mandatory completion condition at the local enforcement point, which catches a fully authorised command that is nonetheless physically unsafe, a failure mode that authorisation checks alone cannot detect. These properties are examined through a deterministic, openly available software proof-of-concept that verifies the design against its own acceptance criteria; it is not a field validation. The software executes six attack and failure scenarios plus one nominal-operation control against an intentionally unsecured negative control and a secure controller, under identical, controlled conditions: the negative control is compromised in all 6 attack scenarios and the secure controller in 0\u002F6, with 0\u002F1 false positives on legitimate operation. Analytically, the design costs 21 bytes and 50–60% additional Long Range Wide Area Network (LoRaWAN) time-on-air per record, and 78% of single-step perturbations of the trust-scoring inputs leave asset classification unchanged: the actuator class (A2) is completely stable, and the gateway\u002FRTU\u002FPLC class (A3) is stable under 6 of 7 admissible perturbations (86%), changing only when its credential\u002Fupdate authority reaches the trust-anchor threshold. A sensor-level mapping from sampling rate, measurement drift and physical fault modes to the edge-verification controls they dictate, provisions for secure time, counter recovery and the choice between fail-closed and fail-open safe states, an illustrative hypothetical worked deployment example, and a transfer of the model to greenhouse, fertigation, livestock-watering and cold-chain settings show how the architecture could be applied in practice.","智能灌溉既是一个物联网(IoT)问题，也是一个运营技术(OT)问题：软件决策直接作用于阀门、水泵、压力和水流输送，因此被欺骗的读数或被重放的命令会产生物理后果。本文提出了一种技术中立的参考架构，使这些后果可控。该架构由七个功能层和三个横切面、六类信任边界(规定每次跨越边界时必须重新建立哪些属性)，以及一份可审计的核心资产清单(配有明确的信任等级评定标准)组成。两条主导性执行原则保障了这一结构：本地安全授权，即将最终执行许可保留在田间网关、远程终端单元(RTU)或可编程逻辑控制器(PLC)处；以及反馈感知执行，即只有当独立的物理反馈予以确认时，才将命令视为完成。该架构以工业控制中的过程感知和安全监督思想为基础，将物理确认设为本地执行点的强制性完成条件，从而能够捕获完全获得授权但在物理上仍不安全的命令——这是仅靠授权检查无法检测到的失效模式。这些特性通过一个确定性、公开可用的软件概念验证进行了检验，该验证依据其自身的验收标准对设计进行评估；这不是现场验证。该软件在相同且受控的条件下，针对一个故意不设防护的阴性对照和一个安全控制器，执行了六种攻击与故障场景以及一项标称运行对照：阴性对照在所有6种攻击场景中均被攻破，而安全控制器为0\u002F6，对合法运行的误报为0\u002F1。分析表明，该设计每条记录额外增加21字节和50–60%的LoRaWAN(LoRaWAN)空中时间，且信任评分输入的78%单步扰动不会改变资产分类：执行器类(A2)完全稳定，网关\u002FRTU\u002FPLC类(A3)在7种可允许扰动中有6种保持稳定(86%)，仅当其凭证\u002F更新权限达到信任锚阈值时才会改变。还提出了从采样率、测量漂移和物理故障模式到其所决定的边缘验证控制的传感器级映射，以及安全时间、计数器恢复和故障关闭与故障打开安全状态之间选择的保障措施。","Sensors","2026-10-15T00:00:00Z",75,{"impact":95,"substance":169,"depth":170,"authority":171,"freshness":96,"relevant":58,"comment":172},22,18,14,"面向智慧灌溉的信任边界参考架构论文，方法新颖、含软件验证与量化开销数据，但属概念验证、非田间实证，产业影响有限，可作为技术前沿精选。",[174],{"name":165,"url":162},[176,23,177,178,24],"数字乡村","农业物联网","智能灌溉",{"code":180,"slug":181,"name":182,"path":183,"parent_code":184},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[186],{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},{"status":35,"locked":14,"version":36,"method":37,"evidence":188},{"candidates":189,"agricultural_evidence":42},[190,195,199,206,212,218,223],{"code":180,"score":122,"supported":42,"title":191,"abstract":192,"tags":194},[181],[193,181],"灌溉",[178],{"code":28,"score":91,"supported":42,"title":196,"abstract":197,"tags":198},[],[46,47,44],[177],{"code":200,"score":201,"supported":42,"title":202,"abstract":203,"tags":205},"B7",3,[],[204],"greenhouse",[],{"code":207,"score":201,"supported":42,"title":208,"abstract":209,"tags":211},"C",[],[210],"livestock",[],{"code":213,"score":201,"supported":42,"title":214,"abstract":215,"tags":217},"F3",[],[216],"fertigation",[],{"code":57,"score":219,"supported":14,"title":220,"abstract":221,"tags":222},2,[],[],[176,24],{"code":50,"score":58,"supported":14,"title":224,"abstract":225,"tags":226},[],[],[23],[228,229],"智能灌溉 信任边界 参考架构","LoRaWAN 灌溉 安全","智能灌溉信任边界参考架构-5077","10.3390\u002Fs26206545",{"doi":231,"openalex_id":233,"authors":234,"venue":165,"cited_by_count":15,"oa_url":162,"card":250,"direction":76,"ingested_from":78},"W7222165876",[235,238,241,244,247],{"name":236,"orcid":237},"Alvertos Ioannis Mourikis","https:\u002F\u002Forcid.org\u002F0009-0009-2386-2803",{"name":239,"orcid":240},"Iosif Polenakis","https:\u002F\u002Forcid.org\u002F0000-0002-6427-5519",{"name":242,"orcid":243},"Christos Sarantidis","https:\u002F\u002Forcid.org\u002F0009-0003-9486-8483",{"name":245,"orcid":246},"Romanos Kalamatianos","https:\u002F\u002Forcid.org\u002F0009-0000-6939-5424",{"name":248,"orcid":249},"Ioannis Karydis","https:\u002F\u002Forcid.org\u002F0000-0002-9470-2729",{"tldr":251,"method":252,"finding":253,"direction":76,"opportunity":254},"提出面向智慧灌溉的安全参考架构，用软件验证可抵御重放与欺骗攻击。","七层三面参考架构、信任边界分类、确定性软件概念验证与LoRaWAN开销分析。","安全控制器在6种攻击场景下0次被攻破，负控制全部被攻破，且误报率为0。","可研究边缘验证控制与采样率、漂移、故障模式的映射，以及安全时间与失效安全状态选择。","2026-10-16T23:30:08.098924Z",{"id":257,"title":258,"url":259,"summary":260,"summary_zh":261,"content":9,"source_name":262,"source_url":259,"published_at":263,"category":12,"cover_url":9,"hotness":91,"is_selected":14,"score":264,"score_detail":265,"sources":267,"tags":269,"primary_topic":272,"secondary_topics":273,"classification":274,"search_phrases":310,"slug":313,"view_count":314,"doi":315,"paper":316,"created_at":330},3471,"A secure federated learning framework with blockchain-based authentication for anomaly detection in cooperative smart farming","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.engappai.2026.116072","Agriculture is still a crucial global pillar of the world economy, ensuring food security and livelihood. But changing climate conditions and increasing reliance on the Internet of Things (IoT) based precision agriculture have brought new challenges, mainly cybersecurity. Cooperative Smart Farming (CSF) schemes promote resource pooling among small farmers and thus, the adoption of cutting-edge technologies. Although democratizing precision farming also makes it more accessible to cyber threats. To sort out all these problems, the proposed method introduces a secure federated learning framework along with blockchain-based authentication for anomaly detection in CSFs. The proposed method employs a federated transfer learning-based shadow attention mechanism, in conjunction with the Zeiler and Fergus network, to amplify the performance of the anomaly detection technique. This allows farms to detect threats in premises while protecting data privacy, as only model updates that are encrypted are transmitted. A blockchain-backed decentralized authentication scheme based on the proof of authentication consensus protocol is built to provide trust and resistance to tampering. Moreover, the random k-sparsification technique with changing rank adjustment has a positive impact on communication efficiency, which results in the same level of accuracy but with less communication overhead. The proposed method is executed on four datasets, CSE-CIS-IDS2018, MQTTset, ToN-IoT, and Edge IoT, achieving classification accuracies of 99.99%,99.93%,99.94%, and 99.91%, respectively, and it performs better than the current techniques. This comprehensive framework provides a scalable and trusted method for anomaly detection in CSFs, enabling resilient, privacy-preserving, and intelligent smart farming ecosystems.","农业仍然是全球经济的重要支柱，保障着粮食安全和生计。然而，不断变化的气候条件以及日益依赖基于物联网（IoT）的精准农业，带来了新的挑战，主要是网络安全问题。合作智慧农业（Cooperative Smart Farming, CSF）方案促进了小农户之间的资源共享，从而推动了前沿技术的采用。尽管精准农业的民主化使其更加普及，但也使其更容易受到网络威胁。为解决上述问题，所提出的方法引入了一种安全的联邦学习框架，并结合基于区块链的身份验证，用于CSF中的异常检测。所提出的方法采用基于联邦迁移学习的阴影注意力机制，结合Zeiler和Fergus网络，以提升异常检测技术的性能。这使得农场能够在本地检测威胁，同时保护数据隐私，因为仅传输加密的模型更新。构建了一种基于认证证明共识协议的区块链支持的去中心化身份验证方案，以提供信任和防篡改能力。此外，具有变化秩调整的随机k稀疏化技术对通信效率产生了积极影响，在保持相同精度的同时减少了通信开销。所提出的方法在四个数据集CSE-CIS-IDS2018、MQTTset、ToN-IoT和Edge IoT上执行，分别达到了99.99%、99.93%、99.94%和99.91%的分类准确率，且性能优于当前技术。该综合框架为CSF中的异常检测提供了一种可扩展且可信的方法，实现了具有韧性、隐私保护和智能化的智慧农业生态系统。","Engineering Applications of Artificial Intelligence","2026-09-24T00:00:00Z",81,{"impact":170,"substance":169,"depth":170,"authority":171,"freshness":96,"relevant":58,"comment":266},"提出联邦学习与区块链认证结合的农业异常检测框架，在四个数据集上验证高精度，方法新颖且具产业应用潜力，值得入选每日精选。",[268],{"name":262,"url":259},[23,101,270,271,25],"联邦学习","区块链",{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},[],{"status":35,"locked":14,"version":36,"method":37,"evidence":275},{"candidates":276,"agricultural_evidence":42},[277,283,287,294,300,306],{"code":50,"score":91,"supported":42,"title":278,"abstract":279,"tags":282},[],[23,280,281],"精准农业","precision agriculture",[23],{"code":28,"score":96,"supported":42,"title":284,"abstract":285,"tags":286},[],[46,47,44],[],{"code":288,"score":81,"supported":42,"title":289,"abstract":290,"tags":293},"A8",[],[291,292],"粮食安全","food security",[],{"code":295,"score":201,"supported":42,"title":296,"abstract":297,"tags":299},"G2",[],[298],"小农户",[],{"code":301,"score":201,"supported":42,"title":302,"abstract":303,"tags":305},"H6",[],[304],"农业生态",[],{"code":105,"score":219,"supported":14,"title":307,"abstract":308,"tags":309},[],[],[101,270],[311,312],"联邦学习 区块链 异常检测","协同智慧农业 物联网安全","联邦学习区块链异常检测-3471",4,"10.1016\u002Fj.engappai.2026.116072",{"doi":315,"openalex_id":317,"authors":318,"venue":262,"cited_by_count":15,"oa_url":259,"card":325,"direction":76,"ingested_from":78},"W7214230618",[319,321,323],{"name":320,"orcid":9},"Ramesh Pandharinath Daund",{"name":322,"orcid":9},"Mohammad Junedul Haque",{"name":324,"orcid":9},"Prof. Umesh B. Pawar",{"tldr":326,"method":327,"finding":328,"direction":76,"opportunity":329},"提出融合联邦迁移学习与区块链认证的异常检测框架，保障协作智慧农业数据隐私与安全。","联邦迁移学习、影子注意力机制、Zeiler-Fergus网络、区块链认证、随机k","在四个IoT数据集上分类准确率均超99.9%，且通信开销更低，优于现有方法。","可探索轻量级区块链共识与联邦学习在资源受限农业边缘设备上的能效与实时性优化。","2026-09-25T23:30:09.960528Z",{"id":332,"title":333,"url":334,"summary":335,"summary_zh":336,"content":9,"source_name":337,"source_url":334,"published_at":338,"category":12,"cover_url":9,"hotness":91,"is_selected":14,"score":264,"score_detail":339,"sources":341,"tags":343,"primary_topic":344,"secondary_topics":345,"classification":347,"search_phrases":367,"slug":370,"view_count":219,"doi":371,"paper":372,"created_at":399},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",{"impact":170,"substance":169,"depth":170,"authority":171,"freshness":96,"relevant":58,"comment":340},"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。",[342],{"name":337,"url":334},[23,101,177,270,24],{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},[346],{"code":105,"slug":106,"name":107,"path":108,"parent_code":32},{"status":35,"locked":14,"version":36,"method":37,"evidence":348},{"candidates":349,"agricultural_evidence":42},[350,354,358,363],{"code":28,"score":170,"supported":42,"title":351,"abstract":352,"tags":353},[44],[46,47,44],[177],{"code":105,"score":94,"supported":42,"title":355,"abstract":356,"tags":357},[],[102,119],[101,270],{"code":57,"score":314,"supported":42,"title":359,"abstract":360,"tags":362},[],[361],"agricultural data",[24],{"code":50,"score":58,"supported":14,"title":364,"abstract":365,"tags":366},[],[],[23],[368,369],"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":371,"openalex_id":373,"authors":374,"venue":337,"cited_by_count":15,"oa_url":334,"card":394,"direction":76,"ingested_from":78},"W7212387370",[375,378,381,384,387,389,391],{"name":376,"orcid":377},"Amina Khacha","https:\u002F\u002Forcid.org\u002F0009-0009-3299-8622",{"name":379,"orcid":380},"Zibouda Aliouat","https:\u002F\u002Forcid.org\u002F0000-0002-7007-7607",{"name":382,"orcid":383},"Yasmine Harbi","https:\u002F\u002Forcid.org\u002F0000-0001-6731-7895",{"name":385,"orcid":386},"Chirihane Gherbi","https:\u002F\u002Forcid.org\u002F0000-0002-0551-3978",{"name":388,"orcid":9},"Rafika Saadouni",{"name":390,"orcid":9},"Ado Adamou ABBA ARI",{"name":392,"orcid":393},"Hakim Mabed","https:\u002F\u002Forcid.org\u002F0000-0001-8358-4029",{"tldr":395,"method":396,"finding":397,"direction":76,"opportunity":398},"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":401,"title":402,"url":403,"summary":404,"summary_zh":405,"content":9,"source_name":406,"source_url":403,"published_at":407,"category":12,"cover_url":9,"hotness":91,"is_selected":14,"score":408,"score_detail":409,"sources":412,"tags":414,"primary_topic":417,"secondary_topics":418,"classification":419,"search_phrases":438,"slug":441,"view_count":58,"doi":442,"paper":443,"created_at":464},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":170,"substance":169,"depth":170,"authority":410,"freshness":94,"relevant":58,"comment":411},13,"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。",[413],{"name":406,"url":403},[23,177,415,24,416],"数据治理","农业工控",{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},[],{"status":35,"locked":14,"version":36,"method":37,"evidence":420},{"candidates":421,"agricultural_evidence":42},[422,426,430,434],{"code":28,"score":91,"supported":42,"title":423,"abstract":424,"tags":425},[],[46,47,44],[177],{"code":180,"score":81,"supported":42,"title":427,"abstract":428,"tags":429},[],[193,181],[],{"code":50,"score":314,"supported":42,"title":431,"abstract":432,"tags":433},[],[23],[23],{"code":57,"score":219,"supported":14,"title":435,"abstract":436,"tags":437},[],[],[415,24],[439,440],"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":442,"openalex_id":444,"authors":445,"venue":406,"cited_by_count":15,"oa_url":403,"card":459,"direction":76,"ingested_from":78},"W7212044568",[446,448,451,453,455,457],{"name":447,"orcid":9},"Radwan Rouzky",{"name":449,"orcid":450},"Abdolhossein Sarrafzadeh","https:\u002F\u002Forcid.org\u002F0000-0003-0816-589X",{"name":452,"orcid":9},"Evelyn Sowells-Boone",{"name":454,"orcid":9},"Jason Green",{"name":456,"orcid":9},"Hannaneh B. Pasandi",{"name":458,"orcid":9},"Gregory Goins",{"tldr":460,"method":461,"finding":462,"direction":76,"opportunity":463},"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",{"id":466,"title":467,"url":468,"summary":469,"summary_zh":470,"content":9,"source_name":471,"source_url":468,"published_at":472,"category":12,"cover_url":9,"hotness":91,"is_selected":14,"score":473,"score_detail":474,"sources":477,"tags":479,"primary_topic":480,"secondary_topics":481,"classification":482,"search_phrases":498,"slug":501,"view_count":314,"doi":502,"paper":503,"created_at":524},1884,"From perception to reaction: an RLCC-engine-driven cybersecurity framework for smart agricultural ecosystems","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10207-026-01327-w","Abstract Agriculture 4.0 makes significant use of interconnected sensors, autonomous farm equipment, cloud-based farm management systems, and data-driven decision-making. Even though these tools make farm operations more productive and sustainable, they also expose farm equipment, Internet of Things (IoT) devices, supply-chain management systems, and farm data to Cybersecurity attacks. Cybersecurity incidents in the agricultural sector may result in operational disruption, financial loss, and the compromise of data sovereignty. To address these risks, this study adopts a three-stage analytical approach consisting of challenge identification through literature and infrastructure analysis, prioritisation of the identified challenges, and systematic mapping onto a four-layer Agricultural Data-sharing Ecosystem (ADE) architecture comprising perception, network, edge, and cloud\u002Fapplication layers. The analysis identifies six structural challenges driving Cybersecurity risks in agriculture: data management, data governance, integration and interoperability, data privacy and security, knowledge gaps, and infrastructure limitations. Based on these findings, a conceptual multi-layer Cybersecurity framework is proposed that introduces a dedicated Reaction Layer enabling coordinated prevention, detection, and mitigation across ADE layers. The operational logic of the Reaction Layer is formalised through the Reaction Layer Cross-Layer Coordination (RLCC) algorithm, which specifies the sequential coordination of prevention, detection, mitigation, and audit actions across the ADE infrastructure layers, and links each response action to the identified challenges. To support the transition from conceptual design to practical deployment, an initial prototype architecture is outlined. It identifies which components can use existing technologies and which require new development. It also defines the scenarios and metrics that will be used to evaluate the framework.","农业4.0大量使用互联传感器、自主农业设备、基于云计算的农场管理系统以及数据驱动的决策。尽管这些工具提高了农场运营的生产力和可持续性，但也使农业设备、物联网（IoT）设备、供应链管理系统和农业数据面临网络安全攻击的风险。农业部门的网络安全事件可能导致运营中断、经济损失以及数据主权的受损。为应对这些风险，本研究采用三阶段分析方法，包括通过文献和基础设施分析识别挑战、对已识别挑战进行优先级排序，以及将其系统映射到由感知层、网络层、边缘层和云\u002F应用层组成的四层农业数据共享生态系统（ADE）架构上。分析识别出导致农业网络安全风险的六项结构性挑战：数据管理、数据治理、集成与互操作性、数据隐私与安全、知识差距以及基础设施局限性。基于这些发现，提出一个概念性的多层网络安全框架，引入专门的响应层，实现跨ADE各层的协调预防、检测和缓解。响应层的运行逻辑通过响应层跨层协调（RLCC）算法加以形式化，该算法规定了跨ADE基础设施各层的预防、检测、缓解和审计行动的序列协调，并将每项响应行动与已识别的挑战相关联。为支持从概念设计向实际部署的过渡，概述了一个初步原型架构。该架构明确了哪些组件可以使用现有技术，哪些需要新开发，并定义了用于评估该框架的场景和指标。","International Journal of Information Security","2026-09-07T00:00:00Z",77,{"impact":475,"substance":169,"depth":170,"authority":122,"freshness":219,"relevant":58,"comment":476},20,"提出农业网络安全框架，系统性强，但时效性较低。",[478],{"name":471,"url":468},[23,177,415,24],{"code":28,"slug":29,"name":30,"path":31,"parent_code":32},[],{"status":35,"locked":14,"version":36,"method":37,"evidence":483},{"candidates":484,"agricultural_evidence":42},[485,489,494],{"code":28,"score":91,"supported":42,"title":486,"abstract":487,"tags":488},[],[46,47,44],[177],{"code":57,"score":490,"supported":42,"title":491,"abstract":492,"tags":493},5,[],[361],[415,24],{"code":50,"score":58,"supported":14,"title":495,"abstract":496,"tags":497},[],[],[23],[499,500],"农业物联网 数据治理 智慧农业 网络安全","农业物联网 数据治理","农业物联网数据治理智慧农业网络安全-1884","10.1007\u002Fs10207-026-01327-w",{"doi":502,"openalex_id":504,"authors":505,"venue":471,"cited_by_count":15,"oa_url":468,"card":518,"direction":76,"ingested_from":78},"W7211869206",[506,508,511,513,515],{"name":507,"orcid":9},"Nasibeh Rahbar-Nodehi",{"name":509,"orcid":510},"Jouni Isoaho","https:\u002F\u002Forcid.org\u002F0000-0002-5789-3992",{"name":512,"orcid":9},"Seppo Virtanen",{"name":514,"orcid":9},"Tahir Mohammad",{"name":516,"orcid":517},"Eda Marchetti","https:\u002F\u002Forcid.org\u002F0000-0003-4223-8036",{"tldr":519,"method":520,"finding":521,"direction":522,"opportunity":523},"提出一个由RLCC引擎驱动的智慧农业网络安全框架，从感知到反应，保护农业生态系统。","三阶段分析：识别挑战、优先级排序、映射到四层ADE架构，设计RLCC算法。","识别出六类网络安全挑战，提出含反应层的多层框架，用RLCC算法协调预防、检测、缓解。","农业人工智能与决策模型","可探索RLCC算法在实际农业IoT环境中的部署与优化，以及与其他安全机制的集成。","2026-09-08T23:30:07.912905Z"]