In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusi...In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.展开更多
Sustainable agriculture plays a crucial role in meeting the growing global demand for food while minimizing adverse environmental impacts from the overuse of synthetic pesticides and conventional fertilizers.In this c...Sustainable agriculture plays a crucial role in meeting the growing global demand for food while minimizing adverse environmental impacts from the overuse of synthetic pesticides and conventional fertilizers.In this context,renewable biopolymers being more sustainable offer a viable solution to improve agricultural sustainability and production.Nano/micro-structural supramolecular biopolymers are among these innovative biopolymers that are much sought after for their unique features.These biomaterials have complex hierarchical structures,great stability,adjustable mechanical strength,stimuli-responsiveness,and self-healing attributes.Functional molecules may be added to their flexible structure,for enabling novel agricultural uses.This overview scrutinizes how nano/micro-structural supramolecular biopolymers may radically alter farming practices and solve lingering problems in agricultural sector namely improve agricultural production,soil health,and resource efficiency.Controlled bioactive ingredient released from biopolymers allows the tailored administration of agrochemicals,bioactive agents,and biostimulators as they enhance nutrient absorption,moisture retention,and root growth.Nano/micro-structural supramolecular biopolymers may protect crops by appending antimicrobials and biosensing entities while their eco-friendliness supports sustainable agriculture.Despite their potential,further studies are warranted to understand and optimize their usage in agricultural domain.This effort seeks to bridge the knowledge gap by investigating their applications,challenges,and future prospects in the agricultural sector.Through experimental investigations and theoretical modeling,this overview aims to provide valuable insights into the practical implementation and optimization of supramolecular biopolymers in sustainable agriculture,ultimately contributing to the development of innovative and eco-friendly solutions to enhance agricultural productivity while minimizing environmental impact.展开更多
近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数...近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数处理程序的自动化XLM反混淆与关键威胁指标(IOC,indicators of compromise)提取系统XLMRevealer;在此基础上,根据XLM代码特点提取Word和Token特征,通过特征融合能够捕获多层次细粒度特征,并在XLMRevealer中构造CNN-BiLSTM(convolution neural network-bidirectional long short term memory)模型,从不同维度挖掘家族样本的关联性和完成家族分类。最后,从5个来源构建包含2346个样本的数据集并用于反混淆实验和家族分类实验。实验结果表明,XLMRevealer的反混淆成功率达到71.3%,相比XLMMacroDeobfuscator和SYMBEXCEL工具分别提高了20.8%和15.8%;反混淆效率稳定,平均耗时仅为0.512 s。XLMRevealer对去混淆XLM代码的家族分类准确率高达94.88%,效果优于所有基线模型,有效体现Word和Token特征融合的优势。此外,为探索反混淆对家族分类的影响,并考虑不同家族使用的混淆技术可能有所不同,模型会识别到混淆技术的特征,分别对反混淆前和反混淆后再统一混淆的XLM代码进行实验,家族分类准确率为89.58%、53.61%,证明模型能够学习混淆技术特征,更验证了反混淆对家族分类极大的促进作用。展开更多
Aims and Scope Journal of IntegrativeAgriculture(JIA),formerly Agricuiltural Sciences in China(ASC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinsese Association o...Aims and Scope Journal of IntegrativeAgriculture(JIA),formerly Agricuiltural Sciences in China(ASC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinsese Association of Agricultural Science Societies(CAAsS).The latest IF is 4.8.JIA seeks to publish those papers that are influential and will significantly advance scientific understanding in agriculture fields worldwide.JIA publishes manuscripts in the categories of Commentary,Review,Research Article,Letter and Short Communication,focusing on the core subjects:Crop Science Horticulture·Plant ProtectionAnimal Science·Veterinary Medicine·Agro-ecosystem&Environment·Food Science·Agricultural Economics and Management·Agricultural Information Science.展开更多
基金supported in part by the Research Start-Up Fund for Talent Researcher of Nanjing Agricultural University(77H0603)in part by the National Natural Science Foundation of China(62072248)。
文摘In this paper,we review and analyze intrusion detection systems for Agriculture 4.0 cyber security.Specifically,we present cyber security threats and evaluation metrics used in the performance evaluation of an intrusion detection system for Agriculture 4.0.Then,we evaluate intrusion detection systems according to emerging technologies,including,Cloud computing,Fog/Edge computing,Network virtualization,Autonomous tractors,Drones,Internet of Things,Industrial agriculture,and Smart Grids.Based on the machine learning technique used,we provide a comprehensive classification of intrusion detection systems in each emerging technology.Furthermore,we present public datasets,and the implementation frameworks applied in the performance evaluation of intrusion detection systems for Agriculture 4.0.Finally,we outline challenges and future research directions in cyber security intrusion detection for Agriculture 4.0.
基金support provided by the UKRI via Grant No.EP/T024607/1Royal Society via grant number IES\R2\222208.
文摘Sustainable agriculture plays a crucial role in meeting the growing global demand for food while minimizing adverse environmental impacts from the overuse of synthetic pesticides and conventional fertilizers.In this context,renewable biopolymers being more sustainable offer a viable solution to improve agricultural sustainability and production.Nano/micro-structural supramolecular biopolymers are among these innovative biopolymers that are much sought after for their unique features.These biomaterials have complex hierarchical structures,great stability,adjustable mechanical strength,stimuli-responsiveness,and self-healing attributes.Functional molecules may be added to their flexible structure,for enabling novel agricultural uses.This overview scrutinizes how nano/micro-structural supramolecular biopolymers may radically alter farming practices and solve lingering problems in agricultural sector namely improve agricultural production,soil health,and resource efficiency.Controlled bioactive ingredient released from biopolymers allows the tailored administration of agrochemicals,bioactive agents,and biostimulators as they enhance nutrient absorption,moisture retention,and root growth.Nano/micro-structural supramolecular biopolymers may protect crops by appending antimicrobials and biosensing entities while their eco-friendliness supports sustainable agriculture.Despite their potential,further studies are warranted to understand and optimize their usage in agricultural domain.This effort seeks to bridge the knowledge gap by investigating their applications,challenges,and future prospects in the agricultural sector.Through experimental investigations and theoretical modeling,this overview aims to provide valuable insights into the practical implementation and optimization of supramolecular biopolymers in sustainable agriculture,ultimately contributing to the development of innovative and eco-friendly solutions to enhance agricultural productivity while minimizing environmental impact.
文摘近年来,使用恶意Excel 4.0宏(XLM)文档的攻击迎来了爆发,而XLM代码往往经过复杂的混淆,现有方法或检测系统难以分析海量样本的真实功能。因此,针对恶意样本中使用的各类混淆技术,基于抽象语法树和模拟执行,设计和实现了包含138个宏函数处理程序的自动化XLM反混淆与关键威胁指标(IOC,indicators of compromise)提取系统XLMRevealer;在此基础上,根据XLM代码特点提取Word和Token特征,通过特征融合能够捕获多层次细粒度特征,并在XLMRevealer中构造CNN-BiLSTM(convolution neural network-bidirectional long short term memory)模型,从不同维度挖掘家族样本的关联性和完成家族分类。最后,从5个来源构建包含2346个样本的数据集并用于反混淆实验和家族分类实验。实验结果表明,XLMRevealer的反混淆成功率达到71.3%,相比XLMMacroDeobfuscator和SYMBEXCEL工具分别提高了20.8%和15.8%;反混淆效率稳定,平均耗时仅为0.512 s。XLMRevealer对去混淆XLM代码的家族分类准确率高达94.88%,效果优于所有基线模型,有效体现Word和Token特征融合的优势。此外,为探索反混淆对家族分类的影响,并考虑不同家族使用的混淆技术可能有所不同,模型会识别到混淆技术的特征,分别对反混淆前和反混淆后再统一混淆的XLM代码进行实验,家族分类准确率为89.58%、53.61%,证明模型能够学习混淆技术特征,更验证了反混淆对家族分类极大的促进作用。
文摘Aims and Scope Journal of IntegrativeAgriculture(JIA),formerly Agricuiltural Sciences in China(ASC),founded in 2002,is sponsored by Chinese Academy of Agricultural Sciences(CAAS),co-sponsored by Chinsese Association of Agricultural Science Societies(CAAsS).The latest IF is 4.8.JIA seeks to publish those papers that are influential and will significantly advance scientific understanding in agriculture fields worldwide.JIA publishes manuscripts in the categories of Commentary,Review,Research Article,Letter and Short Communication,focusing on the core subjects:Crop Science Horticulture·Plant ProtectionAnimal Science·Veterinary Medicine·Agro-ecosystem&Environment·Food Science·Agricultural Economics and Management·Agricultural Information Science.
