Insular biodiversity has aroused the curiosity of biologists in assessing ecological and evolutionary hypotheses concerning their mainland counterparts.Comparisons of particular life history traits between island and ...Insular biodiversity has aroused the curiosity of biologists in assessing ecological and evolutionary hypotheses concerning their mainland counterparts.Comparisons of particular life history traits between island and mainland populations have provided a broader view of phenotypic and behavioral plasticity at both intraspecific and interspecific levels(Meiri 2007).展开更多
E-commerce has transformed Caoxian into a hub of traditional Chinese attire.STEPPING inside Caoxian’s Youai Cloud Warehouse,a livestreaming base dedicated to hanfu(traditional clothing),it feels less like a clothing ...E-commerce has transformed Caoxian into a hub of traditional Chinese attire.STEPPING inside Caoxian’s Youai Cloud Warehouse,a livestreaming base dedicated to hanfu(traditional clothing),it feels less like a clothing warehouse and more like a time capsule.Thousands of intricately embroidered garments line the racks-from elegant horse-face skirts to modernised editions of traditional Chinese clothing.展开更多
This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
As the European debt crisis deepens, the world economy has yet to find its footing despite signs of growth and renewed confidence. While the developed world faces the
This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and und...This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and underlying mechanisms.The results show that,during the developing winter of EP El Niño,significant SSR reductions occur in southwestern China and the east coast of southern China due to a strong,zonally extended Northwest Pacific anticyclone that transports moisture from the tropical Northwest Pacific and North Indian Ocean,while the northeast of southern China experiences a weak increase in SSR.In contrast,during the developing winter of CP El Niño,SSR decreases in the east of southern China with a significant decrease in the lower basin of the Yangtze River but an increase in the west of southern China with a remarkable increase in eastern Yunnan.The pronounced east-west dipole pattern in SSR anomalies is driven by a meridionally elongated Northwest Pacific anticyclone,which enhances northward moisture transport to the east of southern China while leaving western areas drier.Further research reveals that distinct moisture anomalies during the developing winter of EP and CP events result in divergent SSR distributions across southern China,primarily through modulating the total cloud cover.These findings highlight the critical need to differentiate between El Niño types when predicting medium and long-term variability of radiation in southern China.展开更多
Small and medium-sized enterprises in Zhejiang Province face serious headwinds In Wenzhou of Zhejiang Province,the boomtown of China’s private economy,three renowned manufacturers—Jiangnan Leather,Portman Coffee and
Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challen...Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
The characteristics of summertime raindrop size distribution(DSD) and associated relations in the semi-arid region over the Inner Mongolian Plateau(IMP) were investigated,utilizing five-year continuous observations by...The characteristics of summertime raindrop size distribution(DSD) and associated relations in the semi-arid region over the Inner Mongolian Plateau(IMP) were investigated,utilizing five-year continuous observations by a PARSIVEL2disdrometer in East Ujimqin County(EUC),China.It is found that only 7.94% of the 15 664 one-min precipitation samples meet classification criteria as convective rain(CR),but its contribution to the total rainfall amount is 63.87%.Notably,40.72% of the rainfall comes from large-sized raindrops(D> 3 mm),despite the fact that large-sized raindrops account for only 1.73% of the CR total number concentration.Further results show that the mean value of mass-weighted mean diameters(Dm) is larger(2.43 mm) and generalized intercepts(lgN_(W)) is lower(3.19) in CR,aligning with a "continentallike" cluster,which is mainly influenced by the joint impact of in-cloud ice-based processes and the below-cloud environmental background.Also,the empirical relationships of shape-slope(μ-Λ),radar reflectivity-rain rate(Z-R),and rainfall kinetic energy(KE_(time)-Rand KE_(time)-Z) are localized.To quantitatively analyze the impact of DSD parameters on kinetic energy estimation,power-law KE_(time)-R and KE_(time)-Z relationships are derived based on the normalized gamma distribution.N_(W)takes precedence over μ in affecting variabilities of multiplicative coefficients,especially for KE_(time)-R relationship where the multiplicative coefficient is proportional to N_(W)^(-0.287).It should be noted that although the proportion of CR occurring throughout the summer is small,raindrops with lower N_(W) and larger Dmwill generate higher KE_(time),which will bring a higher potential risk of soil erosion in semi-arid regions over IMP.展开更多
Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of th...Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.展开更多
The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods fo...The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods for centroid measurement often necessitate custom equipment and specialized positioning devices,leading to high costs and limited accuracy.Here,we present a centroid measurement method that integrates 3D scanning technology,enabling accurate measurement of centroid across various types of objects without the need for specialized positioning fixtures.A theoretical framework for centroid measurement was established,which combined the principle of the multi-point weighing method with 3D scanning technology.The measurement accuracy was evaluated using a designed standard component.Experimental results demonstrate that the discrepancies between the theoretical and the measured centroid of a standard component with various materials and complex shapes in the X,Y,and Z directions are 0.003 mm,0.009 mm,and 0.105 mm,respectively,yielding a spatial deviation of 0.106 mm.Qualitative verification was conducted through experimental validation of three distinct types.They confirmed the reliability of the proposed method,which allowed for accurate centroid measurements of various products without requiring positioning fixtures.This advancement significantly broadened the applicability and scope of centroid measurement devices,offering new theoretical insights and methodologies for the measurement of complex parts and systems.展开更多
The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic.Cloud environments pose significant challenges in maintaining privacy and security.Global approach...The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic.Cloud environments pose significant challenges in maintaining privacy and security.Global approaches,such as IDS,have been developed to tackle these issues.However,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional data.In fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within it.The traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of features.The selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)module.In this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious traffic.The classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable decisions.With the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive measures.Extensive experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different types.Theproposedmodel outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%F1-score.Such results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.展开更多
基金We are deeply thankful to Neftali Camacho(LACM),Addison Wynn(NMNH),Esther Langan(NMNH),Christina K.Sami(NMNH),David A.Kizirian(AMNH),and Lauren Vonnahme(AMNH)for logistic support to visiting each museum.Shai Meiri and anonymous reviewers provided insightful suggestions that improved the manuscript,and Louis W.Porras also improves the English of the manuscript.Finally,we thank Jose Gonzalez and Ismael Huerta for help in preparing the figure.
文摘Insular biodiversity has aroused the curiosity of biologists in assessing ecological and evolutionary hypotheses concerning their mainland counterparts.Comparisons of particular life history traits between island and mainland populations have provided a broader view of phenotypic and behavioral plasticity at both intraspecific and interspecific levels(Meiri 2007).
文摘E-commerce has transformed Caoxian into a hub of traditional Chinese attire.STEPPING inside Caoxian’s Youai Cloud Warehouse,a livestreaming base dedicated to hanfu(traditional clothing),it feels less like a clothing warehouse and more like a time capsule.Thousands of intricately embroidered garments line the racks-from elegant horse-face skirts to modernised editions of traditional Chinese clothing.
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
文摘As the European debt crisis deepens, the world economy has yet to find its footing despite signs of growth and renewed confidence. While the developed world faces the
基金funded by a Project from China Southern Power Grid Company Ltd.(Nos.ZBKJXM20232481 and ZBKJXM20232482)。
文摘This study investigates the distinct impacts of eastern Pacific(EP)and central Pacific(CP)El Niño events on winter shortwave solar radiation(SSR)in southern China,revealing different spatial distributions and underlying mechanisms.The results show that,during the developing winter of EP El Niño,significant SSR reductions occur in southwestern China and the east coast of southern China due to a strong,zonally extended Northwest Pacific anticyclone that transports moisture from the tropical Northwest Pacific and North Indian Ocean,while the northeast of southern China experiences a weak increase in SSR.In contrast,during the developing winter of CP El Niño,SSR decreases in the east of southern China with a significant decrease in the lower basin of the Yangtze River but an increase in the west of southern China with a remarkable increase in eastern Yunnan.The pronounced east-west dipole pattern in SSR anomalies is driven by a meridionally elongated Northwest Pacific anticyclone,which enhances northward moisture transport to the east of southern China while leaving western areas drier.Further research reveals that distinct moisture anomalies during the developing winter of EP and CP events result in divergent SSR distributions across southern China,primarily through modulating the total cloud cover.These findings highlight the critical need to differentiate between El Niño types when predicting medium and long-term variability of radiation in southern China.
文摘Small and medium-sized enterprises in Zhejiang Province face serious headwinds In Wenzhou of Zhejiang Province,the boomtown of China’s private economy,three renowned manufacturers—Jiangnan Leather,Portman Coffee and
基金supported by the Science and Technology Project of Jiangsu Coastal Power Infrastructure Intelligent Engineering Research Center“Photovoltaic Power Prediction System Driven by Deep Learning and Multi-Source Data Fusion”(F2024-5044).
文摘Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources.However,the fluctuations and intermittency of photovoltaic(PV)power pose challenges for its extensive incorporation into power grids.Thus,enhancing the precision of PV power prediction is particularly important.Although existing studies have made progress in short-term prediction,issues persist,particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data.These factors hinder improvements in PV power prediction performance.To overcome these challenges,this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning.First,the improved LSTMand SA-ConvLSTMare employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images,respectively.Subsequently,a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities,enhancing the capacity to focus on the most relevant features.Finally,theTransformermodel is applied to further capture the short-termtemporal patterns and long-term dependencies within multi-modal feature information.The paper also compares the proposed method with various competitive methods.The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
基金supported by the National Natural Science Foundation of China(Grant Nos.42325503,42075063,42075066,and 42021004)the Hubei Provincial Natural Science Foundation and the Meteorological Innovation and Development Project of China(Grant No.2023AFD096)the Beijige Foundation of NJIAS(Grant No.BJG202304).
文摘The characteristics of summertime raindrop size distribution(DSD) and associated relations in the semi-arid region over the Inner Mongolian Plateau(IMP) were investigated,utilizing five-year continuous observations by a PARSIVEL2disdrometer in East Ujimqin County(EUC),China.It is found that only 7.94% of the 15 664 one-min precipitation samples meet classification criteria as convective rain(CR),but its contribution to the total rainfall amount is 63.87%.Notably,40.72% of the rainfall comes from large-sized raindrops(D> 3 mm),despite the fact that large-sized raindrops account for only 1.73% of the CR total number concentration.Further results show that the mean value of mass-weighted mean diameters(Dm) is larger(2.43 mm) and generalized intercepts(lgN_(W)) is lower(3.19) in CR,aligning with a "continentallike" cluster,which is mainly influenced by the joint impact of in-cloud ice-based processes and the below-cloud environmental background.Also,the empirical relationships of shape-slope(μ-Λ),radar reflectivity-rain rate(Z-R),and rainfall kinetic energy(KE_(time)-Rand KE_(time)-Z) are localized.To quantitatively analyze the impact of DSD parameters on kinetic energy estimation,power-law KE_(time)-R and KE_(time)-Z relationships are derived based on the normalized gamma distribution.N_(W)takes precedence over μ in affecting variabilities of multiplicative coefficients,especially for KE_(time)-R relationship where the multiplicative coefficient is proportional to N_(W)^(-0.287).It should be noted that although the proportion of CR occurring throughout the summer is small,raindrops with lower N_(W) and larger Dmwill generate higher KE_(time),which will bring a higher potential risk of soil erosion in semi-arid regions over IMP.
基金supported By Grant (PLN2022-14) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)。
文摘Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.
基金supported by National Natural Science Foundation of China(No.52176122).
文摘The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods for centroid measurement often necessitate custom equipment and specialized positioning devices,leading to high costs and limited accuracy.Here,we present a centroid measurement method that integrates 3D scanning technology,enabling accurate measurement of centroid across various types of objects without the need for specialized positioning fixtures.A theoretical framework for centroid measurement was established,which combined the principle of the multi-point weighing method with 3D scanning technology.The measurement accuracy was evaluated using a designed standard component.Experimental results demonstrate that the discrepancies between the theoretical and the measured centroid of a standard component with various materials and complex shapes in the X,Y,and Z directions are 0.003 mm,0.009 mm,and 0.105 mm,respectively,yielding a spatial deviation of 0.106 mm.Qualitative verification was conducted through experimental validation of three distinct types.They confirmed the reliability of the proposed method,which allowed for accurate centroid measurements of various products without requiring positioning fixtures.This advancement significantly broadened the applicability and scope of centroid measurement devices,offering new theoretical insights and methodologies for the measurement of complex parts and systems.
文摘The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic.Cloud environments pose significant challenges in maintaining privacy and security.Global approaches,such as IDS,have been developed to tackle these issues.However,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional data.In fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within it.The traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of features.The selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)module.In this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious traffic.The classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable decisions.With the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive measures.Extensive experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different types.Theproposedmodel outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%F1-score.Such results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.
