[目的/意义]梳理国际国家安全情报研究发展脉络与知识生产特征,揭示关键学者的群体画像、职业发展模式、合作网络结构与核心研究议题演进,以期为推动我国安全情报学科建设提供借鉴。[方法/过程]基于发文量标准,从Intelligence and Natio...[目的/意义]梳理国际国家安全情报研究发展脉络与知识生产特征,揭示关键学者的群体画像、职业发展模式、合作网络结构与核心研究议题演进,以期为推动我国安全情报学科建设提供借鉴。[方法/过程]基于发文量标准,从Intelligence and National Security期刊中筛选出核心著者,运用履历分析法将国外核心著者履历划分为学科背景、研究方向、科研成果和工作经历4个核心类属进行比较分析,采用LDA主题模型对发文进行主题挖掘,系统识别出情报研究者关注的核心议题。[结果/结论]核心著者群体呈现显著的男性主导、中老年资深学者为主、机构高度集中、学科背景偏重传统人文社科的特征;安全情报研究面临跨学科深度融合不足、学界与实践存在隔阂、技术伦理与法律探讨滞后等问题。展开更多
Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a cr...Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a critical objective,aiming for comprehensive indigenous replacement through rapid technological iteration.Consequently,Xinchuang systems and Windows platforms are expected to coexist over an extended period.This study seeks to establish an automated verification framework for multi-version operating systems and validate the efficacy of baseline hardening in mitigating security risks.Design/methodology/approach-Based on the Classified Protection 2.0 framework and relevant national standards for endpoint security,this study proposes an endpoint security baseline verification scheme applicable to multiple operating systems.The scheme addresses divergent security policies and implementation methodologies across heterogeneous environments.It automates the inspection of core baselines,including account password complexity,default shared service status and patch installation status.Furthermore,a comprehensive scoring model is established by incorporating differentiated weights for account security,patch management and log auditing,ultimately generating visualized risk reports to facilitate remediation prioritization.Findings-This study reveals that baseline configuration serves as the fundamental prerequisite in endpoint security practices.Through a scalable detection engine and quantitative scoring model,the system can promptly identify and remediate potential risks,thereby reducing the attack surface and mitigating intrusion risks.However,on certain domestic chip architectures,compatibility issues persist in detecting specific configuration items.Further improvement in hardware-software co-adaptation for domestic platforms is required to advance the development of localized security protection systems.Originality/value-Through in-depth research on security baseline configurations across multiple operating systems,this study implements an automated and visualized baseline verification methodology.This approach significantly strengthens the security posture of domestic operating systems and supports the establishment of a more robust,national-level cybersecurity defense framework.展开更多
Malnutrition remains a significant global challenge,particularly in developing countries.Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity(FPD)...Malnutrition remains a significant global challenge,particularly in developing countries.Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity(FPD).While research indicates that FPD correlates positively with reduced malnutrition,other studies emphasize the importance of market access for improved nutritional outcomes.However,this evidence varies by region and remains inconsistent.To address this knowledge gap,this study analyzed survey data from 450 smallholder farmers in Punjab,Pakistan,using regression models to examine the relationship between FPD and dietary diversity,as well as the underlying impact pathways.The findings demonstrate that FPD significantly correlates with increased household dietary diversity score(HDDS).FPD influences dietary diversification through both own-farm production and market food consumption pathways,with the ownfarm production pathway showing greater impact.The increase in food expenditure through own-farm production yielded a marginal return of 8% in household dietary diversity compared to 5.3% through marketing.Gender differences emerged as significant,with male-headed households showing relatively lower dietary diversity.These findings have substantial implications for countries with smallholder farming systems,providing valuable insights for the formation of agricultural policies,resource optimization,and rural development initiatives.展开更多
为了快速识别市场中的劣质食用油,提出了一种结合激光诱导荧光(laser-induced fluorescence,LIF)技术与偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)的高品质食用油掺伪鉴别方法。首先利用实验室搭建的LIF...为了快速识别市场中的劣质食用油,提出了一种结合激光诱导荧光(laser-induced fluorescence,LIF)技术与偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)的高品质食用油掺伪鉴别方法。首先利用实验室搭建的LIF系统采集了橄榄油、芝麻油和花生油及其掺伪样本的荧光光谱数据;然后基于PLS-DA方法分别为橄榄油、芝麻油和花生油构建了掺伪鉴别模型;最后通过预测集对模型性能进行了评估。结果表明,PLS-DA模型能够准确捕捉掺伪样本与真实样本荧光光谱之间的差异性特征,在实验所得数据验证下,达到了100%的分类准确率。该方法可实现对掺伪食用油的高精度鉴别,为食品安全监管提供了科学的鉴别手段。展开更多
Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,B...Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.展开更多
The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreser...The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processin...As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processing and enabled significant improvements in various applications.This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models(LLM).Our primary objective is to evaluate the effectiveness of Static Application Security Testing(SAST)by applying various techniques such as prompt persona,structure outputs and zero-shot.To the selection of the LLMs(CodeLlama 7B,DeepSeek coder 7B,Gemini 1.5 Flash,Gemini 2.0 Flash,Mistral 7b Instruct,Phi 38b Mini 128K instruct,Qwen 2.5 coder,StartCoder 27B)with comparison and combination with Find Security Bugs.The evaluation method will involve using a selected dataset containing vulnerabilities,and the results to provide insights for different scenarios according to the software criticality(Business critical,non-critical,minimum effort,best effort)In detail,the main objectives of this study are to investigate if large language models outperform or exceed the capabilities of traditional static analysis tools,if the combining LLMs with Static Application Security Testing(SAST)tools lead to an improvement and the possibility that local machine learning models on a normal computer produce reliable results.Summarizing the most important conclusions of the research,it can be said that while it is true that the results have improved depending on the size of the LLM for business-critical software,the best results have been obtained by SAST analysis.This differs in“NonCritical,”“Best Effort,”and“Minimum Effort”scenarios,where the combination of LLM(Gemini)+SAST has obtained better results.展开更多
The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)...The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.展开更多
This paper evaluates the performance of Internet Protocol Security (IPSec) based Multiprotocol Label Switching (MPLS) virtual private network (VPN) in a small to medium sized organization. The demand for security in d...This paper evaluates the performance of Internet Protocol Security (IPSec) based Multiprotocol Label Switching (MPLS) virtual private network (VPN) in a small to medium sized organization. The demand for security in data networks has been increasing owing to the high cyber attacks and potential risks associated with networks spread over distant geographical locations. The MPLS networks ride on the public network backbone that is porous and highly susceptible to attacks and so the need for reliable security mechanisms to be part of the deployment plan. The evaluation criteria concentrated on Voice over Internet Protocol (VoIP) and Video conferencing with keen interest in jitter, end to end delivery and general data flow. This study used both structured questionnaire and observation methods. The structured questionnaire was administered to a group of 70 VPN users in a company. This provided the study with precise responses. The observation method was used in data simulations using OPNET Version 14.5 Simulation software. The results show that the IPSec features increase the size of data packets by approximately 9.98% translating into approximately 90.02% effectiveness. The tests showed that the performance metrics are all well within the recommended standards. The IPSec Based MPLS Virtual private network is more stable and secure than one without IPSec.展开更多
数字化教学能力是教育数字化转型时期教师必备的关键能力,深入探究并分析教师数字化教学能力的影响因素,是教育管理者和教师应对技术挑战、提升教学质量、培养创新人才的关键环节。基于此,文章依托统一接受和使用技术(Unified Theory of...数字化教学能力是教育数字化转型时期教师必备的关键能力,深入探究并分析教师数字化教学能力的影响因素,是教育管理者和教师应对技术挑战、提升教学质量、培养创新人才的关键环节。基于此,文章依托统一接受和使用技术(Unified Theory of Acceptance and Use of Technology,UTAUT)模型,采用偏最小二乘结构方程模型(Partial Least Squares Structural Equation Modeling,PLS-SEM)和模糊集定性比较分析(Fuzzy-Set Qualitative Comparative Analysis,fsQCA)方法,对教师数字化教学能力的影响因素及其组合效应进行了实证分析。其中,PLS-SEM分析结果表明,绩效期望、努力期望、社群影响、便利条件和自我效能感对教师数字化教学意愿有显著的正向影响,并进一步正向影响教师的数字化教学能力;教师的自我效能感对数字化教学能力有显著的直接影响,且影响效应最强。而fs QCA分析结果显示,存在四条激发教师数字化教学能力的路径,在这些路径中数字化教学意愿和自我效能感是两个重要的前因变量,这弥补了结构方程模型分析的相对不足。文章通过研究,旨在为教育数字化转型时期教师数字化教学能力的提升提供实证依据。展开更多
水稻类病斑突变体在研究水稻细胞程序性死亡和广谱抗病性中具有重要作用,已报道的水稻类病斑主要发生在叶片上,少量发生在颖壳上。本研究中首次报道了水稻的一种穗叶类病斑突变体pls1(Panicle and leaf spot 1),其从三叶期叶片开始出现...水稻类病斑突变体在研究水稻细胞程序性死亡和广谱抗病性中具有重要作用,已报道的水稻类病斑主要发生在叶片上,少量发生在颖壳上。本研究中首次报道了水稻的一种穗叶类病斑突变体pls1(Panicle and leaf spot 1),其从三叶期叶片开始出现红褐色斑点,随生育进程扩大,并扩展到其他器官。与以往报道的水稻类病斑突变体不同的是,pls1抽穗后稻穗枝梗和颖壳逐渐产生红褐色病斑,成熟期稻穗干枯,严重影响产量,是一种新类型的水稻类病斑。结合图位克隆和全基因组重测序发现pls1突变体产生了173403 bp的大片段缺失,导致7个基因缺失和1个基因启动子缺失。这8个基因中4个编码醇溶蛋白,另外3个在叶片和穗部表达量较低,只有Os12g0268000在叶片和稻穗中较其他器官有较高的表达量,推测PLS1为Os12g0268000,基因功能注释显示其编码色胺5-羟化酶。pls1突变体叶片中活性氧、过氧化氢、超氧阴离子过量积累,抗氧系统相关酶氧化物歧化酶、抗坏血酸过氧化物酶、过氧化氢酶和谷胱甘肽还原酶活性提高,发生细胞程序性死亡和叶绿体降解,降低光合能力。褪黑素在植物耐盐性中起重要作用。进一步的功能分析发现,缺失PLS1会抑制水稻中褪黑素合成相关酶基因OsTDC1、OsTDC3、OsSNAT1、OsASMT1和OsCOMT的表达,进而导致pls1突变体的耐盐性下降。综上,穗叶类病斑突变体pls1是一种新类型的水稻类病斑突变体,将为水稻类病斑研究提供新的种质材料;耐盐性的分析揭示了色胺5-羟化酶的新功能,为研究其在细胞程序性死亡和耐盐性中的机制提供了新视角。展开更多
文摘[目的/意义]梳理国际国家安全情报研究发展脉络与知识生产特征,揭示关键学者的群体画像、职业发展模式、合作网络结构与核心研究议题演进,以期为推动我国安全情报学科建设提供借鉴。[方法/过程]基于发文量标准,从Intelligence and National Security期刊中筛选出核心著者,运用履历分析法将国外核心著者履历划分为学科背景、研究方向、科研成果和工作经历4个核心类属进行比较分析,采用LDA主题模型对发文进行主题挖掘,系统识别出情报研究者关注的核心议题。[结果/结论]核心著者群体呈现显著的男性主导、中老年资深学者为主、机构高度集中、学科背景偏重传统人文社科的特征;安全情报研究面临跨学科深度融合不足、学界与实践存在隔阂、技术伦理与法律探讨滞后等问题。
基金supported by scientific research projects of China Academy of Railway Sciences Co.,Ltd.(grant no.2024YJ117).
文摘Purpose-Amidst an increasingly severe cybersecurity landscape,the widespread adoption of Xinchuang endpoints has become a strategic imperative.Governments and enterprises have established terminal localization as a critical objective,aiming for comprehensive indigenous replacement through rapid technological iteration.Consequently,Xinchuang systems and Windows platforms are expected to coexist over an extended period.This study seeks to establish an automated verification framework for multi-version operating systems and validate the efficacy of baseline hardening in mitigating security risks.Design/methodology/approach-Based on the Classified Protection 2.0 framework and relevant national standards for endpoint security,this study proposes an endpoint security baseline verification scheme applicable to multiple operating systems.The scheme addresses divergent security policies and implementation methodologies across heterogeneous environments.It automates the inspection of core baselines,including account password complexity,default shared service status and patch installation status.Furthermore,a comprehensive scoring model is established by incorporating differentiated weights for account security,patch management and log auditing,ultimately generating visualized risk reports to facilitate remediation prioritization.Findings-This study reveals that baseline configuration serves as the fundamental prerequisite in endpoint security practices.Through a scalable detection engine and quantitative scoring model,the system can promptly identify and remediate potential risks,thereby reducing the attack surface and mitigating intrusion risks.However,on certain domestic chip architectures,compatibility issues persist in detecting specific configuration items.Further improvement in hardware-software co-adaptation for domestic platforms is required to advance the development of localized security protection systems.Originality/value-Through in-depth research on security baseline configurations across multiple operating systems,this study implements an automated and visualized baseline verification methodology.This approach significantly strengthens the security posture of domestic operating systems and supports the establishment of a more robust,national-level cybersecurity defense framework.
基金appreciation to the National Natural Science Foundation of China(72071074)Natural Science Foundation of Hunan Province,China(2025JJ30031)for their financial support。
文摘Malnutrition remains a significant global challenge,particularly in developing countries.Policymakers have increasingly focused on improving household food security and nutrition through farm production diversity(FPD).While research indicates that FPD correlates positively with reduced malnutrition,other studies emphasize the importance of market access for improved nutritional outcomes.However,this evidence varies by region and remains inconsistent.To address this knowledge gap,this study analyzed survey data from 450 smallholder farmers in Punjab,Pakistan,using regression models to examine the relationship between FPD and dietary diversity,as well as the underlying impact pathways.The findings demonstrate that FPD significantly correlates with increased household dietary diversity score(HDDS).FPD influences dietary diversification through both own-farm production and market food consumption pathways,with the ownfarm production pathway showing greater impact.The increase in food expenditure through own-farm production yielded a marginal return of 8% in household dietary diversity compared to 5.3% through marketing.Gender differences emerged as significant,with male-headed households showing relatively lower dietary diversity.These findings have substantial implications for countries with smallholder farming systems,providing valuable insights for the formation of agricultural policies,resource optimization,and rural development initiatives.
文摘为了快速识别市场中的劣质食用油,提出了一种结合激光诱导荧光(laser-induced fluorescence,LIF)技术与偏最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)的高品质食用油掺伪鉴别方法。首先利用实验室搭建的LIF系统采集了橄榄油、芝麻油和花生油及其掺伪样本的荧光光谱数据;然后基于PLS-DA方法分别为橄榄油、芝麻油和花生油构建了掺伪鉴别模型;最后通过预测集对模型性能进行了评估。结果表明,PLS-DA模型能够准确捕捉掺伪样本与真实样本荧光光谱之间的差异性特征,在实验所得数据验证下,达到了100%的分类准确率。该方法可实现对掺伪食用油的高精度鉴别,为食品安全监管提供了科学的鉴别手段。
文摘Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.
文摘The advent of quantum computing poses a significant challenge to traditional cryptographic protocols,particularly those used in SecureMultiparty Computation(MPC),a fundamental cryptographic primitive for privacypreserving computation.Classical MPC relies on cryptographic techniques such as homomorphic encryption,secret sharing,and oblivious transfer,which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries.This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI,IEEE Explore,Springer,and Elsevier,examining the applications,types,and security issues with the solution of Quantum computing in different fields.This review explores the impact of quantum computing on MPC security,assesses emerging quantum-resistant MPC protocols,and examines hybrid classicalquantum approaches aimed at mitigating quantum threats.We analyze the role of Quantum Key Distribution(QKD),post-quantum cryptography(PQC),and quantum homomorphic encryption in securing multiparty computations.Additionally,we discuss the challenges of scalability,computational efficiency,and practical deployment of quantumsecure MPC frameworks in real-world applications such as privacy-preserving AI,secure blockchain transactions,and confidential data analysis.This review provides insights into the future research directions and open challenges in ensuring secure,scalable,and quantum-resistant multiparty computation.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
文摘As artificial Intelligence(AI)continues to expand exponentially,particularly with the emergence of generative pre-trained transformers(GPT)based on a transformer’s architecture,which has revolutionized data processing and enabled significant improvements in various applications.This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models(LLM).Our primary objective is to evaluate the effectiveness of Static Application Security Testing(SAST)by applying various techniques such as prompt persona,structure outputs and zero-shot.To the selection of the LLMs(CodeLlama 7B,DeepSeek coder 7B,Gemini 1.5 Flash,Gemini 2.0 Flash,Mistral 7b Instruct,Phi 38b Mini 128K instruct,Qwen 2.5 coder,StartCoder 27B)with comparison and combination with Find Security Bugs.The evaluation method will involve using a selected dataset containing vulnerabilities,and the results to provide insights for different scenarios according to the software criticality(Business critical,non-critical,minimum effort,best effort)In detail,the main objectives of this study are to investigate if large language models outperform or exceed the capabilities of traditional static analysis tools,if the combining LLMs with Static Application Security Testing(SAST)tools lead to an improvement and the possibility that local machine learning models on a normal computer produce reliable results.Summarizing the most important conclusions of the research,it can be said that while it is true that the results have improved depending on the size of the LLM for business-critical software,the best results have been obtained by SAST analysis.This differs in“NonCritical,”“Best Effort,”and“Minimum Effort”scenarios,where the combination of LLM(Gemini)+SAST has obtained better results.
基金funded and supported by the Ongoing Research Funding program(ORF-2025-314),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.
文摘This paper evaluates the performance of Internet Protocol Security (IPSec) based Multiprotocol Label Switching (MPLS) virtual private network (VPN) in a small to medium sized organization. The demand for security in data networks has been increasing owing to the high cyber attacks and potential risks associated with networks spread over distant geographical locations. The MPLS networks ride on the public network backbone that is porous and highly susceptible to attacks and so the need for reliable security mechanisms to be part of the deployment plan. The evaluation criteria concentrated on Voice over Internet Protocol (VoIP) and Video conferencing with keen interest in jitter, end to end delivery and general data flow. This study used both structured questionnaire and observation methods. The structured questionnaire was administered to a group of 70 VPN users in a company. This provided the study with precise responses. The observation method was used in data simulations using OPNET Version 14.5 Simulation software. The results show that the IPSec features increase the size of data packets by approximately 9.98% translating into approximately 90.02% effectiveness. The tests showed that the performance metrics are all well within the recommended standards. The IPSec Based MPLS Virtual private network is more stable and secure than one without IPSec.
文摘数字化教学能力是教育数字化转型时期教师必备的关键能力,深入探究并分析教师数字化教学能力的影响因素,是教育管理者和教师应对技术挑战、提升教学质量、培养创新人才的关键环节。基于此,文章依托统一接受和使用技术(Unified Theory of Acceptance and Use of Technology,UTAUT)模型,采用偏最小二乘结构方程模型(Partial Least Squares Structural Equation Modeling,PLS-SEM)和模糊集定性比较分析(Fuzzy-Set Qualitative Comparative Analysis,fsQCA)方法,对教师数字化教学能力的影响因素及其组合效应进行了实证分析。其中,PLS-SEM分析结果表明,绩效期望、努力期望、社群影响、便利条件和自我效能感对教师数字化教学意愿有显著的正向影响,并进一步正向影响教师的数字化教学能力;教师的自我效能感对数字化教学能力有显著的直接影响,且影响效应最强。而fs QCA分析结果显示,存在四条激发教师数字化教学能力的路径,在这些路径中数字化教学意愿和自我效能感是两个重要的前因变量,这弥补了结构方程模型分析的相对不足。文章通过研究,旨在为教育数字化转型时期教师数字化教学能力的提升提供实证依据。
