Windows 32/64位代码注入攻击是恶意软件常用的攻击技术,在内存取证领域,现存的代码注入攻击检测技术在验证完整性方面不能处理动态内容,并且在解析内存中数据结构方面无法兼容不同版本的Windows系统。因此提出了通过交叉验证进程堆栈和...Windows 32/64位代码注入攻击是恶意软件常用的攻击技术,在内存取证领域,现存的代码注入攻击检测技术在验证完整性方面不能处理动态内容,并且在解析内存中数据结构方面无法兼容不同版本的Windows系统。因此提出了通过交叉验证进程堆栈和VAD信息定位注入代码方法,将基于遍历栈帧得到的函数返回地址、模块名等信息结合进程VAD结构来检测函数返回地址、匹配文件名以定位注入代码,并且研发了基于Volatility取证框架的Windows代码注入攻击检测插件codefind。测试结果表明,即使在VAD节点被恶意软件修改,方法仍能够有效定位Windows 32/64位注入代码攻击。展开更多
Energy density,the Achilles’heel of aqueous supercapacitors,is simultaneously determined by the voltage window and specific capacitance of the carbon materials,but the strategy of synchronously boosting them has rare...Energy density,the Achilles’heel of aqueous supercapacitors,is simultaneously determined by the voltage window and specific capacitance of the carbon materials,but the strategy of synchronously boosting them has rarely been reported.Herein,we demonstrate that the rational utilization of the interaction between redox mediators(RMs)and carbon electrode materials,especially those with rich intrinsic defects,contributes to extended potential windows and more stored charges concurrently.Using 4-hydroxy-2,2,6,6-tetramethylpiperidinyloxyl(4OH-TEMPO)and intrinsic defect-rich carbons as the RMs and electrode materials,respectively,the potential window and capacitance are increased by 67%and sixfold in a neutral electrolyte.Moreover,this strategy could also be applied to alkaline and acid electrolytes.The first-principle calculation and experimental results demonstrate that the strong interaction between 4OH-TEMPO and defectrich carbons plays a key role as preferential adsorbed RMs may largely prohibit the contact of free water molecules with the electrode materials to terminate the water splitting at elevated potentials.For the RMs offering weaker interaction with the electrode materials,the water splitting still proceeds with a thus sole increase of the stored charges.The results discovered in this work could provide an alternative solution to address the low energy density of aqueous supercapacitors.展开更多
To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,...To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.展开更多
Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malwar...Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.展开更多
随着应用对象的扩大和微电子技术、软件技术的发展,嵌入式系统逐渐从单片机发展到高性能嵌入式微处理器和嵌入式操作系统。本文详细分析Windows CE 3.0的实时性、通用性、模块化、Win32兼容等性能特点。根据工业控制系统对系统平台的一...随着应用对象的扩大和微电子技术、软件技术的发展,嵌入式系统逐渐从单片机发展到高性能嵌入式微处理器和嵌入式操作系统。本文详细分析Windows CE 3.0的实时性、通用性、模块化、Win32兼容等性能特点。根据工业控制系统对系统平台的一般要求,如实时性、可靠性、图形界面、开发环境和成本等,分折Windows CE在工业控制领域的优缺点,并指出Windows CE在工业控制中有很好的应用前景。展开更多
Perovskite(PRV)luminescent solar concentrators(LSCs)use PRV materials to concentrate and convert sunlight into electricity.LSCs are made up of a flat plate or sheet of glass or plastic that contains a layer of lumines...Perovskite(PRV)luminescent solar concentrators(LSCs)use PRV materials to concentrate and convert sunlight into electricity.LSCs are made up of a flat plate or sheet of glass or plastic that contains a layer of luminescent PRV material.When sunlight enters the LSC,the PRV material absorbs the light and emits it at a longer wavelength.This emitted light is then trapped inside the LSC by total internal reflection,and it travels to the edges of the plate where it is collected by photovoltaic(PV)solar cells(SCs).The use of PRV materials in LSCs offers several advantages over other materials.PRV materials are highly efficient at converting light into electricity.They are also flexible,low-cost,and easy to manufacture,making them a promising candidate for large-scale solar energy applications.However,PRV materials have some challenges preventing their adoption.They are sensitive to moisture or heat and can degrade quickly over time.This significantly limits their lifespan and stability.Research on PRV is mostly focused on making them more stable and durable,but finding ways to improve the manufacturing process to reduce costs and increase efficiency is also relevant.While the opportunities offered by PRV materials for the specific application to LCSs are certainly interesting,the challenges make the prospect of a commercial product very unlikely in the short term.展开更多
基金financially supported by the National Natural Science Foundation of China(22179145,22138013,and 21975287)Shandong Provincial Natural Science Foundation(ZR2020ZD08)+1 种基金Taishan Scholar Project(no.ts201712020)the startup support grant from China University of Petroleum(East China)
文摘Energy density,the Achilles’heel of aqueous supercapacitors,is simultaneously determined by the voltage window and specific capacitance of the carbon materials,but the strategy of synchronously boosting them has rarely been reported.Herein,we demonstrate that the rational utilization of the interaction between redox mediators(RMs)and carbon electrode materials,especially those with rich intrinsic defects,contributes to extended potential windows and more stored charges concurrently.Using 4-hydroxy-2,2,6,6-tetramethylpiperidinyloxyl(4OH-TEMPO)and intrinsic defect-rich carbons as the RMs and electrode materials,respectively,the potential window and capacitance are increased by 67%and sixfold in a neutral electrolyte.Moreover,this strategy could also be applied to alkaline and acid electrolytes.The first-principle calculation and experimental results demonstrate that the strong interaction between 4OH-TEMPO and defectrich carbons plays a key role as preferential adsorbed RMs may largely prohibit the contact of free water molecules with the electrode materials to terminate the water splitting at elevated potentials.For the RMs offering weaker interaction with the electrode materials,the water splitting still proceeds with a thus sole increase of the stored charges.The results discovered in this work could provide an alternative solution to address the low energy density of aqueous supercapacitors.
基金supported by Natural Science Foundation Project of Gansu Provincial Science and Technology Department(No.1506RJZA084)Gansu Provincial Education Department Scientific Research Fund Grant Project(No.1204-13).
文摘To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.
基金This researchwork is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R411),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.
文摘随着应用对象的扩大和微电子技术、软件技术的发展,嵌入式系统逐渐从单片机发展到高性能嵌入式微处理器和嵌入式操作系统。本文详细分析Windows CE 3.0的实时性、通用性、模块化、Win32兼容等性能特点。根据工业控制系统对系统平台的一般要求,如实时性、可靠性、图形界面、开发环境和成本等,分折Windows CE在工业控制领域的优缺点,并指出Windows CE在工业控制中有很好的应用前景。
文摘Perovskite(PRV)luminescent solar concentrators(LSCs)use PRV materials to concentrate and convert sunlight into electricity.LSCs are made up of a flat plate or sheet of glass or plastic that contains a layer of luminescent PRV material.When sunlight enters the LSC,the PRV material absorbs the light and emits it at a longer wavelength.This emitted light is then trapped inside the LSC by total internal reflection,and it travels to the edges of the plate where it is collected by photovoltaic(PV)solar cells(SCs).The use of PRV materials in LSCs offers several advantages over other materials.PRV materials are highly efficient at converting light into electricity.They are also flexible,low-cost,and easy to manufacture,making them a promising candidate for large-scale solar energy applications.However,PRV materials have some challenges preventing their adoption.They are sensitive to moisture or heat and can degrade quickly over time.This significantly limits their lifespan and stability.Research on PRV is mostly focused on making them more stable and durable,but finding ways to improve the manufacturing process to reduce costs and increase efficiency is also relevant.While the opportunities offered by PRV materials for the specific application to LCSs are certainly interesting,the challenges make the prospect of a commercial product very unlikely in the short term.
