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
CO_(2)electrochemical reduction reaction(CO_(2)RR)to formate is a hopeful pathway for reducing CO_(2)and producing high-value chemicals,which needs highly selective catalysts with ultra-broad potential windows to meet...CO_(2)electrochemical reduction reaction(CO_(2)RR)to formate is a hopeful pathway for reducing CO_(2)and producing high-value chemicals,which needs highly selective catalysts with ultra-broad potential windows to meet the industrial demands.Herein,the nanorod-like bimetallic ln_(2)O_(3)/Bi_(2)O_(3)catalysts were successfully synthesized by pyrolysis of bimetallic InBi-MOF precursors.The abundant oxygen vacancies generated from the lattice mismatch of Bi_(2)O_(3)and ln_(2)O_(3)reduced the activation energy of CO_(2)to*CO_(2)·^(-)and improved the selectivity of*CO_(2)·^(-)to formate simultaneously.Meanwhile,the carbon skeleton derived from the pyrolysis of organic framework of InBi-MOF provided a conductive network to accelerate the electrons transmission.The catalyst exhibited an ultra-broad applied potential window of 1200 mV(from-0.4 to-1.6 V vs RHE),relativistic high Faradaic efficiency of formate(99.92%)and satisfactory stability after 30 h.The in situ FT-IR experiment and DFT calculation verified that the abundant oxygen vacancies on the surface of catalysts can easily absorb CO_(2)molecules,and oxygen vacancy path is dominant pathway.This work provides a convenient method to construct high-performance bimetallic catalysts for the industrial application of CO_(2)RR.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
作为Windows2000 Server和Windows Server 2003系列产品的标志,活动目录在微软公司服务器产品中扮演了极为重要的角色。可以说。活动目录的出现从根本上改变了微软的服务器产品。活动目录技术使所有的计算机硬件和信息资源都能够得到...作为Windows2000 Server和Windows Server 2003系列产品的标志,活动目录在微软公司服务器产品中扮演了极为重要的角色。可以说。活动目录的出现从根本上改变了微软的服务器产品。活动目录技术使所有的计算机硬件和信息资源都能够得到集中的管理。提高了服务器的可靠性、安全性、可扩展性和可管理性。作为Windows 2000和Windows 2003系列服务器产品的重要组成部分,新的组策略管理在Windows服务器产品的革新中也扮演了极为重要的角色。基于活动目录技术。组策略可以完成软件部署、安全管理、系统管理、网络管理,以及其它很多任务。组策略技术的使用大大增强了基于活动目录技术的计算机网络的可管理性,节省了网络管理员的宝贵时间,提高了工作效率。本文将从不同角度描述组策略的历史、架构、管理、实施、故障排除等具体细节,希望能为更多的网络管理员更好地理解和运用组策略提供一些帮助。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
Windows Vista(代号Longhorn)——微软还没面世的操作系统,最近可以说风头正盛,除了出色的3D界面吸引人以外,极高的配置需求也成为人们议论的焦点。Vista预计于2006年圣诞旺季或者2007年初发售,beta1测试版已经于7月底发布,我们下面安...Windows Vista(代号Longhorn)——微软还没面世的操作系统,最近可以说风头正盛,除了出色的3D界面吸引人以外,极高的配置需求也成为人们议论的焦点。Vista预计于2006年圣诞旺季或者2007年初发售,beta1测试版已经于7月底发布,我们下面安装的也正是这一版本。展开更多
基金financially supported by the National Natural Science Foundation of China(52072409)the Major Scientific and Technological Innovation Project of Shandong Province(2020CXGC010403)+1 种基金the Taishan Scholar Project(No.ts201712020)the Natural Science Foundation of Shandong Province(ZR2021QE062)
文摘CO_(2)electrochemical reduction reaction(CO_(2)RR)to formate is a hopeful pathway for reducing CO_(2)and producing high-value chemicals,which needs highly selective catalysts with ultra-broad potential windows to meet the industrial demands.Herein,the nanorod-like bimetallic ln_(2)O_(3)/Bi_(2)O_(3)catalysts were successfully synthesized by pyrolysis of bimetallic InBi-MOF precursors.The abundant oxygen vacancies generated from the lattice mismatch of Bi_(2)O_(3)and ln_(2)O_(3)reduced the activation energy of CO_(2)to*CO_(2)·^(-)and improved the selectivity of*CO_(2)·^(-)to formate simultaneously.Meanwhile,the carbon skeleton derived from the pyrolysis of organic framework of InBi-MOF provided a conductive network to accelerate the electrons transmission.The catalyst exhibited an ultra-broad applied potential window of 1200 mV(from-0.4 to-1.6 V vs RHE),relativistic high Faradaic efficiency of formate(99.92%)and satisfactory stability after 30 h.The in situ FT-IR experiment and DFT calculation verified that the abundant oxygen vacancies on the surface of catalysts can easily absorb CO_(2)molecules,and oxygen vacancy path is dominant pathway.This work provides a convenient method to construct high-performance bimetallic catalysts for the industrial application of CO_(2)RR.
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.