在Windows Server 2003(以下简称Win2003)操作系统推出两年后，在众多用户的期盼之下。微软终于发布了它的第一个中文版补丁包Windows Server 2003 Service Pack 1(以下简称Win2003 SP1)。Win2003 SP1汇集了Win2003系统推出以来最完整...在Windows Server 2003(以下简称Win2003)操作系统推出两年后，在众多用户的期盼之下。微软终于发布了它的第一个中文版补丁包Windows Server 2003 Service Pack 1(以下简称Win2003 SP1)。Win2003 SP1汇集了Win2003系统推出以来最完整的安全更新，并根据这些安全更新降低了可能的攻击面，提升系统的防御能力。同时也提供了更健全的默认值与使用者的权限控管，使系统服务受到更好的保护。本文就是应用Win2003 SP1的安全配置向导来配置安全策略，增强Win2003服务器的安全性。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
This study unfolds an innovative approach aiming to address the critical role of building design in global energy consumption, focusing on optimizing the Window-to-Wall Ratio (WWR), since buildings account for approxi...This study unfolds an innovative approach aiming to address the critical role of building design in global energy consumption, focusing on optimizing the Window-to-Wall Ratio (WWR), since buildings account for approximately 30% of total energy consumed worldwide. The greatest contributors to energy expenditure in buildings are internal artificial lighting and heating and cooling systems. The WWR, determined by the proportion of the building’s glazed area to its wall area, is a significant factor influencing energy efficiency and minimizing energy load. This study introduces the development of a semi-automated computer model designed to offer a real-time, interactive simulation environment, fostering improving communication and engagement between designers and owners. The said model serves to optimize both the WWR and building orientation to align with occupants’ needs and expectations, subsequently reducing annual energy consumption and enhancing the overall building energy performance. The integrated model incorporates Building Information Modeling (BIM), Virtual Reality (VR), and Energy Analysis tools deployed at the conceptual design stage, allowing for the amalgamation of owners’ inputs in the design process and facilitating the creation of more realistic and effective design strategies.展开更多
这款外观颇似Tablet PC的电脑可谓是一个“异数”，其采用的硬件配置和略微精简的Windows XP embedded操作系统使它看起来更像是一台传统的Tablet PC。然而．与PDA类似．M1400未配置硬盘．它的操作系统固化在512MB的ROM中．这意味着它...这款外观颇似Tablet PC的电脑可谓是一个“异数”，其采用的硬件配置和略微精简的Windows XP embedded操作系统使它看起来更像是一台传统的Tablet PC。然而．与PDA类似．M1400未配置硬盘．它的操作系统固化在512MB的ROM中．这意味着它没有传统的开关机等待时间．而且与系统相关的操作更是快捷无比．其与PC平台软件共通性的特点更是PDA所无法与之匹敌。我们期待已久的固化桌面操作系统终于在M1400身上成为了现实。展开更多
文摘在Windows Server 2003(以下简称Win2003)操作系统推出两年后，在众多用户的期盼之下。微软终于发布了它的第一个中文版补丁包Windows Server 2003 Service Pack 1(以下简称Win2003 SP1)。Win2003 SP1汇集了Win2003系统推出以来最完整的安全更新，并根据这些安全更新降低了可能的攻击面，提升系统的防御能力。同时也提供了更健全的默认值与使用者的权限控管，使系统服务受到更好的保护。本文就是应用Win2003 SP1的安全配置向导来配置安全策略，增强Win2003服务器的安全性。
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This study unfolds an innovative approach aiming to address the critical role of building design in global energy consumption, focusing on optimizing the Window-to-Wall Ratio (WWR), since buildings account for approximately 30% of total energy consumed worldwide. The greatest contributors to energy expenditure in buildings are internal artificial lighting and heating and cooling systems. The WWR, determined by the proportion of the building’s glazed area to its wall area, is a significant factor influencing energy efficiency and minimizing energy load. This study introduces the development of a semi-automated computer model designed to offer a real-time, interactive simulation environment, fostering improving communication and engagement between designers and owners. The said model serves to optimize both the WWR and building orientation to align with occupants’ needs and expectations, subsequently reducing annual energy consumption and enhancing the overall building energy performance. The integrated model incorporates Building Information Modeling (BIM), Virtual Reality (VR), and Energy Analysis tools deployed at the conceptual design stage, allowing for the amalgamation of owners’ inputs in the design process and facilitating the creation of more realistic and effective design strategies.
文摘这款外观颇似Tablet PC的电脑可谓是一个“异数”，其采用的硬件配置和略微精简的Windows XP embedded操作系统使它看起来更像是一台传统的Tablet PC。然而．与PDA类似．M1400未配置硬盘．它的操作系统固化在512MB的ROM中．这意味着它没有传统的开关机等待时间．而且与系统相关的操作更是快捷无比．其与PC平台软件共通性的特点更是PDA所无法与之匹敌。我们期待已久的固化桌面操作系统终于在M1400身上成为了现实。
