The debate on global food security has regained vigor since the food crisis of 2008, when a sudden spike in the prices of staple food commodities dramatically demonstrated that securing the supply and accessibility of...The debate on global food security has regained vigor since the food crisis of 2008, when a sudden spike in the prices of staple food commodities dramatically demonstrated that securing the supply and accessibility of food for a world of nine billion people in 2050 cannot be taken for grant- ed （Godfray etal. 2010; Swinnen and Squicciarini 2012;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
As the number of Virtual Machines(VMs) consolidated on single physical server increases with the rapid advance of server hardware,virtual network turns complex and frangible.Modern Network Security Engines(NSE) are in...As the number of Virtual Machines(VMs) consolidated on single physical server increases with the rapid advance of server hardware,virtual network turns complex and frangible.Modern Network Security Engines(NSE) are introduced to eradicate the intrusions occurring in the virtual network.In this paper,we point out the inadequacy of the present live migration implementation,which hinders itself from providing transparent VM relocation between hypervisors equipped with Network Security Engines(NSE-H).This occurs because the current implementation ignores VM-related Security Context(SC) required by NSEs embedded in NSE-H.We present the CoM,a comprehensive live migration framework,for NSE-H-based virtualization computing environment.We built a prototype system on Xen hypervisors to evaluate our framework,and conduct experiments under various realistic application environments.The results demonstrate that our solution successfully fixes the inadequacy of the present live migration implementation,and the performance overhead is negligible.展开更多
A multi-homed VPN architecture based on extended SOCKSv5 and TLS was proposed. The architecture employs a dynamic connection mechanism for multiple proxies in the end system,i n which the security-demanded transmissio...A multi-homed VPN architecture based on extended SOCKSv5 and TLS was proposed. The architecture employs a dynamic connection mechanism for multiple proxies in the end system,i n which the security-demanded transmission connections can switch smoothly among the multiple proxies by maint aining a coherent connection context.The mechanism is transparent to application programs and can support th e building of VPN.With the cooperation of some other security components,the mechanism guarantees the reso urce availability and reliability of the end system against some attacks to the specific ports or hosts.展开更多
基金financial support from the National Nonprofit Institute Research Grant of Chinese Academy of Agricultural Sciences (CAAS, IARRP-2015-28)the logistical support from the CAAS-UGent Joint Labooratory of Global Change and Food Security
文摘The debate on global food security has regained vigor since the food crisis of 2008, when a sudden spike in the prices of staple food commodities dramatically demonstrated that securing the supply and accessibility of food for a world of nine billion people in 2050 cannot be taken for grant- ed （Godfray etal. 2010; Swinnen and Squicciarini 2012;
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.
基金supported by State Key Laboratory of Software Development Environment under Grant No. SKLSDE-2009ZX-02China Aviation Science Fund under Grant No.20081951National High Technical Research and Development Program of China (863 Program) under Grant No.2007AA01Z183
文摘As the number of Virtual Machines(VMs) consolidated on single physical server increases with the rapid advance of server hardware,virtual network turns complex and frangible.Modern Network Security Engines(NSE) are introduced to eradicate the intrusions occurring in the virtual network.In this paper,we point out the inadequacy of the present live migration implementation,which hinders itself from providing transparent VM relocation between hypervisors equipped with Network Security Engines(NSE-H).This occurs because the current implementation ignores VM-related Security Context(SC) required by NSEs embedded in NSE-H.We present the CoM,a comprehensive live migration framework,for NSE-H-based virtualization computing environment.We built a prototype system on Xen hypervisors to evaluate our framework,and conduct experiments under various realistic application environments.The results demonstrate that our solution successfully fixes the inadequacy of the present live migration implementation,and the performance overhead is negligible.
基金National Natural Science Foundation ofChina (No.90104029)
文摘A multi-homed VPN architecture based on extended SOCKSv5 and TLS was proposed. The architecture employs a dynamic connection mechanism for multiple proxies in the end system,i n which the security-demanded transmission connections can switch smoothly among the multiple proxies by maint aining a coherent connection context.The mechanism is transparent to application programs and can support th e building of VPN.With the cooperation of some other security components,the mechanism guarantees the reso urce availability and reliability of the end system against some attacks to the specific ports or hosts.
