在计算机网络实验室的实践教学中,常常遇到难以满足安装操作系统、组网、测试网络软件等需求的难题。介绍了VMWare的工作原理,分析它的优势和特性,提出基于VMWare建立虚拟网络实验室的思路。通过VMWare创建多个虚拟子系统,在其中安装Li...在计算机网络实验室的实践教学中,常常遇到难以满足安装操作系统、组网、测试网络软件等需求的难题。介绍了VMWare的工作原理,分析它的优势和特性,提出基于VMWare建立虚拟网络实验室的思路。通过VMWare创建多个虚拟子系统,在其中安装Linux、Windows Server 2003等不同种类的操作系统。这些虚拟子系统通过多种方便、灵活的方式进行通讯,形成复杂和多变的测试环境,可以完成各种复杂的网络实验。VMWare在实践中使用方便、性能强大,能很好地满足计算机网络实验室的要求。展开更多
The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are ins...The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.展开更多
Phage-microbe interactions are appealing systems to study coevolution,and have also been increasingly emphasized due to their roles in human health,disease,and the development of novel therapeutics.Phage-microbe inter...Phage-microbe interactions are appealing systems to study coevolution,and have also been increasingly emphasized due to their roles in human health,disease,and the development of novel therapeutics.Phage-microbe interactions leave diverse signals in bacterial and phage genomic sequences,defined as phage-host interaction signals(PHISs),which include clustered regularly interspaced short palindromic repeats(CRISPR)targeting,prophage,and protein-protein interaction signals.In the present study,we developed a novel tool phage-host interaction signal detector(PHISDetector)to predict phage-host interactions by detecting and integrating diverse in silico PHISs,and scoring the probability of phage-host interactions using machine learning models based on PHIS features.We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases.When tested on a dataset of 758 annotated phage-host pairs,PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels,respectively,outperforming other phage-host prediction tools.When applied to 125,842 metagenomic viral contigs(mVCs)derived from 3042 geographically diverse samples,a detection rate of 54.54% could be achieved.Furthermore,PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant(MDR)bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health(NIH)Human Microbiome Project(HMP).The PHISDetector can be run either as a web server(http://www.microbiome-bigdata.com/PHISDetector/)for general users to study individual inputs or as a stand-alone version(https://github.com/HITImmunologyLab/PHISDetector)to process massive phage contigs from virome studies.展开更多
文摘在计算机网络实验室的实践教学中,常常遇到难以满足安装操作系统、组网、测试网络软件等需求的难题。介绍了VMWare的工作原理,分析它的优势和特性,提出基于VMWare建立虚拟网络实验室的思路。通过VMWare创建多个虚拟子系统,在其中安装Linux、Windows Server 2003等不同种类的操作系统。这些虚拟子系统通过多种方便、灵活的方式进行通讯,形成复杂和多变的测试环境,可以完成各种复杂的网络实验。VMWare在实践中使用方便、性能强大,能很好地满足计算机网络实验室的要求。
文摘The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.
基金supported by the National Natural Science Foundation of China(Grant Nos.31825008,31422014,and 61872117).
文摘Phage-microbe interactions are appealing systems to study coevolution,and have also been increasingly emphasized due to their roles in human health,disease,and the development of novel therapeutics.Phage-microbe interactions leave diverse signals in bacterial and phage genomic sequences,defined as phage-host interaction signals(PHISs),which include clustered regularly interspaced short palindromic repeats(CRISPR)targeting,prophage,and protein-protein interaction signals.In the present study,we developed a novel tool phage-host interaction signal detector(PHISDetector)to predict phage-host interactions by detecting and integrating diverse in silico PHISs,and scoring the probability of phage-host interactions using machine learning models based on PHIS features.We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases.When tested on a dataset of 758 annotated phage-host pairs,PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels,respectively,outperforming other phage-host prediction tools.When applied to 125,842 metagenomic viral contigs(mVCs)derived from 3042 geographically diverse samples,a detection rate of 54.54% could be achieved.Furthermore,PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant(MDR)bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health(NIH)Human Microbiome Project(HMP).The PHISDetector can be run either as a web server(http://www.microbiome-bigdata.com/PHISDetector/)for general users to study individual inputs or as a stand-alone version(https://github.com/HITImmunologyLab/PHISDetector)to process massive phage contigs from virome studies.
