The boom of coding languages in the 1950s revolutionized how our digital world was construed and accessed. The languages invented then, including Fortran, are still in use today due to their versatility and ability to...The boom of coding languages in the 1950s revolutionized how our digital world was construed and accessed. The languages invented then, including Fortran, are still in use today due to their versatility and ability to underpin a large majority of the older portions of our digital world and applications. Fortran, or Formula Translation, was a programming language implemented by IBM that shortened the apparatus of coding and the efficacy of the language syntax. Fortran marked the beginning of a new era of efficient programming by reducing the number of statements needed to operate a machine several-fold. Since then, dozens more languages have come into regular practice and have been increasingly diversified over the years. Some modern languages include Python, Java, JavaScript, C, C++, and PHP. These languages significantly improved efficiency and also have a broad range of uses. Python is mainly used for website/software development, data analysis, task automation, image processing, and graphic design applications. On the other hand, Java is primarily used as a client-side programming language. Expanding the coding languages allowed for increasing accessibility but also opened up applications to pertinent security issues. These security issues have varied by prevalence and language. Previous research has narrowed its focus on individual languages, failing to evaluate the security. This research paper investigates the severity and frequency of coding vulnerabilities comparatively across different languages and contextualizes their uses in a systematic literature review.展开更多
The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses ...The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.展开更多
Open Ground Storey（OGS） framed buildings where the ground storey is kept open without infill walls, mainly to facilitate parking, is increasing commonly in urban areas. However, vulnerability of this type of buildin...Open Ground Storey（OGS） framed buildings where the ground storey is kept open without infill walls, mainly to facilitate parking, is increasing commonly in urban areas. However, vulnerability of this type of buildings has been exposed in past earthquakes. OGS buildings are conventionally designed by a bare frame analysis that ignores the stiffness of the infill walls present in the upper storeys, but doing so underestimates the inter-storey drift（ISD） and thereby the force demand in the ground storey columns. Therefore, a multiplication factor（MF） is introduced in various international codes to estimate the design forces（bending moments and shear forces） in the ground storey columns. This study focuses on the seismic performance of typical OGS buildings designed by means of MFs. The probabilistic seismic demand models, fragility curves, reliability and cost indices for various frame models including bare frames and fully infilled frames are developed. It is found that the MF scheme suggested by the Israel code is better than other international codes in terms of reliability and cost.展开更多
文摘The boom of coding languages in the 1950s revolutionized how our digital world was construed and accessed. The languages invented then, including Fortran, are still in use today due to their versatility and ability to underpin a large majority of the older portions of our digital world and applications. Fortran, or Formula Translation, was a programming language implemented by IBM that shortened the apparatus of coding and the efficacy of the language syntax. Fortran marked the beginning of a new era of efficient programming by reducing the number of statements needed to operate a machine several-fold. Since then, dozens more languages have come into regular practice and have been increasingly diversified over the years. Some modern languages include Python, Java, JavaScript, C, C++, and PHP. These languages significantly improved efficiency and also have a broad range of uses. Python is mainly used for website/software development, data analysis, task automation, image processing, and graphic design applications. On the other hand, Java is primarily used as a client-side programming language. Expanding the coding languages allowed for increasing accessibility but also opened up applications to pertinent security issues. These security issues have varied by prevalence and language. Previous research has narrowed its focus on individual languages, failing to evaluate the security. This research paper investigates the severity and frequency of coding vulnerabilities comparatively across different languages and contextualizes their uses in a systematic literature review.
文摘The detection of software vulnerabilities written in C and C++languages takes a lot of attention and interest today.This paper proposes a new framework called DrCSE to improve software vulnerability detection.It uses an intelligent computation technique based on the combination of two methods:Rebalancing data and representation learning to analyze and evaluate the code property graph(CPG)of the source code for detecting abnormal behavior of software vulnerabilities.To do that,DrCSE performs a combination of 3 main processing techniques:(i)building the source code feature profiles,(ii)rebalancing data,and(iii)contrastive learning.In which,the method(i)extracts the source code’s features based on the vertices and edges of the CPG.The method of rebalancing data has the function of supporting the training process by balancing the experimental dataset.Finally,contrastive learning techniques learn the important features of the source code by finding and pulling similar ones together while pushing the outliers away.The experiment part of this paper demonstrates the superiority of the DrCSE Framework for detecting source code security vulnerabilities using the Verum dataset.As a result,the method proposed in the article has brought a pretty good performance in all metrics,especially the Precision and Recall scores of 39.35%and 69.07%,respectively,proving the efficiency of the DrCSE Framework.It performs better than other approaches,with a 5%boost in Precision and a 5%boost in Recall.Overall,this is considered the best research result for the software vulnerability detection problem using the Verum dataset according to our survey to date.
文摘Open Ground Storey（OGS） framed buildings where the ground storey is kept open without infill walls, mainly to facilitate parking, is increasing commonly in urban areas. However, vulnerability of this type of buildings has been exposed in past earthquakes. OGS buildings are conventionally designed by a bare frame analysis that ignores the stiffness of the infill walls present in the upper storeys, but doing so underestimates the inter-storey drift（ISD） and thereby the force demand in the ground storey columns. Therefore, a multiplication factor（MF） is introduced in various international codes to estimate the design forces（bending moments and shear forces） in the ground storey columns. This study focuses on the seismic performance of typical OGS buildings designed by means of MFs. The probabilistic seismic demand models, fragility curves, reliability and cost indices for various frame models including bare frames and fully infilled frames are developed. It is found that the MF scheme suggested by the Israel code is better than other international codes in terms of reliability and cost.
