A New Framework for Software Vulnerability Detection Based on an Advanced Computing

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.

Eco-environmental vulnerability assessment for large drinking water resource： a case study of Qiandao Lake Area, China

The Qiandao Lake Area （QLA） is of great significance in terms of drinking water supply in East Coast China as well as a nationally renowned tourist attraction. A series of laws and regulations regarding the QLA environment have been enacted and implemented throughout the past decade with the aim of negating the harmful effects associated with expanding urbanization and industrialization. In this research, an assessment framework was developed to analyze the eco-environ- mental vulnerability of the QLA from 1990-2010 by integrating fuzzy analytic hierarchy process （FAHP） and geographical information systems （GIS） in an attempt to gain insights into the status quo of the QLA so as to review and evaluate the effectiveness of the related policies. After processing and analyzing the temporal and spatial variation of eco-environmental vulnerability and major environ- mental issues in the QLA, we found that the state of eco- environmental vulnerability of the QLA was acceptable, though a moderate deterioration was detected during the study period. Furthermore, analysis of the combination of vulnerability and water quality indicated that the water quality showed signs of declination, though the overall status remained satisfactory. It was hence concluded that the collective protection and treatment actions were effective over the study period, whereas immediately stricter measures would be required for protecting the drinking water quality from domestic sewage and industrial wastewater. Finally, the spatial variation of the eco-environmental vulnerability assessment also implied that specifically more targeted measures should be adoptedin respective regions for long-term sustainable develop- ment of the QLA.