JFinder:A novel architecture for java vulnerability identification based quad self-attention and pre-training mechanism

Software vulnerabilities pose significant risks to computer systems,impacting our daily lives,productivity,and even our health.Identifying and addressing security vulnerabilities in a timely manner is crucial to prevent hacking and data breaches.Unfortunately,current vulnerability identification methods,including classical and deep learning-based approaches,exhibit critical drawbacks that prevent them from meeting the demands of the contemporary software industry.To tackle these issues,we present JFinder,a novel architecture for Java vulnerability identification that leverages quad self-attention and pre-training mechanisms to combine structural information and semantic representations.Experimental results demonstrate that JFinder outperforms all baseline methods,achieving an accuracy of 0.97 on the CWE dataset and an F1 score of 0.84 on the PROMISE dataset.Furthermore,a case study reveals that JFinder can accurately identify four cases of vulnerabilities after patching.

Vulnerable Point Identification Using Heterogeneous Interdependent Node Theory for Distribution Systems

In order to reduce the occurrence or expansion of accidents and maintain safety in distribution networks,it is essential to find out the vulnerable points for the power system in time.In this paper,a vulnerable point identification method based on heterogeneous interdependent(HI)node theory and risk theory is proposed.Compared with the methods based on betweenness theory,the method based on HI nodes theory can deal with the shortcomings of the power flow shortest path,and consider the direct and indirect relationship of nodes.It is more suitable for identifying vulnerable points in a realistic power system.First,according to the analysis of heterogenous interdependent networks,the HI nodes are defined and used to evaluate the utility coupling value of each node.Then an identification indicator,which combines the utility coupling value and the risk indicators,is utilized to evaluate the vulnerability of each node.Results show that the proposed method is a suitable one to find the vulnerable points and better than betweennessbased methods for a distribution network.

Research Progress in the Intelligent Identification of Ecologically Vulnerable Areas and Its Prospects in the Mongolian Plateau

Identifying ecologically vulnerable areas is critical for constructing ecological barriers and precisely controlling ecological risks.With the rapid development of big data and Artificial Intelligence(AI)technologies,many intelligent methods have been developed to support the identification of vulnerable ecological areas.This paper reviews the methodological advancements in identifying ecologically vulnerable areas,including geographic zoning,expert integration,mathematical statistics,geographic information visualization,artificial neural networks,and unsupervised deep learning clustering methods.Additionally,we assessed several classic software tools used in ecology and natural resource management.Based on the review,several urgent research challenges for ecological function zoning research are proposed,such as the application of ecological vulnerability assessment intelligent algorithms,big data collaborative analysis,and the development of automated identification software.Considering the requirements in the Mongolian Plateau,this study proposes future development prospects of methods for identifying ecologically vulnerable area zoning,combined with the new AI research paradigm.They include enhancing the comprehensive analysis of multimodal data,increasing ecological barrier big data collaborative processing,advancing the interpretability of ecological function partitioning algorithms,developing automatic zoning software tools,and pushing the collaborative analysis of geographic big data and citizen science data.