A Vulnerability Detection Model for Java Systems Based on Complex Networks

Xiong, Ling; Shan, Chun; Hu, Changzhen; Yun, Zijun; Xiong, Wenjie

doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00246

Cited by 1 publication

(1 citation statement)

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“…Myers et al [16] found that most of the topological structures of software systems have the relevant characteristics of complex networks such as "small world" and "scale-free." Literature [17] regards software system as a complex network graph composed of algebraic components and connection relations between them.…”

Section: Introductionmentioning

confidence: 99%

A Software Vulnerability Detection Method Based on Complex Network Community

Shan

Gong

Xiong

et al. 2022

Security and Communication Networks

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To find out whether there is any vulnerability in software programs where conditional judgment is ignored, this article proposes a software vulnerability detection method based on complex network community. First, the method abstracts the software system into a directed weighted graph by using the software algebraic component model and then preprocesses the directed weighted graph to get a complex network graph. Then, by using the partition algorithm, the complex network graph is divided into the communities, and the key nodes in communities are found by nRank algorithm. Finally, the graph of the key nodes with high influence is matched with the complex network graph that has been preprocessed. In order to evaluate the effectiveness of the community partition algorithm and the nRank algorithm, comparative experiments are carried out on two datasets. The experimental results show that the community partition algorithm is better than the comparison algorithm in precision, recall, and comprehensive evaluation index, and the nRank algorithm is closer to the result of degree centrality measurement index than the PageRank algorithm and the LeaderRank algorithm. The spring-shiro-training project is used to verify the vulnerability detection method based on complex network community, and the results show that the method is effective.

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Section: Introductionmentioning

confidence: 99%