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.

A Software Vulnerability Detection Method Based on Complex Network Community

With the deepening of research on network theory, scientists have gradually begun to adopt complex networks to look at complex systems in nature. A complex network is a network of a large number of closely related and interacting units. This paper believes that each component of the software can be abstracted into various algebraic components. These algebraic components are assembled into a software system. Its assembly process satisfies the configuration and fit characteristics in the topological space. Using algebraic components as the vertices of complex network graphs, the connection relationship between them is taken as the edge of the complex network graph. Using this new perspective, the software system is regarded as a complex network graph composed of algebraic components and their connection relationships. We have proved that the software system can be abstracted into the form of algebraic component expressions, and can find algebraic components that may have vulnerabilities based on the eigenvalues of complex networks.

A Vulnerability Detection Model for Java Systems Based on Complex Networks

This paper studies an effective unsupervised deep learning model for multivariate time series anomaly detection. Since multivariate time series usually have problems of insufficient labeling and highly-complex temporal correlation, effectively detecting anomalies in multivariate time series data is particularly challenging. To solve this problem, we propose a model named Wasserstein-GAN with gradient Penalty and effective Scoring (WPS). In this model, Wasserstein Distance with Gradient Penalty helps to capture the data regularities between generator output and real data, thus improving the training stability. Meanwhile, an effective scoring function that consists of reconstruction error, discrimination error, and prediction error is designed to evaluate the accuracy of the abnormal prediction and recall. The experimental results show that compared with the suboptimal baseline model, our proposed WPS obtains 17.68% and 10.41% improvement in prediction precision and F1 score, respectively. 

Ling Xiong

Papers

A Vulnerability Detection Model for Java Systems Based on Complex Networks

An Effective WGAN-Based Anomaly Detection Model for IoT Multivariate Time Series

A Software Vulnerability Detection Method Based on Complex Network Community