LAUSR

A method for identifying software data flow vulnerabilities is proposed based on the dendritic cell algorithm and the improved convolutional neural network to effectively solve the transmission errors in software data flow. In this method, we first gave the software data flow propagation model and constructed the data propagation tree structure. Secondly, we analyzed the running characteristics of the software, took the interaction among indexes into account, and identified data flow vulnerabilities using the dendritic cell algorithm and the improved convolutional neural network. Finally, we conducted an in-depth study on the performance of this method and other algorithms through mathematical simulation. The results show that this method has better advantages in detection time, storage cost, and software code size.