#Open source risk engine setup software#

The authors did not have special access privileges.ДЇunding: This research was funded by the State Grid Corporation Headquarters Science and Technology Project 'Research and Application of Key Technologies for Open Source Software Security Monitoring' (SGFJXT00YJJS1800074).Д¬ompeting interests: The authors have declared that no competing interests exist. Data are freely available from the following link. The data set used in the paper is from the official website of the National Institute of Standards and Technology (NIST): National Institute of Standards and Technology: Software Assurance Reference Dataset Project. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Дata Availability: The data used to support the findings of this study are included within the article. Received: ApAccepted: AugPublished: August 23, 2019Д¬opyright: В© 2019 Li et al. PLoS ONE 14(8):Д®ditor: Hua Wang, Victoria University, AUSTRALIA The experimental results verify the effectiveness of the proposed open source software security vulnerability detection method based on dynamic behavior features.Д¬itation: Li Y, Ma L, Shen L, Lv J, Zhang P (2019) Open source software security vulnerability detection based on dynamic behavior features. In addition, the CNN-IndRNN classification model is constructed by improving the Independently Recurrent Neural Net-work (IndRNN) algorithm and applies to open source software security vulnerability detection. Then, the behavioral risk vulnerability database of open source software is proposed as a support for vulnerability detection. Firstly, the relationship between open source software vulnerability and API call sequence is studied. In order to solve the problems, based on the further study of behavior feature extraction and vulnerability detection technology, a method of using dynamic behavior features to detect open source software vulnerabilities is proposed.

The current mainstream open source software vulnerability analysis technology is based on source code, and there are problems such as false positives, false negatives and restatements. Therefore, security analysis is required before using open source software. Open source software has been widely used in various industries due to its openness and flexibility, but it also brings potential security problems.

Open source risk engine setup