An approach for predicting multiple-type overflow vulnerabilities based on combination features and a time series neural network algorithm-Reference-Cited by-同舟云学术

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An approach for predicting multiple-type overflow vulnerabilities based on combination features and a time series neural network algorithm

Published:2022-03 Issue: Volume:114 Page:102572
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Zheng Zhangqi,Zhang Bing,Liu Yongshan,Ren Jiadong,Zhao Xuyang,Wang Qian

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference35 articles.

1. Recognizing Algorithms Using Language Constructs, Software Metrics and Roles of Variables: An Experiment with Sorting Algorithms;Ahmad;Computer Journal,2011

2. Methodology for Validating Software Metrics;Schneidewind;IEEE Transactions on Software Engineering,1992

3. Empirical analysis of change metrics for software fault prediction;Choudhary;Computers and Electrical Engineering,2018

4. Vulnerability Scrying Method for Software Vulnerability Discovery Prediction Without a Vulnerability Database;Rahimi;IEEE Transactions on Reliability,2013

5. Vulnerability Finding Using Symbolic Execution on Binary Programs;Niu;Computer Science,2013

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1. VDoTR: Vulnerability detection based on tensor representation of comprehensive code graphs;Computers & Security;2023-07

2. Cross-domain vulnerability detection using graph embedding and domain adaptation;Computers & Security;2023-02

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