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Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable.
Individual comparisons by ranking methods
Frank Wilcoxon · 1945
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Comparison of the predicted and observed secondary structure of t4 phage lysozyme
B.W. Matthews · 1975
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On biases in estimating multi-valued attributes
Igor Kononenko · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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A critique and improvement of the ”cl” common language effect size statistics of mcgraw and wong
András Vargha and Harold D. Delaney · 2000
Earlier work this paper cites.
Software security testing
B. Potter and G. McGraw · 2004
Earlier work this paper cites.
Predicting vulnerable software components
Stephan Neuhaus, Thomas Zimmermann, Christian Holler, and Andreas Zeller · 2007
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An empirical model to predict security vulnerabilities using code complexity metrics
Yonghee Shin and Laurie Williams · 2008
Earlier work this paper cites.
Cross-project defect prediction: A large scale experiment on data vs. domain vs. process
Thomas Zimmermann, Nachiappan Nagappan, Harald Gall, Emanuel Giger, and Brendan Murphy · 2009
Earlier work this paper cites.
Using complexity, coupling, and cohesion metrics as early indicators of vulnerabilities
Istehad Chowdhury and Mohammad Zulkernine · 2011
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Evaluating complexity, code churn, and developer activity metrics as indicators of software vulnerabilities
Yonghee Shin, Andrew Meneely, Laurie Williams, and Jason A. Osborne · 2011
Earlier work this paper cites.
Evaluating defect prediction approaches: A benchmark and an extensive comparison
Marco D’Ambros, Michele Lanza, and Romain Robbes · 2012
Earlier work this paper cites.
A systematic literature review on fault prediction performance in software engineering
T. Hall, S. Beecham, D. Bowes, D. Gray, and S. Counsell · 2012
Earlier work this paper cites.
Can traditional fault prediction models be used for vulnerability prediction?
Yonghee Shin and Laurie Williams · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate, 2014
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Predicting vulnerable software components via text mining
R. Scandariato, J. Walden, A. Hovsepyan, and W. Joosen · 2014
Earlier work this paper cites.
Researcher bias: The use of machine learning in software defect prediction
M. Shepperd, D. Bowes, and T. Hall · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks, 2014
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi et al · 2015
Cited alongside, same era.
Challenges with applying vulnerability prediction models
Patrick Morrison, Kim Herzig, Brendan Murphy, and Laurie Williams · 2015
Cited alongside, same era.
Predicting vulnerable components via text mining or software metrics? an effort-aware perspective
Yaming Tang, Fei Zhao, Yibiao Yang, Hongmin Lu, Yuming Zhou, and Baowen Xu · 2015
Cited alongside, same era.
Deep learning for just-in-time defect prediction
X. Yang, D. Lo, X. Xia, Y. Zhang, and J. Sun · 2015
Cited alongside, same era.
srcml 1.0: Explore, analyze, and manipulate source code
M. L. Collard and J. I. Maletic · 2016
Cited alongside, same era.
Deep api learning
Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim · 2016
Cited alongside, same era.
The importance of accounting for real-world labelling when predicting software vulnerabilities
Matthieu Jimenez, Renaud Rwemalika, Mike Papadakis, Federica Sarro, Yves Le Traon, and Mark Harman · 2019
Later among the works it cites.
Performance evaluation of deep neural networks applied to speech recognition: Rnn, lstm and gru
Apeksha Shewalkar, Deepika Nyavanandi, and Simone Ludwig · 2019
Later among the works it cites.
Learning how to mutate source code from bug-fixes
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk · 2019
Later among the works it cites.
An empirical study on learning bug-fixing patches in the wild via neural machine translation
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk · 2019
Later among the works it cites.
Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks, 2019
Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu · 2019
Later among the works it cites.
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Learning unified features from natural and programming languages for locating buggy source code
Xuan Huo, Ming Li, and Zhi-Hua Zhou · 2016
Cited alongside, same era.
An empirical analysis of vulnerabilities in openssl and the linux kernel
Matthieu Jimenez, Mike Papadakis, and Yves Le Traon · 2016
Cited alongside, same era.
Evaluating and comparing complexity, coupling and a new proposed set of coupling metrics in cross-project vulnerability prediction
Sara Moshtari and Ashkan Sami · 2016
Cited alongside, same era.
Automatically learning semantic features for defect prediction
Song Wang, Taiyue Liu, and Lin Tan · 2016
Cited alongside, same era.
Deep learning code fragments for code clone detection
M. White, M. Tufano, C. Vendome, and D. Poshyvanyk · 2016
Cited alongside, same era.
Massive Exploration of Neural Machine Translation Architectures
D. Britz, A. Goldie, T. Luong, and Q. Le · 2017
Cited alongside, same era.
Better together: Comparing vulnerability prediction models
Christopher Theisen and Laurie A. Williams · 2020
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https://cve.mitre.org/about/terminology.html
Definition of vulnerability · 2021
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https://heartbleed.com/
The heartbleed bug · 2021
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https://www.linux.com/news/linux-in-2020-27-8-million-lines-of-code-in-the-kernel-1-3-million-in-systemd/
Linux in 2020: 27.8 million lines of code in the kernel · 2021
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Linux kernal · 2021
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https://nvd.nist.gov
National vulnerability database · 2021
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https://www.openssl.org
Openssl · 2021
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https://owasp.org/www-community/vulnerabilities/
Vulnerabilities · 2021
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https://www.wireshark.org
Wireshark · 2021
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When to use mlp, cnn, and rnn neural networks
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