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Automated detection of software vulnerabilities is a fundamental problem in software security.
J. S. Bridle, “Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition,” in Neurocomputing . Springer, 1990, pp. 227–236
1990
Earlier work this paper cites.
B. W. SUTER, “The multilayer perceptron as an approximation to a bayes optimal discriminant function,” IEEE Transactions on Neural Networks , vol. 1, no. 4, p. 291, 1990
1990
Earlier work this paper cites.
F. Girosi, M. Jones, and T. Poggio, “Regularization theory and neural networks architectures,” Neural computation , vol. 7, no. 2, pp. 219–269, 1995
1995
Earlier work this paper cites.
H. D. Beale, H. B. Demuth, and M. Hagan, “Neural network design,” Pws, Boston , 1996
1996
Earlier work this paper cites.
K. Plunkett and J. L. Elman, Exercises in rethinking innateness: A handbook for connectionist simulations . Mit Press, 1997
1997
Earlier work this paper cites.
J. Platt et al. , “Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods,” Advances in large margin classifiers , vol. 10, no. 3, pp. 61–74, 1999
1999
Earlier work this paper cites.
A. Vargha and H. D. Delaney, “A critique and improvement of the cl common language effect size statistics of mcgraw and wong,” Journal of Educational and Behavioral Statistics , vol. 25, no. 2, pp. 101–132, 2000
2000
Earlier work this paper cites.
B. Baesens, S. Viaene, T. Van Gestel, J. A. Suykens, G. Dedene, B. De Moor, and J. Vanthienen, “An empirical assessment of kernel type performance for least squares support vector machine classifiers,” in KES’2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No. 00TH8516) , vol. 1. IEEE, 2000, pp. 313–316
2000
Earlier work this paper cites.
M. G. Canteri, R. A. Althaus, J. S. das Virgens Filho, E. Giglioti, and C. V. Godoy, “Sasm-agri-sistema para análise e separação de médias em experimentos agrícolas pelos métodos scott-knott, tukey e duncan.” Embrapa Soja-Artigo em periódico indexado (ALICE) , 2001
2001
Earlier work this paper cites.
R. W. Johnson, “An introduction to the bootstrap,” Teaching Statistics , vol. 23, no. 2, pp. 49–54, 2001
2001
Earlier work this paper cites.
D. Larochelle and D. Evans, “Statically detecting likely buffer overflow vulnerabilities,” in 10th USENIX Security Symposium , 2001
2001
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,” Journal of artificial intelligence research , vol. 16, pp. 321–357, 2002
2002
Earlier work this paper cites.
G. Wu and E. Y. Chang, “Class-boundary alignment for imbalanced dataset learning,” in ICML 2003 workshop on learning from imbalanced data sets II, Washington, DC , 2003, pp. 49–56
2003
Earlier work this paper cites.
P. Koehn, “Statistical significance tests for machine translation evaluation,” in Proceedings of the 2004 conference on empirical methods in natural language processing , 2004, pp. 388–395
2004
Earlier work this paper cites.
M. R. Hess and J. D. Kromrey, “Robust confidence intervals for effect sizes: A comparative study of cohen’sd and cliff’s delta under non-normality and heterogeneous variances,” in annual meeting of the American Educational Research Association , 2004, pp. 1–30
2004
Earlier work this paper cites.
J. Newsome and D. X. Song, “Dynamic taint analysis for automatic detection, analysis, and signaturegeneration of exploits on commodity software.” in NDSS , vol. 5. Citeseer, 2005, pp. 3–4
2005
Earlier work this paper cites.
R. J. Grissom and J. J. Kim, Effect sizes for research: A broad practical approach. Lawrence Erlbaum Associates Publishers, 2005
2005
Earlier work this paper cites.
M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar, “Can machine learning be secure?” in Proceedings of the 2006 ACM Symposium on Information, computer and communications security . ACM, 2006, pp. 16–25
2006
Earlier work this paper cites.
A. Aggarwal and P. Jalote, “Integrating static and dynamic analysis for detecting vulnerabilities,” in 30th Annual International Computer Software and Applications Conference (COMPSAC’06) , vol. 1. IEEE, 2006, pp. 343–350
2006
Earlier work this paper cites.
N. Ayewah, W. Pugh, J. D. Morgenthaler, J. Penix, and Y. Zhou, “Evaluating static analysis defect warnings on production software,” 2007
2007
Earlier work this paper cites.
M. D. Smucker, J. Allan, and B. Carterette, “A comparison of statistical significance tests for information retrieval evaluation,” in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management , 2007, pp. 623–632
2007
Earlier work this paper cites.
E. Yudkowsky, “Artificial intelligence as a positive and negative factor in global risk,” Global catastrophic risks , vol. 1, no. 303, p. 184, 2008
2008
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research , vol. 9, no. Nov, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
H. Joh, J. Kim, and Y. K. Malaiya, “Vulnerability discovery modeling using weibull distribution,” in 2008 19th International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 2008, pp. 299–300
2008
Earlier work this paper cites.
Y. Sun, A. K. Wong, and M. S. Kamel, “Classification of imbalanced data: A review,” International journal of pattern recognition and artificial intelligence , vol. 23, no. 04, pp. 687–719, 2009
2009
Earlier work this paper cites.
C. Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “Rusboost: A hybrid approach to alleviating class imbalance,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans , vol. 40, no. 1, pp. 185–197, 2009
2009
Earlier work this paper cites.
J. Turian, L. Ratinov, and Y. Bengio, “Word representations: a simple and general method for semi-supervised learning,” in Proceedings of the 48th annual meeting of the association for computational linguistics . Association for Computational Linguistics, 2010, pp. 384–394
2010
Earlier work this paper cites.
A. Torralba and A. Efros, “Unbiased look at dataset bias,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition . IEEE Computer Society, 2011, pp. 1521–1528
2011
Earlier work this paper cites.
S. Heckman and L. Williams, “A systematic literature review of actionable alert identification techniques for automated static code analysis,” Information and Software Technology , vol. 53, no. 4, pp. 363–387, 2011
2011
Earlier work this paper cites.
D. M. Powers, “Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation,” 2011
2011
Earlier work this paper cites.
A. Arcuri and L. Briand, “A practical guide for using statistical tests to assess randomized algorithms in software engineering,” in Proceedings of the 33rd International Conference on Software Engineering , ser. ICSE ’11. New York, NY, USA: Association for Computing Machinery, 2011, p. 1–10. [Online]. Available: https://doi.org/10.1145/1985793.1985795
2011
Earlier work this paper cites.
B. Liu, L. Shi, Z. Cai, and M. Li, “Software vulnerability discovery techniques: A survey,” in 2012 fourth international conference on multimedia information networking and security . IEEE, 2012, pp. 152–156
2012
Earlier work this paper cites.
N. Mittas and L. Angelis, “Ranking and clustering software cost estimation models through a multiple comparisons algorithm,” IEEE Transactions on software engineering , vol. 39, no. 4, pp. 537–551, 2012
2012
Earlier work this paper cites.
S. Barua, M. M. Islam, X. Yao, and K. Murase, “Mwmote–majority weighted minority oversampling technique for imbalanced data set learning,” IEEE Transactions on knowledge and data engineering , vol. 26, no. 2, pp. 405–425, 2012
2012
Cited alongside, same era.
B. Johnson, Y. Song, E. Murphy-Hill, and R. Bowdidge, “Why don’t software developers use static analysis tools to find bugs?” in Proceedings of the 2013 International Conference on Software Engineering . IEEE Press, 2013, pp. 672–681
2013
Cited alongside, same era.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Cited alongside, same era.
V. Okun, A. Delaitre, and P. E. Black, “Report on the static analysis tool exposition (sate) iv,” NIST Special Publication , vol. 500, p. 297, 2013
2013
Cited alongside, same era.
G. Suarez-Tangil, S. K. Dash, M. Ahmadi, J. Kinder, G. Giacinto, and L. Cavallaro, “Droidsieve: Fast and accurate classification of obfuscated android malware,” in Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy . ACM, 2017, pp. 309–320
2017
Later among the works it cites.
Y. Zhou and A. Sharma, “Automated identification of security issues from commit messages and bug reports,” in Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering , 2017, pp. 914–919
2017
Later among the works it cites.
R. Zhao, D. Wang, R. Yan, K. Mao, F. Shen, and J. Wang, “Machine health monitoring using local feature-based gated recurrent unit networks,” IEEE Transactions on Industrial Electronics , vol. 65, no. 2, pp. 1539–1548, 2017
2017
Later among the works it cites.
2017
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H. Booth, D. Rike, and G. Witte, “The national vulnerability database (nvd): Overview,” National Institute of Standards and Technology, Tech. Rep., 2013
2013
Cited alongside, same era.
L. Lusa et al. , “Smote for high-dimensional class-imbalanced data,” BMC bioinformatics , vol. 14, no. 1, p. 106, 2013
2013
Cited alongside, same era.
K. Herzig and A. Zeller, “The impact of tangled code changes,” in 2013 10th Working Conference on Mining Software Repositories (MSR) . IEEE, 2013, pp. 121–130
2013
Cited alongside, same era.
F. Yamaguchi, C. Wressnegger, H. Gascon, and K. Rieck, “Chucky: Exposing missing checks in source code for vulnerability discovery,” in Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security . ACM, 2013, pp. 499–510
2013
Cited alongside, same era.
F. Yamaguchi, N. Golde, D. Arp, and K. Rieck, “Modeling and discovering vulnerabilities with code property graphs,” in 2014 IEEE Symposium on Security and Privacy . IEEE, 2014, pp. 590–604
2014
Cited alongside, same era.
2014
Cited alongside, same era.
X. Rong, “word2vec parameter learning explained,” arXiv preprint arXiv:1411.2738 , 2014
2014
Cited alongside, same era.
D. F. Ferreira, “Sisvar: a guide for its bootstrap procedures in multiple comparisons,” Ciência e agrotecnologia , vol. 38, no. 2, pp. 109–112, 2014
2014
Cited alongside, same era.
Later among the works it cites.
J. Wang, F. Zhou, S. Wen, X. Liu, and Y. Lin, “Deep metric learning with angular loss,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 2593–2601
2017
Later among the works it cites.
2017
Later among the works it cites.
G. Lin, J. Zhang, W. Luo, L. Pan, and Y. Xiang, “Poster: Vulnerability discovery with function representation learning from unlabeled projects,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , 2017, pp. 2539–2541
2017
Later among the works it cites.
H. Alsawalqah, H. Faris, I. Aljarah, L. Alnemer, and N. Alhindawi, “Hybrid smote-ensemble approach for software defect prediction,” in Computer Science On-line Conference . Springer, 2017, pp. 355–366
2017
Later among the works it cites.
Z. Li, D. Zou, S. Xu, X. Ou, H. Jin, S. Wang, Z. Deng, and Y. Zhong, “Vuldeepecker: A deep learning-based system for vulnerability detection,” in Proceedings of the 25th Annual Network and Distributed System Security Symposium (NDSS‘2018) , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
R. Russell, L. Kim, L. Hamilton, T. Lazovich, J. Harer, O. Ozdemir, P. Ellingwood, and M. McConley, “Automated vulnerability detection in source code using deep representation learning,” in Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018) . IEEE, 2018, pp. 757–762
2018
Later among the works it cites.
W. Guo, D. Mu, J. Xu, P. Su, G. Wang, and X. Xing, “Lemna: Explaining deep learning based security applications,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2018, pp. 364–379
2018
Later among the works it cites.
N. I. of Standards and Technology, “Software assurance reference dataset,” October 2018. [Online]. Available: https://samate.nist.gov/SRD/index.php
2018
Later among the works it cites.
A. Agrawal and T. Menzies, “Is “better data” better than “better data miners”?” in 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) . IEEE, 2018, pp. 1050–1061
2018
Later among the works it cites.
B. Kellenberger, D. Marcos, and D. Tuia, “Detecting mammals in uav images: Best practices to address a substantially imbalanced dataset with deep learning,” Remote sensing of environment , vol. 216, pp. 139–153, 2018
2018
Later among the works it cites.
S. Ding, B. Mirza, Z. Lin, J. Cao, X. Lai, T. V. Nguyen, and J. Sepulveda, “Kernel based online learning for imbalance multiclass classification,” Neurocomputing , vol. 277, pp. 139–148, 2018
2018
Later among the works it cites.
S. Sothornprapakorn, S. Hayashi, and M. Saeki, “Visualizing a tangled change for supporting its decomposition and commit construction,” in 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) , vol. 1. IEEE, 2018, pp. 74–79
2018
Later among the works it cites.
C. Pak, T. T. Wang, and X. H. Su, “An empirical study on software defect prediction using over-sampling by smote,” International Journal of Software Engineering and Knowledge Engineering , vol. 28, no. 06, pp. 811–830, 2018
2018
Later among the works it cites.
D. Maiorca and B. Biggio, “Digital investigation of pdf files: Unveiling traces of embedded malware,” IEEE Security & Privacy , vol. 17, no. 1, pp. 63–71, 2019
2019
Later among the works it cites.
Y. Zhou, S. Liu, J. Siow, X. Du, and Y. Liu, “Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks,” in Advances in Neural Information Processing Systems , 2019, pp. 10 197–10 207
2019
Later among the works it cites.
L. Developers, “Clang,” October 2019. [Online]. Available: clang.llvm.org
2019
Later among the works it cites.
C. Developers, “Cppcheck: A tool for static c/c++ code analysis,” October 2019. [Online]. Available: http://cppcheck.sourceforge.net
2019
Later among the works it cites.
C. Mao, Z. Zhong, J. Yang, C. Vondrick, and B. Ray, “Metric learning for adversarial robustness,” in Advances in Neural Information Processing Systems , 2019, pp. 478–489
2019
Later among the works it cites.
D. Zou, S. Wang, S. Xu, Z. Li, and H. Jin, “ μ \displaystyle\mu vuldeepecker: A deep learning-based system for multiclass vulnerability detection,” IEEE Transactions on Dependable and Secure Computing , 2019
2019
Later among the works it cites.
M. Allamanis, “The adverse effects of code duplication in machine learning models of code,” in Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software , 2019, pp. 143–153
2019
Later among the works it cites.
M. Wang, Z. Lin, Y. Zou, and B. Xie, “Cora: decomposing and describing tangled code changes for reviewer,” in 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2019, pp. 1050–1061
2019
Later among the works it cites.
X. Ban, S. Liu, C. Chen, and C. Chua, “A performance evaluation of deep-learnt features for software vulnerability detection,” Concurrency and Computation: Practice and Experience , vol. 31, no. 19, p. e5103, 2019
2019
Later among the works it cites.
D. She, K. Pei, D. Epstein, J. Yang, B. Ray, and S. Jana, “Neuzz: Efficient fuzzing with neural program smoothing,” in 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 2019, pp. 803–817
2019
Later among the works it cites.
S. Guo, R. Chen, H. Li, T. Zhang, and Y. Liu, “Identify severity bug report with distribution imbalance by cr-smote and elm,” International Journal of Software Engineering and Knowledge Engineering , vol. 29, no. 02, pp. 139–175, 2019
2019
Later among the works it cites.
P. Mackerras, “Cve-2020-8597 patch commit.” [Online]. Available: https://github.com/paulusmack/ppp/commit/8d7970b8f3db727fe798b65f3377fe6787575426
2020
Closest in time.
“Cve-2020-8597.” [Online]. Available: https://security-tracker.debian.org/tracker/CVE-2020-8597
2020
Closest in time.
2020
Closest in time.
S. Tyagi and S. Mittal, “Sampling approaches for imbalanced data classification problem in machine learning,” in Proceedings of ICRIC 2019 . Springer, 2020, pp. 209–221
2020
Closest in time.