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Fuzzing has become the de facto standard technique for finding software vulnerabilities.
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A theoretical and empirical study of search-based testing: Local, global, and hybrid search
M. Harman and P. McMinn · 2010
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TaintScope: A checksum-aware directed fuzzing tool for automatic software vulnerability detection
T. Wang, T. Wei, G. Gu, and W. Zou · 2010
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Symbolic execution for software testing in practice: preliminary assessment
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zzuf—multi-purpose fuzzer
S. Hocevar · 2011
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Continuity and robustness of programs
S. Chaudhuri, S. Gulwani, and R. Lublinerman · 2012
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Imagenet classification with deep convolutional neural networks
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Symbolic execution for software testing: three decades later
C. Cadar and K. Sen · 2013
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Comparison of statistical and machine learning methods in modelling of data with multicollinearity
A. Garg and K. Tai · 2013
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Dowsing for overflows: A guided fuzzer to find buffer boundary violations
I. Haller, A. Slowinska, M. Neugschwandtner, and H. Bos · 2013
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Synthesizing program input grammars
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Cyber Grand Challenge Repository
DARPA · 2017
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Neurogenesis deep learning: Extending deep networks to accommodate new classes
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Pathnet: Evolution channels gradient descent in super neural networks
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KleeFL - seeding fuzzers with symbolic execution
J. Fietkau, B. Shastry, and J.-P. Seifert · 2017
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Learn&Fuzz: Machine learning for input fuzzing
P. Godefroid, H. Peleg, and R. Singh · 2017
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Handbook of global optimization
R. Horst and P. M. Pardalos · 2013
Cited alongside, same era.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Cited alongside, same era.
KameleonFuzz: evolutionary fuzzing for black-box XSS detection
F. Duchene, S. Rawat, J.-L. Richier, and R. Groz · 2014
Cited alongside, same era.
Unsupervised neuron selection for mitigating catastrophic forgetting in neural networks
B. Goodrich and I. Arel · 2014
Cited alongside, same era.
A. Graves, G. Wayne, and I. Danihelka · 2014
Cited alongside, same era.
Fuzzing random programs without execve()
lcamtuf · 2014
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Measuring catastrophic forgetting in neural networks
R. Kemker, A. Abitino, M. McClure, and C. Kanan · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
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Steelix: Program-state based binary fuzzing
Y. Li, B. Chen, M. Chandramohan, S.-W. Lin, Y. Liu, and A. Tiu · 2017
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Faster fuzzing: Reinitialization with deep neural models
N. Nichols, M. Raugas, R. Jasper, and N. Hilliard · 2017
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SlowFuzz: automated domain-independent detection of algorithmic complexity vulnerabilities
T. Petsios, J. Zhao, A. D. Keromytis, and S. Jana · 2017
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Not All Bytes Are Equal: Neural Byte Sieve for Fuzzing
M. Rajpal, W. Blum, and R. Singh · 2017
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VUzzer: Application-aware evolutionary fuzzing
S. Rawat, V. Jain, A. Kumar, L. Cojocar, C. Giuffrida, and H. Bos · 2017
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Life-long learning based on dynamic combination model
B. Ren, H. Wang, J. Li, and H. Gao · 2017
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HVLearn: Automated black-box analysis of hostname verification in SSL/TLS implementations
S. Sivakorn, G. Argyros, K. Pei, A. D. Keromytis, and S. Jana · 2017
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Memory corruption mitigation via hardening and testing
L. Szekeres · 2017
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Skyfire: Data-driven seed generation for fuzzing
J. Wang, B. Chen, L. Wei, and Y. Liu · 2017
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https://github.com/google/sanitizers
Address sanitizer, thread sanitizer, and memory sanitizer · 2018
Closest in time.
https://keras.io/
Keras: The python deep learning library · 2018
Closest in time.
https://www.tensorflow.org/
An open source machine learning framework for everyone · 2018
Closest in time.
https://clang.llvm.org/docs/UndefinedBehaviorSanitizer.html
Undefined behavior sanitizer · 2018
Closest in time.
K. Böttinger, P. Godefroid, and R. Singh · 2018
Closest in time.
Angora: Efficient fuzzing by principled search
P. Chen and H. Chen · 2018
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Of Bugs and Baselines
B. Dolan-Gavitt · 2018
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T-Fuzz: fuzzing by program transformation
H. Peng, Y. Shoshitaishvili, and M. Payer · 2018
Closest in time.
T-Fuzz: fuzzing by program transformation
H. Peng, Y. Shoshitaishvili, and M. Payer · 2018
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libFuzzer – a library for coverage-guided fuzz testing
K. Serebryany · 2018
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Neuro-symbolic execution: The feasibility of an inductive approach to symbolic execution
S. Shen, S. Ramesh, S. Shinde, A. Roychoudhury, and P. Saxena · 2018
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American Fuzzy Lop (AFL) README
M. Zalewski · 2018
Closest in time.