Fetching the paper…
Reading the bibliography…
Fuzzing is a widely used technique for detecting software bugs and vulnerabilities.
A Review of Machine Learning Applications in Fuzzing
Gary J Saavedra, Kathryn N Rodhouse, Daniel M Dunlavy, and Philip W Kegelmeyer. 2019 · 1906
Earlier work this paper cites.
An empirical study of the reliability of UNIX utilities
Barton P Miller, Louis Fredriksen, and Bryan So. 1990 · 1990
Earlier work this paper cites.
Discriminability-Based Transfer between Neural Networks. In Advances in Neural Information Processing Systems 5, [NIPS Conference] . San Francisco, CA, USA
Lorien Y. Pratt. 1992 · 1992
Earlier work this paper cites.
Algorithms and applications for multitask learning. In Icml . 87–95
Rich Caruana. 1996 · 1996
Earlier work this paper cites.
A Bayesian/information theoretic model of learning to learn via multiple task sampling
Jonathan Baxter. 1997 · 1997
Earlier work this paper cites.
Multitask learning
Rich Caruana. 1997 · 1997
Earlier work this paper cites.
GRIMOIRE: Synthesizing Structure while Fuzzing. In 28th USENIX Security Symposium (USENIX Security 19) . USENIX Association, Santa Clara, CA, 1985–2002
Tim Blazytko, Cornelius Aschermann, Moritz Schlögel, Ali Abbasi, Sergej Schumilo, Simon Wörner, and Thorsten Holz. 2019 · 2002
Earlier work this paper cites.
Hybridizing Evolutionary Testing with the Chaining Approach. In Genetic and Evolutionary Computation – GECCO 2004 , Kalyanmoy Deb (Ed.). Springer Berlin Heidelberg
Phil McMinn and Mike Holcombe. 2004b · 2004
Earlier work this paper cites.
DART: directed automated random testing. In ACM Sigplan Notices , Vol. 40. Acm, 213–223
Patrice Godefroid, Nils Klarlund, and Koushik Sen. 2005 · 2005
Earlier work this paper cites.
Constructing Informative Priors Using Transfer Learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML ’06)
Rajat Raina, Andrew Y. Ng, and Daphne Koller. 2006 · 2006
Earlier work this paper cites.
CUTE and jCUTE: Concolic unit testing and explicit path model-checking tools. In Intl. Conf. Computer Aided Verification . Springer, 419–423
Koushik Sen and Gul Agha. 2006 · 2006
Earlier work this paper cites.
Mapping and Revising Markov Logic Networks for Transfer Learning. In Proceedings of the 22nd National Conference on Artificial Intelligence - Volume 1 (AAAI’07)
Lilyana Mihalkova, Tuyen Huynh, and Raymond J. Mooney. 2007 · 2007
Earlier work this paper cites.
Convex multi-task feature learning
Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. 2008 · 2008
Earlier work this paper cites.
KLEE: Unassisted and Automatic Generation of High-Coverage Tests for Complex Systems Programs.. In USENIX Symp. on Operating Systems Design and Implementation (OSDI) , Vol. 8. 209–224
Cristian Cadar, Daniel Dunbar, Dawson R Engler, et al · 2008
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning . 160–167
Ronan Collobert and Jason Weston. 2008 · 2008
Earlier work this paper cites.
Automated Whitebox Fuzz Testing
Patrice Godefroid, Michael Y Levin, and David A Molnar. 2008b · 2008
Earlier work this paper cites.
Guest editor’s introduction: Special issue on inductive transfer learning
Daniel Silver and Kristin Bennett. 2008 · 2008
Earlier work this paper cites.
Feature Selection by Transfer Learning with Linear Regularized Models
Thibault Helleputte and Pierre Dupont. 2009 · 2009
Earlier work this paper cites.
It does matter how you normalise the branch distance in search based software testing. In 3rd Intl. Conf. Software Testing, Verification and Validation . Ieee, 205–214
Andrea Arcuri. 2010 · 2010
Earlier work this paper cites.
Fuzzing at scale
Chris Evans, Matt Moore, and Tavis Ormandy. 2011 · 2011
Earlier work this paper cites.
zzuf-multi-purpose fuzzer
S Hocevar. 2011 · 2011
Earlier work this paper cites.
Search-based software testing: Past, present and future. In 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops . Ieee, 153–163
Phil McMinn. 2011 · 2011
Earlier work this paper cites.
Fuzzing for security
Abhishek Arya and Cris Neckar. 2012 · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013 · 2013
Earlier work this paper cites.
Monte Carlo theory, methods and examples
Art B. Owen. 2013 · 2013
Earlier work this paper cites.
Automated test data generation for branch testing using genetic algorithm: An improved approach using branch ordering, memory and elitism
Ankur Pachauri and Gursaran Srivastava. 2013 · 2013
Cited alongside, same era.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013 · 2013
Cited alongside, same era.
Efficient multi-task feature learning with calibration. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . 761–770
Pinghua Gong, Jiayu Zhou, Wei Fan, and Jieping Ye. 2014 · 2014
Cited alongside, same era.
Program-adaptive mutational fuzzing
Sang Kil Cha, Maverick Woo, and David Brumley. 2015 · 2015
Cited alongside, same era.
Deep spatial autoencoders for visuomotor learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel. 2015 · 2015
A survey on multi-task learning
Yu Zhang and Qiang Yang. 2017 · 2017
Later among the works it cites.
Deep reinforcement fuzzing. In Security and Privacy Workshops (SPW) . Ieee, 116–122
Konstantin Böttinger, Patrice Godefroid, and Rishabh Singh. 2018 · 2018
Later among the works it cites.
Angora: Efficient fuzzing by principled search
Peng Chen and Hao Chen. 2018 · 2018
Later among the works it cites.
Contractfuzzer: Fuzzing smart contracts for vulnerability detection. In Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering . Acm, 259–269
Bo Jiang, Ye Liu, and WK Chan. 2018 · 2018
Later among the works it cites.
Fairfuzz: Targeting rare branches to rapidly increase greybox fuzz testing coverage. In Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering . Acm
Caroline Lemieux and Koushik Sen. 2018 · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep convolutional inverse graphics network. In Advances in neural information processing systems . 2539–2547
Tejas D Kulkarni, William F Whitney, Pushmeet Kohli, and Josh Tenenbaum. 2015 · 2015
Cited alongside, same era.
Learning Transferable Features with Deep Adaptation Networks. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37 (ICML’15)
Mingsheng Long, Yue Cao, Jianmin Wang, and Michael I. Jordan. 2015 · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning. In 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI) . 265–283
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in neural information processing systems . 2172–2180
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016 · 2016
Cited alongside, same era.
Cyber Grand Challenge
Darpa. 2016 · 2016
Cited alongside, same era.
Mining Input Grammars from Dynamic Taints. In Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering . Acm, New York, NY, USA, 720–725
Matthias Höschele and Andreas Zeller. 2016 · 2016
Cited alongside, same era.
Understand scene categories by objects: A semantic regularized scene classifier using convolutional neural networks. In 2016 IEEE international conference on robotics and automation (ICRA) . IEEE, 2318–2325
Yiyi Liao, Sarath Kodagoda, Yue Wang, Lei Shi, and Yong Liu. 2016 · 2016
Cited alongside, same era.
Later among the works it cites.
Fuzzing: Art, science, and engineering
Valentin JM Manes, HyungSeok Han, Choongwoo Han, Sang Kil Cha, Manuel Egele, Edward J Schwartz, and Maverick Woo. 2018 · 2018
Later among the works it cites.
Multinet: Real-time joint semantic reasoning for autonomous driving. In 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 1013–1020
Marvin Teichmann, Michael Weber, Marius Zoellner, Roberto Cipolla, and Raquel Urtasun. 2018 · 2018
Later among the works it cites.
Singularity: Pattern fuzzing for worst case complexity. In Proc. 26th ACM Joint Meeting on European Software Engineering Conf. and Symp. Foundations of Software Engineering . Acm, 213–223
Jiayi Wei, Jia Chen, Yu Feng, Kostas Ferles, and Isil Dillig. 2018 · 2018
Later among the works it cites.
SLF: fuzzing without valid seed inputs. In Proceedings of the 41st ACM/IEEE International Conference on Software Engineering (ICSE’19) . Acm, 712–723
Wei Zhen You, Xuwei Liu, Shiqing Ma, David Perry, Xiangyu Zhang, and Bin Liang. 2018 · 2018
Later among the works it cites.
Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 3712–3722
Amir R Zamir, Alexander Sax, William Shen, Leonidas J Guibas, Jitendra Malik, and Silvio Savarese. 2018 · 2018
Later among the works it cites.
REDQUEEN: Fuzzing with Input-to-State Correspondence
Cornelius Aschermann, Sergej Schumilo, Tim Blazytko, Robert Gawlik, and Thorsten Holz. 2019 · 2019
Later among the works it cites.
DifFuzz: Differential Fuzzing for Side-channel Analysis. In Proc. 41st Intl. Conf. Software Engineering (Icse ’19) . IEEE Press, Piscataway, NJ, USA, 176–187
Shirin Nilizadeh, Yannic Noller, and Corina S. Păsăreanu. 2019 · 2019
Later among the works it cites.
Smart Greybox Fuzzing. In IEEE Transactions on Software Engineering
Thuan Pham, Marcel Böhme, Andrew Santosa, Alexandru Răzvan Căciulescu, and Abhik Roychoudhury. 2019 · 2019
Later among the works it cites.
Neutaint: Efficient Dynamic Taint Analysis with Neural Networks
Dongdong She, Yizheng Chen, Baishakhi Ray, and Suman Jana. 2019a · 2019
Later among the works it cites.
Neuzz: Efficient fuzzing with neural program learning
Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, and Suman Jana. 2019b · 2019
Later among the works it cites.
Which Tasks Should Be Learned Together in Multi-task Learning?
Trevor Standley, Amir R Zamir, Dawn Chen, Leonidas Guibas, Jitendra Malik, and Silvio Savarese. 2019 · 2019
Later among the works it cites.
Superion: Grammar-aware Greybox Fuzzing. In Proc. 41st International Conf. Software Engineering (Icse ’19) . IEEE Press, Piscataway, NJ, USA, 724–735
Junjie Wang, Bihuan Chen, Lei Wei, and Yang Liu. 2019a · 2019
Later among the works it cites.
Be Sensitive and Collaborative: Analyzing Impact of Coverage Metrics in Greybox Fuzzing, In 22nd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2019)
Jinghan Wang, Yue Duan, Wei Song, Heng Yin, and Chengyu Song. 2019b · 2019
Later among the works it cites.
ProFuzzer: On-the-fly Input Type Probing for Better Zero-Day Vulnerability Discovery
Wei Zhen You, Xueqiang Wang, Shiqing Ma, Jianjun Huang, Xiangyu Zhang, Xiaofeng Wang, and Bin Liang. 2019 · 2019
Later among the works it cites.
Address Sanitizer, Thread Sanitizer, and Memory Sanitizer
2020 · 2020
Closest in time.
UndefinedBehaviorSanitizer
2020 · 2020
Closest in time.
xmlwf xml parser
2020 · 2020
Closest in time.
GREYONE: Data Flow Sensitive Fuzzing. In 29th USENIX Security Symposium (USENIX Security 20) . USENIX Association, Boston, MA
Shuitao Gan, Chao Zhang, Peng Chen, Bodong Zhao, Xiaojun Qin, Dong Wu, and Zuoning Chen. 2020 · 2020
Closest in time.
Identifying beneficial task relations for multi-task learning in deep neural networks
Joachim Binge and Anders Sogaard. 2017 · 2026
Closest in time.