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Motivated by the transformative impact of deep neural networks (DNNs) in various domains, researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering.
Superoptimizer: A look at the smallest program
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Mimicry attacks on host-based intrusion detection systems. In Proc. CCS
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Testing malware detectors. In Proc. ISSTA
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Static disassembly of obfuscated binaries. In Proc. USENIX Security
Christopher Kruegel, William Robertson, Fredrik Valeur, and Giovanni Vigna. 2004 · 2004
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Practical analysis of stripped binary code
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The Art of Computer Virus Research and Defense
Peter Szor. 2005 · 2005
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J Zico Kolter and Marcus A Maloof. 2006 · 2006
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Normalizing metamorphic malware using term rewriting. In Proc. SCAM
Andrew Walenstein, Rachit Mathur, Mohamed R Chouchane, and Arun Lakhotia. 2006 · 2006
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Limits of static analysis for malware detection. In Proc. ACSAC
Andreas Moser, Christopher Kruegel, and Engin Kirda. 2007 · 2007
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So long, and no thanks for the externalities: the rational rejection of security advice by users. In Proc. NSPW
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A general paradigm for normalizing metamorphic malwares. In Proc. FIT
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Smashing the gadgets: Hindering return-oriented programming using in-place code randomization. In Proc. IEEE S&P
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Evasion attacks against machine learning at test time. In Proc. ECML PKDD
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Binary-code obfuscations in prevalent packer tools
Kevin A Roundy and Barton P Miller. 2013 · 2013
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Stochastic superoptimization. In Proc. ASPLOS
Eric Schkufza, Rahul Sharma, and Alex Aiken. 2013 · 2013
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DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket.. In Proc. NDSS
Daniel Arp, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck, and CERT Siemens. 2014 · 2014
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Obfuscation for evasive functions. In Proc. TCC
Boaz Barak, Nir Bitansky, Ran Canetti, Yael Tauman Kalai, Omer Paneth, and Amit Sahai. 2014 · 2014
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SoK: Automated software diversity. In Proc. IEEE S&P
Per Larsen, Andrei Homescu, Stefan Brunthaler, and Michael Franz. 2014 · 2014
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Distributed representations of sentences and documents. In Proc. ICML
Quoc Le and Tomas Mikolov. 2014 · 2014
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Practical evasion of a learning-based classifier: A case study. In Proc. IEEE S&P
Nedim Srndic and Pavel Laskov. 2014 · 2014
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Intriguing properties of neural networks. In Proc. ICLR
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. 2014 · 2014
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Explaining and Harnessing Adversarial Examples. In Proc. ICLR
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
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Obfuscator-LLVM–software protection for the masses. In Proc. IWSP
Pascal Junod, Julien Rinaldini, Johan Wehrli, and Julie Michielin. 2015 · 2015
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SoK: Deep packer inspection: A longitudinal study of the complexity of run-time packers. In Proc. IEEE S&P
Xabier Ugarte-Pedrero, Davide Balzarotti, Igor Santos, and Pablo G Bringas. 2015 · 2015
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Evasion and Hardening of Tree Ensemble Classifiers. In Proc. ICML
Alex Kantchelian, JD Tygar, and Anthony D. Joseph. 2016 · 2016
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Juggling the gadgets: Binary-level code randomization using instruction displacement. In Proc. AsiaCCS
Hyungjoon Koo and Michalis Polychronakis. 2016 · 2016
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The Limitations of Deep Learning in Adversarial Settings. In Proc. IEEE Euro S&P
Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z Berkay Celik, and Ananthram Swami. 2016 · 2016
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Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. In Proc. CCS
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K. Reiter. 2016 · 2016
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Uroboros: Instrumenting stripped binaries with static reassembling. In Proc. SANER
Shuai Wang, Pei Wang, and Dinghao Wu. 2016 · 2016
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Automatically evading classifiers. In Proc. NDSS
Weilin Xu, Yanjun Qi, and David Evans. 2016 · 2016
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Evading machine learning malware detection
Hyrum S Anderson, Anant Kharkar, Bobby Filar, and Phil Roth. 2017 · 2017
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Houdini: Fooling deep structured prediction models. In Proc. NIPS
Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet. 2017 · 2017
Cited alongside, same era.
Evading classifiers by morphing in the dark. In Proc. CCS
Hung Dang, Yue Huang, and Ee-Chien Chang. 2017 · 2017
Cited alongside, same era.
Yes, machine learning can be more secure! A case study on android malware detection
Ambra Demontis, Marco Melis, Battista Biggio, Davide Maiorca, Daniel Arp, Konrad Rieck, Igino Corona, Giorgio Giacinto, and Fabio Roli. 2017 · 2017
Cited alongside, same era.
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations. In Proc. NeurIPSW
Logan Engstrom, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry. 2017 · 2017
Cited alongside, same era.
Detecting Adversarial Samples from Artifacts
Reuben Feinman, Ryan R Curtin, Saurabh Shintre, and Andrew B Gardner. 2017 · 2017
Cited alongside, same era.
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers. In Proc. RAID
Ishai Rosenberg, Asaf Shabtai, Lior Rokach, and Yuval Elovici. 2018 · 2018
Later among the works it cites.
Defense-GAN: Protecting classifiers against adversarial attacks using generative models. In Proc. ICLR
Pouya Samangouei, Maya Kabkab, and Rama Chellappa. 2018 · 2018
Later among the works it cites.
Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller, Wojciech Samek, and Shinichi Nakajima. 2018 · 2018
Later among the works it cites.
Exploring Adversarial Examples in Malware Detection. In Proc. AAAIW
Octavian Suciu, Scott E Coull, and Jeffrey Johns. 2018 · 2018
Later among the works it cites.
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On the (statistical) detection of adversarial examples
Kathrin Grosse, Praveen Manoharan, Nicolas Papernot, Michael Backes, and Patrick McDaniel. 2017a · 2017
Cited alongside, same era.
Generating adversarial malware examples for black-box attacks based on GAN
Weiwei Hu and Ying Tan. 2017 · 2017
Cited alongside, same era.
Adversarial machine learning at scale. In Proc. ICLR
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2017 · 2017
Cited alongside, same era.
MagNet: A Two-Pronged Defense against Adversarial Examples. In Proc. CCS
Dongyu Meng and Hao Chen. 2017 · 2017
Cited alongside, same era.
On detecting adversarial perturbations. In Proc. ICLR
Jan Hendrik Metzen, Tim Genewein, Volker Fischer, and Bastian Bischoff. 2017 · 2017
Cited alongside, same era.
Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K. Reiter. 2017 · 2017
Cited alongside, same era.
Ember: An Open Dataset for Training Static PE Malware Machine Learning Models
H. S. Anderson and P. Roth. 2018 · 2018
Cited alongside, same era.
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan Yuille, and Kaiming He. 2018 · 2018
Later among the works it cites.
Feature squeezing: Detecting adversarial examples in deep neural networks. In Proc. NDSS
Weilin Xu, David Evans, and Yanjun Qi. 2018 · 2018
Later among the works it cites.
VirusTotal
Chronicle. 2004– · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter. 2019 · 2019
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What are Deep Neural Networks Learning About Malware?
Scott Coull and Christopher Gardner. 2018 · 2019
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Cylance: Artificial Intelligence Based Advanced Threat Prevention
Cylance Inc. 2019 · 2019
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Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries
Luca Demetrio, Battista Biggio, Giovanni Lagorio, Fabio Roli, and Alessandro Armando. 2019 · 2019
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The Cuckoo Sandbox
Claudio Guarnieri, Allessandro Tanasi, Jurriaan Bremer, and Mark Schloesser. 2012 · 2019
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IDA: About
Hex-Rays. [n.d.] · 2019
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Prior convictions: Black-box adversarial attacks with bandits and priors. In Proc. ICLR
Andrew Ilyas, Logan Engstrom, and Aleksander Madry. 2019 · 2019
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Neurlux: Dynamic malware analysis without feature engineering. In Proc. ACSAC
Chani Jindal, Christopher Salls, Hojjat Aghakhani, Keith Long, Christopher Kruegel, and Giovanni Vigna. 2019 · 2019
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PE Format
John Kennedy, Drew Batchelor, Colin Robertson, Michael Satran, and Mark LeBLanc. 2019 · 2019
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Certified robustness to adversarial examples with differential privacy. In Proc. IEEE S&P
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana. 2019 · 2019
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Generation Evaluation of Adversarial Examples for Malware Obfuscation. In Proc. ICMLA . 1283–1290
Daniel Park, Haidar Khan, and Bulent Yener. 2019 · 2019
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Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. In Proc. ICML
Yao Qin, Nicholas Carlini, Ian Goodfellow, Garrison Cottrell, and Colin Raffel. 2019 · 2019
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Misleading Authorship Attribution of Source Code using Adversarial Learning. In Proc. USENIX Security
Erwin Quiring, Alwin Maier, and Konrad Rieck. 2019 · 2019
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VirusShare
Michael Roberts. 2012 · 2019
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Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding. In Proc. NDSS
Lea Schönherr, Katharina Kohls, Steffen Zeiler, Thorsten Holz, and Dorothea Kolossa. 2019 · 2019
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Packer YARA Ruleset
Mike Sconzo. 2014 · 2019
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Packer YARA Ruleset
VirusTotal. 2016 · 2019
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Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan. 2019 · 2019
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Avast Malware detection and blocking
Avast Software. 2020 · 2020
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ClamAV: Creating signature for ClamAV
Cisco. 2020 · 2020
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UPX: The Ultimate Packer for Executables
Markus Oberhumer, Laszlo Molnar, and John Reiser. [n.d.] · 2020
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Intriguing Properties of Adversarial ML Attacks in the Problem Space. In Proc. IEEE S&P
F. Pierazzi, F. Pendlebury, J. Cortellazzi, and L. Cavallaro. 2020 · 2020
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How does Symantec Endpoint Protection use advanced machine learning?
Symantec. 2019 · 2020
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TrendMicro Machine Learning
TrendMicro. 2020 · 2020
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Vipre Android Security
Vipre. 2020 · 2020
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