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Recent research efforts on adversarial machine learning (ML) have investigated problem-space attacks, focusing on the generation of real evasive objects in domains where, unlike images, there is no clear inverse mapping to the feature space (e.g., software).
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Nilesh Dalvi, Pedro Domingos, Sumit Sanghai and Deepak Verma · 2004
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Daniel Lowd and Christopher Meek · 2005
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“Pattern Recognition and Machine Learning”, 2006
Christopher Bishop · 2006
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Engin Andreas Christopher · 2007
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“Limits of Static Analysis for Malware Detection”
Andreas Moser, Christopher Kruegel and Engin Kirda · 2007
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“Adversarial machine learning”
Ling Huang et al · 2011
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“Static Detection of Malicious JavaScript-Bearing PDF Documents”
Pavel Laskov and Nedim Šrndić · 2011
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“Scikit-Learn: Machine Learning in Python”
F. Pedregosa et al · 2011
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“A Pattern Recognition System for Malicious PDF Files Detection”
Davide Maiorca, Giorgio Giacinto and Igino Corona · 2012
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“Security evaluation of pattern classifiers under attack”
Battista Biggio, Giorgio Fumera and Fabio Roli · 2013
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“Evasion attacks against machine learning at test time”
Battista Biggio et al · 2013
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“Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues”
Igino Corona, Giorgio Giacinto and Fabio Roli · 2013
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“Looking at the bag is not enough to find the bomb: an evasion of structural methods for malicious pdf files detection”
Davide Maiorca, Igino Corona and Giorgio Giacinto · 2013
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“DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket”
Daniel Arp et al · 2014
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“FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps”
Steven Arzt et al · 2014
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“Explaining and harnessing adversarial examples”
Ian Goodfellow, Jonathon Shlens and Christian Szegedy · 2014
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“Practical evasion of a learning-based classifier: A case study”
Pavel Laskov · 2014
Earlier work this paper cites.
“Intriguing properties of neural networks”
Christian Szegedy et al · 2014
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“Automated Software Transplantation”
Earl Barr et al · 2015
Cited alongside, same era.
“Explaining and Harnessing Adversarial Examples”
Ian. Goodfellow, Jonathon Shlens and Christian Szegedy · 2015
Cited alongside, same era.
“SoK: Deep packer inspection: A longitudinal study of the complexity of run-time packers”
Xabier Ugarte-Pedrero, Davide Balzarotti, Igor Santos and Pablo Bringas · 2015
Cited alongside, same era.
“Fundamentals of Model Theory”
William Weiss and Cherie D’Mello · 2015
Cited alongside, same era.
“Androzoo: Collecting Millions of Android Apps for the Research Community”
Kevin Allix, Tegawendé Bissyandé, Jacques Klein and Yves Le · 2016
Cited alongside, same era.
“Deep Learning”
Ian Goodfellow, Yoshua Bengio and Aaron Courville · 2016
Cited alongside, same era.
“Evading classifiers in discrete domains with provable optimality guarantees”
Bogdan Kulynych, Jamie Hayes, Nikita Samarin and Carmela Troncoso · 2018
Later among the works it cites.
“Explaining black-box android malware detection”
Marco Melis et al · 2018
Later among the works it cites.
“Malware detection by eating a whole exe”
Edward Raff et al · 2018
Later among the works it cites.
“Generic black-box end-to-end attack against state of the art API call based malware classifiers”
Ishai Rosenberg, Asaf Shabtai, Lior Rokach and Yuval Elovici · 2018
Later among the works it cites.
“When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks”
Octavian Suciu et al · 2018
Later among the works it cites.
“When Malware is Packin’ Heat”
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“Reviewer Integration and Performance Measurement for Malware Detection”
Brad Miller et al · 2016
Cited alongside, same era.
“The limitations of deep learning in adversarial settings”
Nicolas Papernot et al · 2016
Cited alongside, same era.
“Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition”
Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer and Michael Reiter · 2016
Cited alongside, same era.
“Automatically Evading Classifiers”
Weilin Xu, Yanjun Qi and David Evans · 2016
Cited alongside, same era.
“Towards evaluating the robustness of neural networks”
Nicholas Carlini and David Wagner · 2017
Cited alongside, same era.
“Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods”
Nicholas Carlini and David. Wagner · 2017
Cited alongside, same era.
Giovanni Vigna and Davide Balzarotti · 2018
Later among the works it cites.
“Evaluating the effectiveness of Adversarial Attacks against Botnet Detectors”
Giovanni Apruzzese, Michele Colajanni and Mirco Marchetti · 2019
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“On evaluating adversarial robustness”
Nicholas Carlini et al · 2019
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“HideNoSeek: Camouflaging Malicious JavaScript in Benign ASTs”
Aurore Fass, Michael Backes and Ben Stock · 2019
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“Adversarial attacks on medical machine learning”
Samuel Finlayson et al · 2019
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“TextBugger: Generating Adversarial Text Against Real-world Applications”
Jinfeng Li et al · 2019
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“Towards Robust Detection of Adversarial Infection Vectors: Lessons Learned in PDF Malware”
Davide Maiorca, Battista Biggio and Giorgio Giacinto · 2019
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“Adversarial Authorship Attribution in Open-Source Projects”
Alina Matyukhina, Natalia Stakhanova, Mila Dalla and Celine Perley · 2019
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“TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time” USENIX Sec
Feargus Pendlebury et al · 2019
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“Misleading Authorship Attribution of Source Code using Adversarial Learning”
Erwin Quiring, Alwin Maier and Konrad Rieck · 2019
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“Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency”
Shuhuai Ren, Yihe Deng, Kun He and Wanxiang Che · 2019
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“Improving robustness of { \{ ML } \} classifiers against realizable evasion attacks using conserved features”
Liang Tong et al · 2019
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“Seeing is Not Believing: Camouflage Attacks on Image Scaling Algorithms”
Qixue Xiao et al · 2019
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“Theoretically principled trade-off between robustness and accuracy”
Hongyang Zhang et al · 2019
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“Adversarial Machine Learning Beyond the Image Domain”
Giulio Zizzo, Chris Hankin, Sergio Maffeis and Kevin Jones · 2019
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“Permissions overview - Dangerous Permissions”, 2020
Android · 2020
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“Explore the transformation space for adversarial images”
Jiyu Chen, David Wang and Hao Chen · 2020
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“Malware Makeover: Breaking ML-Based Static Analysis by Modifying Executable Bytes”
Keane Lucas et al · 2021
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“On the empirical effectiveness of unrealistic adversarial hardening against realistic adversarial attacks”
Salijona Dyrmishi et al · 2023
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“Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting”
Ping He, Yifan Xia, Xuhong Zhang and Shouling Ji · 2023
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“Adversarial Training for { \{ Raw-Binary } \} Malware Classifiers”
Keane Lucas et al · 2023
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