Fetching the paper…
Reading the bibliography…
Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware.
Hidenoseek: Camouflaging malicious javascript in benign asts. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security . 1899–1913
Aurore Fass, Michael Backes, and Ben Stock. 2019 · 1913
Earlier work this paper cites.
Data mining methods for detection of new malicious executables. In Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001 . IEEE, 38–49
Matthew G Schultz, Eleazar Eskin, F Zadok, and Salvatore J Stolfo. 2000 · 2001
Earlier work this paper cites.
An empirical evaluation of thompson sampling. In Advances in neural information processing systems . 2249–2257
Olivier Chapelle and Lihong Li. 2011 · 2011
Earlier work this paper cites.
Automatic analysis of malware behavior using machine learning
Konrad Rieck, Philipp Trinius, Carsten Willems, and Thorsten Holz. 2011 · 2011
Earlier work this paper cites.
Smashing the gadgets: Hindering return-oriented programming using in-place code randomization. In 2012 IEEE Symposium on Security and Privacy . IEEE, 601–615
Vasilis Pappas, Michalis Polychronakis, and Angelos D Keromytis. 2012 · 2012
Earlier work this paper cites.
Large-scale malware classification using random projections and neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing . IEEE, 3422–3426
George E Dahl, Jack W Stokes, Li Deng, and Dong Yu. 2013 · 2013
Earlier work this paper cites.
Stealth attacks: An extended insight into the obfuscation effects on android malware
Davide Maiorca, Davide Ariu, Igino Corona, Marco Aresu, and Giorgio Giacinto. 2015 · 2015
Earlier work this paper cites.
Deep neural network based malware detection using two dimensional binary program features. In 2015 10th International Conference on Malicious and Unwanted Software (MALWARE) . IEEE, 11–20
Joshua Saxe and Konstantin Berlin. 2015 · 2015
Earlier work this paper cites.
malWASH: Washing Malware to Evade Dynamic Analysis. In 10th USENIX Workshop on Offensive Technologies (WOOT 16) . USENIX Association, Austin, TX
Kyriakos K. Ispoglou and Mathias Payer. 2016 · 2016
Earlier work this paper cites.
Automatically evading classifiers. In Proceedings of the 2016 network and distributed systems symposium . 21–24
Weilin Xu, Yanjun Qi, and David Evans. 2016 · 2016
Earlier work this paper cites.
Adversarial machine learning in malware detection: Arms race between evasion attack and defense. In 2017 European Intelligence and Security Informatics Conference (EISIC) . IEEE, 99–106
Lingwei Chen, Yanfang Ye, and Thirimachos Bourlai. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Generating adversarial malware examples for black-box attacks based on GAN
Weiwei Hu and Ying Tan. 2017a · 2017
Earlier work this paper cites.
Generating adversarial malware examples for black-box attacks based on GAN
Weiwei Hu and Ying Tan. 2017b · 2017
Earlier work this paper cites.
Android malware detection using deep learning on api method sequences
ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, and Djedjiga Mouheb. 2017 · 2017
Earlier work this paper cites.
Adversarial Detection of Flash Malware: Limitations and Open Issues
Davide Maiorca, Battista Biggio, Maria Elena Chiappe, and Giorgio Giacinto. 2017 · 2017
Earlier work this paper cites.
Practical black-box attacks against machine learning. In Proceedings of the 2017 ACM on Asia conference on computer and communications security . ACM, 506–519
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami. 2017 · 2017
Earlier work this paper cites.
Attack and defense of dynamic analysis-based, adversarial neural malware classification models
Jack W Stokes, De Wang, Mady Marinescu, Marc Marino, and Brian Bussone. 2017 · 2017
Earlier work this paper cites.
Malware detection in adversarial settings: Exploiting feature evolutions and confusions in android apps. In Proceedings of the 33rd Annual Computer Security Applications Conference . 288–302
Wei Yang, Deguang Kong, Tao Xie, and Carl A Gunter. 2017 · 2017
Earlier work this paper cites.
Adversarial deep learning for robust detection of binary encoded malware. In 2018 IEEE Security and Privacy Workshops (SPW) . IEEE, 76–82
Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, and Una-May O’Reilly. 2018 · 2018
Earlier work this paper cites.
Learning to evade static PE machine learning malware models via reinforcement learning
Hyrum S Anderson, Anant Kharkar, Bobby Filar, David Evans, and Phil Roth. 2018 · 2018
Earlier work this paper cites.
Ember: an open dataset for training static PE malware machine learning models
Hyrum S Anderson and Phil Roth. 2018 · 2018
Cited alongside, same era.
AI & Machine Learning
Avast 2018 · 2018
Cited alongside, same era.
Static malware detection & subterfuge: Quantifying the robustness of machine learning and current anti-virus. In 2018 13th International Conference on Malicious and Unwanted Software (MALWARE) . IEEE, 1–10
William Fleshman, Edward Raff, Richard Zak, Mark McLean, and Charles Nicholas. 2018 · 2018
Cited alongside, same era.
Black-box attacks against RNN based malware detection algorithms. In Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence
Weiwei Hu and Ying Tan. 2018 · 2018
Cited alongside, same era.
Li Chen. 2019 · 2019
Later among the works it cites.
Generation & Evaluation of Adversarial Examples for Malware Obfuscation
Park Daniel, Khan Haidar, and Yener Bülent. 2019 · 2019
Later among the works it cites.
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries
Luca Demetrio, Battista Biggio, Giovanni Lagorio, Fabio Roli, and Alessandro Armando. 2019 · 2019
Later among the works it cites.
Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks. In 28th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 19) . 321–338
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski, Battista Biggio, Alina Oprea, Cristina Nita-Rotaru, and Fabio Roli. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alex Huang, Abdullah Al-Dujaili, Erik Hemberg, and Una-May O’Reilly. 2018 · 2018
Cited alongside, same era.
Adversarial malware binaries: Evading deep learning for malware detection in executables. In 2018 26th European Signal Processing Conference (EUSIPCO) . IEEE, 533–537
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, and Fabio Roli. 2018 · 2018
Cited alongside, same era.
Alex Kouzemtchenko. 2018 · 2018
Cited alongside, same era.
Adversarial examples on discrete sequences for beating whole-binary malware detection
Felix Kreuk, Assi Barak, Shir Aviv-Reuven, Moran Baruch, Benny Pinkas, and Joseph Keshet. 2018a · 2018
Cited alongside, same era.
Deceiving end-to-end deep learning malware detectors using adversarial examples
Felix Kreuk, Assi Barak, Shir Aviv-Reuven, Moran Baruch, Benny Pinkas, and Joseph Keshet. 2018b · 2018
Cited alongside, same era.
Deqiang Li, Ramesh Baral, Tao Li, Han Wang, Qianmu Li, and Shouhuai Xu. 2018a · 2018
Cited alongside, same era.
Deqiang Li, Qianmu Li, Yanfang Ye, and Shouhuai Xu. 2018b · 2018
Cited alongside, same era.
Towards Robust Detection of Adversarial Infection Vectors: Lessons Learned in PDF Malware
Davide Maiorca, Battista Biggio, and Giorgio Giacinto. 2018 · 2018
Cited alongside, same era.
Saeed Ehteshamifar, Antonio Barresi, Thomas R Gross, and Michael Pradel. 2019 · 2019
Later among the works it cites.
Malware Evasion Attack and Defense
Yonghong Huang, Utkarsh Verma, Celeste Fralick, Gabriel Infantec-Lopez, Brajesh Kumar, and Carl Woodward. 2019 · 2019
Later among the works it cites.
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection
Aminollah Khormali, Ahmed Abusnaina, Songqing Chen, DaeHun Nyang, and Aziz Mohaisen. 2019 · 2019
Later among the works it cites.
Adversarial Samples on Android Malware Detection Systems for IoT Systems
Xiaolei Liu, Xiaojiang Du, Xiaosong Zhang, Qingxin Zhu, Hao Wang, and Mohsen Guizani. 2019a · 2019
Later among the works it cites.
Atmpa: Attacking machine learning-based malware visualization detection methods via adversarial examples. In 2019 IEEE/ACM 27th International Symposium on Quality of Service (IWQoS) . IEEE, 1–10
Xinbo Liu, Jiliang Zhang, Yaping Lin, and He Li. 2019b · 2019
Later among the works it cites.
Machine Learning Static Evasion Competition 2019
MLSEC2019 [n.d.] · 2019
Later among the works it cites.
Short Paper: Creating Adversarial Malware Examples using Code Insertion
Daniel Park, Haidar Khan, and Bülent Yener. 2019 · 2019
Later among the works it cites.
D-TIME: Distributed Threadless Independent Malware Execution for Runtime Obfuscation. In 13th USENIX Workshop on Offensive Technologies (WOOT 19) . USENIX Association, Santa Clara, CA
Jithin Pavithran, Milan Patnaik, and Chester Rebeiro. 2019 · 2019
Later among the works it cites.
Effectiveness of Adversarial Examples and Defenses for Malware Classification
Robert Podschwadt and Hassan Takabi. 2019 · 2019
Later among the works it cites.
Misleading authorship attribution of source code using adversarial learning. In 28th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 19) . 479–496
Erwin Quiring, Alwin Maier, and Konrad Rieck. 2019 · 2019
Later among the works it cites.
Exploring adversarial examples in malware detection. In 2019 IEEE Security and Privacy Workshops (SPW) . IEEE, 8–14
Octavian Suciu, Scott E Coull, and Jeffrey Johns. 2019 · 2019
Later among the works it cites.
On Defending Against Label Flipping Attacks on Malware Detection Systems
Rahim Taheri, Reza Javidan, Mohammad Shojafar, Zahra Pooranian, Ali Miri, and Mauro Conti. 2019 · 2019
Later among the works it cites.
New machine learning model sifts through the good to unearth the bad in evasive malware
Microsoft Defender ATP Research Team. 2019 · 2019
Later among the works it cites.
Functionality-preserving Black-box Optimization of Adversarial Windows Malware
Luca Demetrio, B. Biggio, Giovanni Lagorio, F. Roli, and A. Armando. 2020 · 2020
Closest in time.
The best antivirus protection
pcmag 2020 · 2020
Closest in time.
Intriguing Properties of Adversarial ML Attacks in the Problem Space
Fabio Pierazzi, Feargus Pendlebury, Jacopo Cortellazzi, and Lorenzo Cavallaro. 2020 · 2020
Closest in time.