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Expand Up @@ -36,11 +36,11 @@ For the categorization and more details, please refer to our survey paper [**Adv
3. **Black-Box Attacks against RNN based Malware Detection Algorithms**. *Weiwei Hu, Ying Tan*. AAAI Workshops 2018. `Black-box` [[pdf](https://www.aaai.org/ocs/index.php/WS/AAAIW18/paper/viewPaper/16594)]
4. **Enhancing Machine Learning based Malware Detection Model by Reinforcement Learning**. *Cangshuai Wu, Jiangyong Shi, Yuexiang Yang, Wenhua Li*. International Conference on Communication and Network Security 2018. `Black-box` [[pdf](https://dl.acm.org/doi/pdf/10.1145/3290480.3290494)]
5. **Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-virus**. *William Fleshman, Edward Raff, Richard Zak, Mark McLean, Charles Nicholas*. International Conference on Malicious and Unwanted Software (MALWARE) 2018. `Black-box` [[pdf](https://ieeexplore.ieee.org/abstract/document/8659360)]
6. **Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables**. *Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, Fabio Roli*. European Signal Processing Conference 2018. `White-box` [[pdf](https://ieeexplore.ieee.org/abstract/document/8553214)]
7. **Exploring Adversarial Examples in Malware Detection**. *Octavian Suciu, Scott E. Coull, Jeffrey Johns*. Arxiv 2018. `White-box` [[pdf](https://arxiv.org/pdf/1810.08280.pdf)]
6. **Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables**. *Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, Davide Maiorca, Giorgio Giacinto, Claudia Eckert, Fabio Roli*. European Signal Processing Conference 2018. `White-box` [[pdf](https://ieeexplore.ieee.org/abstract/document/8553214)] [[code](https://github.com/yuxiaorun/malconv-adversarial)]
7. **Exploring Adversarial Examples in Malware Detection**. *Octavian Suciu, Scott E. Coull, Jeffrey Johns*. 2019 IEEE Security and Privacy Workshops (SPW). `White-box` [[pdf](https://arxiv.org/pdf/1810.08280.pdf)]
8. **Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples**. *Felix Kreuk, Assi Barak, Shir Aviv, Moran Baruch, Benny Pinkas, Joseph Keshet*. Arxiv 2018. `White-box` [[pdf](https://arxiv.org/pdf/1802.04528.pdf)]
9. **Adversarial Deep Learning for Robust Detection of Binary Encoded Malware**. *Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, Una-May O’Reilly*. IEEE Security and Privacy
Workshops 2018. `White-box` [[pdf](https://arxiv.org/pdf/1801.02950.pdf)][[code](https://github.com/ALFA-group/robust-adv-malware-detection)]
Workshops 2018. `White-box` [[pdf](https://arxiv.org/pdf/1801.02950.pdf)] [[code](https://github.com/ALFA-group/robust-adv-malware-detection)]

### 2019:

Expand All @@ -51,9 +51,9 @@ For the categorization and more details, please refer to our survey paper [**Adv
5. **Improved MalGAN: Avoiding Malware Detector by Leaning Cleanware Features**. *Masataka Kawai, Kaoru Ota, Mianxing Dong*. International Conference on Artificial Intelligence in Information and Communication 2019. `Black-box` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8669079)]
6. **Evading API Call Sequence Based Malware Classifiers**. *FenilFadadu, AnandHanda, NiteshKumar SandeepKumarShukla*. The 21st International Conference on Information and Communications Security 2019. `Black-box` [[pdf](https://link.springer.com/chapter/10.1007/978-3-030-41579-2_2)]
7. **Shallow Security: on the Creation of Adversarial Variants to Evade Machine Learning-Based Malware Detectors**. *Fabricio Ceschin, Marcus Botacin, Heitor Murilo Gomes, L. S. Oliveira, A. Grégio*. Reversing and Offensive-Oriented Trends Symposium (ROOTS) 2019. `Black-box` [[pdf](https://github.com/marcusbotacin/Dropper/tree/master/paper)] [[code](https://github.com/marcusbotacin/Dropper)]
8. **Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes**. *Keane Lucas, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, Saurabh Shintre*. Arxiv 2019. `Black-box and White-box` [[pdf](https://arxiv.org/pdf/1912.09064.pdf)] [[code](https://github.com/pwwl/enhanced-binary-diversification)]
8. **Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes**. *Keane Lucas, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, Saurabh Shintre*. Proceedings of the 2021 ACM Asia Conference on Computer and Communications Security. `Black-box and White-box` [[pdf](https://arxiv.org/pdf/1912.09064.pdf)] [[code](https://github.com/pwwl/enhanced-binary-diversification)]
9. **Adversarial Examples for CNN-Based Malware Detectors**. *Bingcai Chen, Zhongru Ren, Chao Yu, Iftikhar Hussain, Jintao Liu*. IEEE Access 2019. `Black-box and White-box` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8703786&tag=1)]
10. **Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries**. *Luca Demetrio, Battista Biggio, Giovanni Lagorio, Fabio Roli, Alessandro Armando*. Arxiv 2019. `White-box` [[pdf](https://arxiv.org/pdf/1901.03583.pdf)]
10. **Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries**. *Luca Demetrio, Battista Biggio, Giovanni Lagorio, Fabio Roli, Alessandro Armando*. Arxiv 2019. `White-box` [[pdf](https://arxiv.org/pdf/1901.03583.pdf)] [[code](https://github.com/pralab/secml_malware)]
11. **COPYCAT: Practical Adversarial Attacks on Visualization-based Malware Detection**. *Aminollah Khormali, Ahmed Abusnaina, Songqing Chen, DaeHun Nyang, Aziz Mohaisen*. Arxiv 2019. `Black-box and White-box` [[pdf](https://arxiv.org/pdf/1909.09735.pdf)]
12. **Generation & Evaluation of Adversarial Examples for Malware Obfuscation**. *Daniel Park, Haidar Khan, Bulent Yener*. IEEE International Conference On Machine Learning And Applications 2019. `Black-box and White-box` [[pdf](https://ieeexplore.ieee.org/abstract/document/8999277)]

Expand All @@ -62,7 +62,7 @@ For the categorization and more details, please refer to our survey paper [**Adv
1. **Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers**. *Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach*. ACSAC 2020: Annual Computer Security Applications Conference. `Black-box` [[pdf](https://dl.acm.org/doi/pdf/10.1145/3427228.3427230)]
2. **MalFox: Camouflaged Adversarial Malware Example Generation Based on C-GANs Against Black-Box Detectors**. *Fangtian Zhong, Xiuzhen Cheng, Dongxiao Yu, Bei Gong, Shuaiwen Song, Jiguo Yu*. Arxiv 2020. `Black-box` [[pdf](https://arxiv.org/pdf/2011.01509.pdf)]
3. **Generating Adversarial Examples for Static PE Malware Detector Based on Deep Reinforcement Learning**. *Jun Chen, Jingfei Jiang, Rongchun Li, Yong Dou*. Journal of Physics: Conference Series 2020. `Black-box` [[pdf](https://iopscience.iop.org/article/10.1088/1742-6596/1575/1/012011/pdf)]
4. **Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection**. *Lan Zhang, Peng Liu, Yoon-Ho Choi*. Arxiv 2020. `Black-box` [[pdf](https://arxiv.org/pdf/2009.05602.pdf)]
4. **Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection**. *Lan Zhang, Peng Liu, Yoon-Ho Choi*. IEEE Transactions on Dependable and Secure Computing. `Black-box` [[pdf](https://arxiv.org/pdf/2009.05602.pdf)]
5. **Black-box Adversarial Attacks Against Deep Learning Based Malware Binaries Detection with GAN**. *Junkun Yuan, Shaofang Zhou, Lanfen Lin, Feng Wang, Jia Cui*. European Conference on Artificial Intelligence 2020. `Black-box` [[pdf](http://ecai2020.eu/papers/1118_paper.pdf)]
6. **MDEA: Malware Detection with Evolutionary Adversarial Learning**. *Xiruo Wang, Risto Miikkulainen*. IEEE Congress on Evolutionary Computation 2020. `Black-box` [[pdf](https://ieeexplore.ieee.org/abstract/document/9185810)]
7. **MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers**. *Wei Song, Xuezixiang Li, Sadia Afroz, Deepali Garg, Dmitry Kuznetsov, Heng Yin*. Arxiv 2020. `Black-box` [[pdf](https://arxiv.org/pdf/2003.03100.pdf)] [[code](https://github.com/bitsecurerlab/adversarial_malware.git)]
Expand All @@ -82,7 +82,7 @@ For the categorization and more details, please refer to our survey paper [**Adv

## 3. Defense Papers [[Back to Top:point_up:](#awesome-resources-for-adversarial-attacks-and-defenses-for-windows-pe-malware-detection)]

1. **Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification**. *Erwin Quiring, Lukas Pirch, Michael Reimsbach, Daniel Arp, Konrad Rieck*. Arxiv 2020. [[pdf](https://arxiv.org/abs/2010.09569)]
1. **Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification**. *Erwin Quiring, Lukas Pirch, Michael Reimsbach, Daniel Arp, Konrad Rieck*. Arxiv 2020. [[pdf](https://arxiv.org/abs/2010.09569)] [[code](https://github.com/EQuiw/2020-evasion-competition)]
2. **Soteria: Detecting Adversarial Examples in Control Flow Graph-based Malware Classifiers**. *Hisham Alasmary, Ahmed Abusnaina, Rhongho Jang, Mohammed Abuhamad, Afsah Anwar, DaeHun Nyang, David Mohaisen*. IEEE International Conference on Distributed Computing Systems (ICDCS) 2020. [[pdf](http://seal.cs.ucf.edu/doc/icdcs20aml.pdf)]

## 4. Other Papers [[Back to Top:point_up:](#awesome-resources-for-adversarial-attacks-and-defenses-for-windows-pe-malware-detection)]
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