From f583b683a176164076586d91a23682c5e68e2dc3 Mon Sep 17 00:00:00 2001 From: acbcfjh <85011269+acbcfjh@users.noreply.github.com> Date: Thu, 1 Sep 2022 21:03:12 +0800 Subject: [PATCH 1/2] Update README.md update published papers and add code that can be found --- README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 04ae399..3f31c71 100644 --- a/README.md +++ b/README.md @@ -33,14 +33,14 @@ For the categorization and more details, please refer to our survey paper [**Adv 1. **Generic Black-Box End-to-End Attack against State of the Art API Call based Malware Classifiers**. *Ishai Rosenberg, Asaf Shabtai, Lior Rokach, Yuval Elovici*. RAID 2018. `Black-box` [[pdf](https://link.springer.com/chapter/10.1007/978-3-030-00470-5_23)] 2. **Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning**. *Hyrum S. Anderson, Anant Kharkar, Bobby Filar, David Evans, Phil Roth*. Arxiv 2018. `Black-box` [[pdf](https://arxiv.org/pdf/1801.08917.pdf)] [[code](https://github.com/endgameinc/gym-malware)] -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)] +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)] [[code](https://github.com/benjamin347/advnlp)] 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)] +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: @@ -51,18 +51,18 @@ 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/zangobot/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)] +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)] [[code](https://github.com/zhengliz/natural-adversary)] ### 2020: 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)] [[code](https://github.com/kevinkoo001/ropf)] 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)] @@ -73,7 +73,7 @@ For the categorization and more details, please refer to our survey paper [**Adv ### 2021: -1. **An IRL-based Malware Adversarial Generation Method to Evade Anti-malware Engines**. *Xintong Li, Qi Li*. Computers & Security 2021. `White-box` [[pdf](https://www.sciencedirect.com/science/article/pii/S0167404820303916)] +1. **An Adversarial Machine Learning Method based on OpCode N-gramare Engines**. *Xintong Li, Qi Li*. Computers & Security 2021. `White-box` [[pdf](https://www.sciencedirect.com/science/article/pii/S0167404820303916)] 2. **Binary Black-Box Attacks against Static Malware Detectors with Reinforcement Learning in Discrete Action Spaces**. *Mohammadreza Ebrahimi, Jason Pacheco, Weifeng Li, James Lee Hu*. IEEE Security and Privacy Workshops 2021. `Black-box` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9474314)] 3. **Improving Adversarial Attacks against Executable Raw Byte Classifiers**. *Justin Burr, Shengjie Xu*. IEEE INFOCOM Poster 2021. `White-box` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9484612)] 4. **AIMED-RL: Exploring Adversarial Malware Examples with Reinforcement Learning**. *Labaca-Castro, Raphael, Sebastian Franz, Gabi Dreo Rodosek*. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD) 2021. `Black-box` [[pdf](https://link.springer.com/chapter/10.1007/978-3-030-86514-6_3)] [[code](https://github.com/zRapha/AIMED)] @@ -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)] From bbab02fdede6851e8a6af10b86262454c9eaf63f Mon Sep 17 00:00:00 2001 From: acbcfjh <85011269+acbcfjh@users.noreply.github.com> Date: Fri, 2 Sep 2022 19:43:12 +0800 Subject: [PATCH 2/2] Update README.md update the published papers and public codes --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 3f31c71..ddba30c 100644 --- a/README.md +++ b/README.md @@ -33,10 +33,10 @@ For the categorization and more details, please refer to our survey paper [**Adv 1. **Generic Black-Box End-to-End Attack against State of the Art API Call based Malware Classifiers**. *Ishai Rosenberg, Asaf Shabtai, Lior Rokach, Yuval Elovici*. RAID 2018. `Black-box` [[pdf](https://link.springer.com/chapter/10.1007/978-3-030-00470-5_23)] 2. **Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning**. *Hyrum S. Anderson, Anant Kharkar, Bobby Filar, David Evans, Phil Roth*. Arxiv 2018. `Black-box` [[pdf](https://arxiv.org/pdf/1801.08917.pdf)] [[code](https://github.com/endgameinc/gym-malware)] -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)] [[code](https://github.com/benjamin347/advnlp)] +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)] +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 @@ -53,16 +53,16 @@ For the categorization and more details, please refer to our survey paper [**Adv 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*. 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)] [[code](https://github.com/zangobot/secml_malware)] +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)] [[code](https://github.com/zhengliz/natural-adversary)] +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)] ### 2020: 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*. IEEE Transactions on Dependable and Secure Computing. `Black-box` [[pdf](https://arxiv.org/pdf/2009.05602.pdf)] [[code](https://github.com/kevinkoo001/ropf)] +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)] @@ -73,7 +73,7 @@ For the categorization and more details, please refer to our survey paper [**Adv ### 2021: -1. **An Adversarial Machine Learning Method based on OpCode N-gramare Engines**. *Xintong Li, Qi Li*. Computers & Security 2021. `White-box` [[pdf](https://www.sciencedirect.com/science/article/pii/S0167404820303916)] +1. **An IRL-based Malware Adversarial Generation Method to Evade Anti-malware Engines**. *Xintong Li, Qi Li*. Computers & Security 2021. `White-box` [[pdf](https://www.sciencedirect.com/science/article/pii/S0167404820303916)] 2. **Binary Black-Box Attacks against Static Malware Detectors with Reinforcement Learning in Discrete Action Spaces**. *Mohammadreza Ebrahimi, Jason Pacheco, Weifeng Li, James Lee Hu*. IEEE Security and Privacy Workshops 2021. `Black-box` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9474314)] 3. **Improving Adversarial Attacks against Executable Raw Byte Classifiers**. *Justin Burr, Shengjie Xu*. IEEE INFOCOM Poster 2021. `White-box` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9484612)] 4. **AIMED-RL: Exploring Adversarial Malware Examples with Reinforcement Learning**. *Labaca-Castro, Raphael, Sebastian Franz, Gabi Dreo Rodosek*. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD) 2021. `Black-box` [[pdf](https://link.springer.com/chapter/10.1007/978-3-030-86514-6_3)] [[code](https://github.com/zRapha/AIMED)]