Awesome Adversarial ML

Key papers

• Backdoor Learning: A Survey (2020)
  Yiming Li, Yong Jiang, Zhifeng Li, et al.
  25.77
• Evasion Attacks Against Machine Learning At Test Time (2017)
  Battista Biggio, Igino Corona, Davide Maiorca, et al.
  22.04
• Textbugger: Generating Adversarial Text Against Real-world Applications (2018)
  Jinfeng Li, Shouling Ji, Tianyu Du, et al.
  18.70
• PRADA: Protecting Against DNN Model Stealing Attacks (2018)
  Mika Juuti, Sebastian Szyller, Samuel Marchal, et al.
  18.53
• Adversarial Malware Binaries: Evading Deep Learning For Malware Detection In Executables (2018)
  Bojan Kolosnjaji, Ambra Demontis, Battista Biggio, et al.
  17.71
• Adversarial Machine Learning Attacks And Defense Methods In The Cyber Security Domain (2020)
  Ihai Rosenberg, Asaf Shabtai, Yuval Elovici, et al.
  17.66
• Automated Poisoning Attacks And Defenses In Malware Detection Systems: An Adversarial Machine Learning Approach (2017)
  Sen Chen, Minhui Xue, Lingling Fan, et al.
  17.14
• Adversarial Machine Learning In Network Intrusion Detection Systems (2020)
  Elie Alhajjar, Paul Maxwell, Nathaniel D. Bastian
  17.04
• Adversarial Feature Selection Against Evasion Attacks (2020)
  Fei Zhang, Patrick P. K. Chan, Battista Biggio, et al.
  16.84
• IDSGAN: Generative Adversarial Networks For Attack Generation Against Intrusion Detection (2018)
  Zilong Lin, Yong Shi, Zhi Xue
  16.79
• Privacy Risks Of Securing Machine Learning Models Against Adversarial Examples (2019)
  Liwei Song, Reza Shokri, Prateek Mittal
  16.71
• Evaluating And Improving Adversarial Robustness Of Machine Learning-based Network Intrusion Detectors (2020)
  Dongqi Han, Zhiliang Wang, Ying Zhong, et al.
  16.65
• Support Vector Machines Under Adversarial Label Contamination (2022)
  Huang Xiao, Battista Biggio, Blaine Nelson, et al.
  16.53
• Deepsweep: An Evaluation Framework For Mitigating DNN Backdoor Attacks Using Data Augmentation (2020)
  Han Qiu, Yi Zeng, Shangwei Guo, et al.
  16.51
• Deep K-nn Defense Against Clean-label Data Poisoning Attacks (2019)
  Neehar Peri, Neal Gupta, W. Ronny Huang, et al.
  16.44
• Generating Adversarial Malware Examples For Black-box Attacks Based On GAN (2017)
  Weiwei Hu, Ying Tan
  16.03
• Enhanced Membership Inference Attacks Against Machine Learning Models (2021)
  Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, et al.
  15.78
• Modeling Realistic Adversarial Attacks Against Network Intrusion Detection Systems (2021)
  Giovanni Apruzzese, Mauro Andreolini, Luca Ferretti, et al.
  15.73
• A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models (2019)
  Heng Chang, Yu Rong, Tingyang Xu, et al.
  15.16
• Robust Watermarking Of Neural Network With Exponential Weighting (2019)
  Ryota Namba, Jun Sakuma
  15.10
• Hidden Killer: Invisible Textual Backdoor Attacks With Syntactic Trigger (2021)
  Fanchao Qi, Mukai Li, Yangyi Chen, et al.
  15.00
• Poison Ink: Robust And Invisible Backdoor Attack (2021)
  Jie Zhang, Dongdong Chen, Qidong Huang, et al.
  14.90
• Adversarial Attack On Large Scale Graph (2020)
  Jintang Li, Tao Xie, Liang Chen, et al.
  14.77
• Mockingbird: Defending Against Deep-learning-based Website Fingerprinting Attacks With Adversarial Traces (2019)
  Mohammad Saidur Rahman, Mohsen Imani, Nate Mathews, et al.
  14.73
• Learning The Associations Of MITRE ATT&CK Adversarial Techniques (2020)
  Rawan Al-Shaer, Jonathan M. Spring, Eliana Christou
  14.69
• Adversary-resilient Distributed And Decentralized Statistical Inference And Machine Learning: An Overview Of Recent Advances Under The Byzantine Threat Model (2019)
  Zhixiong Yang, Arpita Gang, Waheed U. Bajwa
  14.35
• Evading Classifiers By Morphing In The Dark (2017)
  Hung Dang, Yue Huang, Ee-Chien Chang
  14.19
• Towards Adversarial Malware Detection: Lessons Learned From Pdf-based Attacks (2018)
  Davide Maiorca, Battista Biggio, Giorgio Giacinto
  14.11
• Backdoor Defense With Machine Unlearning (2022)
  Yang Liu, Mingyuan Fan, Cen Chen, et al.
  14.06
• How To Backdoor Diffusion Models? (2022)
  Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho
  13.84
• Backdoor Attacks On Pre-trained Models By Layerwise Weight Poisoning (2021)
  Linyang Li, Demin Song, Xiaonan Li, et al.
  13.74
• Poisoning Web-scale Training Datasets Is Practical (2023)
  Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, et al.
  13.50
• "real Attackers Don't Compute Gradients": Bridging The Gap Between Adversarial ML Research And Practice (2022)
  Giovanni Apruzzese, Hyrum S. Anderson, Savino Dambra, et al.
  13.28
• A Framework For Enhancing Deep Neural Networks Against Adversarial Malware (2020)
  Deqiang Li, Qianmu Li, Yanfang Ye, et al.
  13.28
• A Simple Framework To Enhance The Adversarial Robustness Of Deep Learning-based Intrusion Detection System (2023)
  Xinwei Yuan, Shu Han, Wei Huang, et al.
  13.28
• Membership Inference Attacks And Defenses In Classification Models (2020)
  Jiacheng Li, Ninghui Li, Bruno Ribeiro
  13.23
• Black-box Attacks On Sequential Recommenders Via Data-free Model Extraction (2021)
  Zhenrui Yue, Zhankui He, Huimin Zeng, et al.
  13.17
• Charbot: A Simple And Effective Method For Evading DGA Classifiers (2019)
  Jonathan Peck, Claire Nie, Raaghavi Sivaguru, et al.
  12.99
• Handling Adversarial Concept Drift In Streaming Data (2018)
  Tegjyot Singh Sethi, Mehmed Kantardzic
  12.81
• Enhancing Network Intrusion Detection Performance Using Generative Adversarial Networks (2024)
  Xinxing Zhao, Kar Wai Fok, Vrizlynn L. L. Thing
  12.81
• Evaluation Of Adversarial Training On Different Types Of Neural Networks In Deep Learning-based Idss (2020)
  Rana Abou Khamis, Ashraf Matrawy
  12.54
• Enhancing Fine-tuning Based Backdoor Defense With Sharpness-aware Minimization (2023)
  Mingli Zhu, Shaokui Wei, Li Shen, et al.
  12.33
• R-htdetector: Robust Hardware-trojan Detection Based On Adversarial Training (2022)
  Kento Hasegawa, Seira Hidano, Kohei Nozawa, et al.
  12.10
• RAP: Robustness-aware Perturbations For Defending Against Backdoor Attacks On NLP Models (2021)
  Wenkai Yang, Yankai Lin, Peng Li, et al.
  12.10
• Generalizable Black-box Adversarial Attack With Meta Learning (2023)
  Fei Yin, Yong Zhang, Baoyuan Wu, et al.
  12.10
• Deep Neural Rejection Against Adversarial Examples (2019)
  Angelo Sotgiu, Ambra Demontis, Marco Melis, et al.
  12.02
• Adaptative Perturbation Patterns: Realistic Adversarial Learning For Robust Intrusion Detection (2022)
  João Vitorino, Nuno Oliveira, Isabel Praça
  12.02
• Query-efficient Black-box Attack Against Sequence-based Malware Classifiers (2018)
  Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, et al.
  11.93
• Prompt As Triggers For Backdoor Attack: Examining The Vulnerability In Language Models (2023)
  Shuai Zhao, Jinming Wen, Luu Anh Tuan, et al.
  11.85
• Backdoor Attack With Sparse And Invisible Trigger (2023)
  Yinghua Gao, Yiming Li, Xueluan Gong, et al.
  11.58
• Data Driven Exploratory Attacks On Black Box Classifiers In Adversarial Domains (2017)
  Tegjyot Singh Sethi, Mehmed Kantardzic
  11.58
• A Black-box Adversarial Attack For Poisoning Clustering (2020)
  Antonio Emanuele Cinà, Alessandro Torcinovich, Marcello Pelillo
  11.29
• Can Machine Learning Model With Static Features Be Fooled: An Adversarial Machine Learning Approach (2019)
  Rahim Taheri, Reza Javidan, Mohammad Shojafar, et al.
  11.29
• Synthetic Flow-based Cryptomining Attack Generation Through Generative Adversarial Networks (2021)
  Alberto Mozo, Ángel González-Prieto, Antonio Pastor, et al.
  11.08
• Trojanpuzzle: Covertly Poisoning Code-suggestion Models (2023)
  Hojjat Aghakhani, Wei Dai, Andre Manoel, et al.
  10.97
• Statistical Detection Of Adversarial Examples In Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems (2022)
  Ibrahim Aliyu, Selinde van Engelenburg, Muhammed Bashir Muazu, et al.
  10.97
• A Survey On Adversarial Attacks For Malware Analysis (2021)
  Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam
  10.97
• Man-in-the-middle Attacks Against Machine Learning Classifiers Via Malicious Generative Models (2019)
  Derui, Wang, Chaoran Li, et al.
  10.85
• PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks (2023)
  Deqiang Li, Shicheng Cui, Yun Li, et al.
  10.74
• Rethinking Graph Backdoor Attacks: A Distribution-preserving Perspective (2024)
  Zhiwei Zhang, Minhua Lin, Enyan Dai, et al.
  10.48