Key papers

• Adversarial Machine Learning Attacks And Defense Methods In The Cyber Security Domain (2020)
  Ihai Rosenberg, Asaf Shabtai, Yuval Elovici, et al.
  17.66
• Adversarial Feature Selection Against Evasion Attacks (2020)
  Fei Zhang, Patrick P. K. Chan, Battista Biggio, et al.
  16.84
• Privacy Risks Of Securing Machine Learning Models Against Adversarial Examples (2019)
  Liwei Song, Reza Shokri, Prateek Mittal
  16.71
• Support Vector Machines Under Adversarial Label Contamination (2022)
  Huang Xiao, Battista Biggio, Blaine Nelson, et al.
  16.53
• 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
• A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models (2019)
  Heng Chang, Yu Rong, Tingyang Xu, et al.
  15.16
• Hidden Killer: Invisible Textual Backdoor Attacks With Syntactic Trigger (2021)
  Fanchao Qi, Mukai Li, Yangyi Chen, et al.
  15.00
• Adversarial Attack On Large Scale Graph (2020)
  Jintang Li, Tao Xie, Liang Chen, et al.
  14.77
• How To Backdoor Diffusion Models? (2022)
  Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho
  13.84
• 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
• 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
• 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
• 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
• Rethinking Graph Backdoor Attacks: A Distribution-preserving Perspective (2024)
  Zhiwei Zhang, Minhua Lin, Enyan Dai, et al.
  10.48
• Universal Detection Of Backdoor Attacks Via Density-based Clustering And Centroids Analysis (2023)
  Wei Guo, Benedetta Tondi, Mauro Barni
  10.35
• An Improved Genetic Algorithm And Its Application In Neural Network Adversarial Attack (2021)
  Dingming Yang, Zeyu Yu, Hongqiang Yuan, et al.
  10.35
• Backdoor Cleansing With Unlabeled Data (2022)
  Lu Pang, Tao Sun, Haibin Ling, et al.
  10.21
• Treant: Training Evasion-aware Decision Trees (2019)
  Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei, et al.
  10.07
• GANI: Global Attacks On Graph Neural Networks Via Imperceptible Node Injections (2022)
  Junyuan Fang, Haixian Wen, Jiajing Wu, et al.
  10.07
• Active Deep Learning Attacks Under Strict Rate Limitations For Online API Calls (2018)
  Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu, et al.
  10.07
• Reverse Engineering Imperceptible Backdoor Attacks On Deep Neural Networks For Detection And Training Set Cleansing (2020)
  Zhen Xiang, David J. Miller, George Kesidis
  10.07
• Generative Adversarial Networks For Black-box API Attacks With Limited Training Data (2019)
  Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu, et al.
  9.92
• Advmind: Inferring Adversary Intent Of Black-box Attacks (2020)
  Ren Pang, Xinyang Zhang, Shouling Ji, et al.
  9.76
• Adversarial Robustness Of Neural-statistical Features In Detection Of Generative Transformers (2022)
  Evan Crothers, Nathalie Japkowicz, Herna Viktor, et al.
  9.59
• Robustifying Models Against Adversarial Attacks By Langevin Dynamics (2018)
  Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller, et al.
  9.59
• Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey (2025)
  Wencheng Yang et al.
  9.44
• Malprotect: Stateful Defense Against Adversarial Query Attacks In Ml-based Malware Detection (2023)
  Aqib Rashid, Jose Such
  9.23
• A Robust Adversary Detection-deactivation Method For Metaverse-oriented Collaborative Deep Learning (2023)
  Pengfei Li, Zhibo Zhang, Ameena S. Al-Sumaiti, et al.
  9.03
• Adversarial Learning In Real-world Fraud Detection: Challenges And Perspectives (2023)
  Danele Lunghi, Alkis Simitsis, Olivier Caelen, et al.
  9.03
• Adversarial Robustness For Tabular Data Through Cost And Utility Awareness (2022)
  Klim Kireev, Bogdan Kulynych, Carmela Troncoso
  8.82
• Influence Based Defense Against Data Poisoning Attacks In Online Learning (2021)
  Sanjay Seetharaman, Shubham Malaviya, Rosni Kv, et al.
  8.82
• Backdooring Neural Code Search (2023)
  Weisong Sun, Yuchen Chen, Guanhong Tao, et al.
  8.82
• Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks (2026)
  MD Azizul Hakim et al.
  8.80
• Adiotack: Quantifying And Refining Resilience Of Decision Tree Ensemble Inference Models Against Adversarial Volumetric Attacks On Iot Networks (2022)
  Arman Pashamokhtari, Gustavo Batista, Hassan Habibi Gharakheili
  8.60
• Quarantine: Sparsity Can Uncover The Trojan Attack Trigger For Free (2022)
  Tianlong Chen, Zhenyu Zhang, Yihua Zhang, et al.
  8.35
• Putting Words Into The System's Mouth: A Targeted Attack On Neural Machine Translation Using Monolingual Data Poisoning (2021)
  Jun Wang, Chang Xu, Francisco Guzman, et al.
  8.35
• Link-backdoor: Backdoor Attack On Link Prediction Via Node Injection (2022)
  Haibin Zheng, Haiyang Xiong, Haonan Ma, et al.
  8.35
• Using Single-step Adversarial Training To Defend Iterative Adversarial Examples (2020)
  Guanxiong Liu, Issa Khalil, Abdallah Khreishah
  8.09
• CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator (2019)
  Huy Phan, Yi Xie, Siyu Liao, et al.
  8.09
• Have You Poisoned My Data? Defending Neural Networks Against Data Poisoning (2024)
  Fabio de Gaspari, Dorjan Hitaj, Luigi V. Mancini
  7.81
• On Security And Sparsity Of Linear Classifiers For Adversarial Settings (2017)
  Ambra Demontis, Paolo Russu, Battista Biggio, et al.
  7.81
• Perturbation Analysis Of Gradient-based Adversarial Attacks (2020)
  Utku Ozbulak, Manvel Gasparyan, Wesley de Neve, et al.
  7.81
• Shortcuts Everywhere And Nowhere: Exploring Multi-trigger Backdoor Attacks (2024)
  Yige Li, Jiabo He, Hanxun Huang, et al.
  7.70
• From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories (2025)
  Ye Tian et al.
  7.50
• PACE: Poisoning Attacks On Learned Cardinality Estimation (2024)
  Jintao Zhang, Chao Zhang, Guoliang Li, et al.
  7.50
• GPT-Red: Automated Red Teaming via Self-Play at Scale (2026)
  Eric Wallace et al.
  7.32
• HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment (2026)
  Shei Pern Chua et al.
  7.31
• MANDERA: Malicious Node Detection in Federated Learning via Ranking (2021)
  Wanchuang Zhu et al.
  7.16
• Identifying A Training-set Attack's Target Using Renormalized Influence Estimation (2022)
  Zayd Hammoudeh, Daniel Lowd
  7.16
• Sparse Adversarial Attack In Multi-agent Reinforcement Learning (2022)
  Yizheng Hu, Zhihua Zhang
  7.16
• Beating Backdoor Attack At Its Own Game (2023)
  Min Liu, Alberto Sangiovanni-Vincentelli, Xiangyu Yue
  7.16