CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications
Not on the map. Why?
Abstract (arXiv)
Ensemble defenses, are widely employed in various security-related applications to enhance model performance and robustness. The widespread adoption of these techniques also raises many questions: Are general ensembles defenses guaranteed to be more robust than individuals? Will stronger adaptive attacks defeat existing ensemble defense strategies as the cybersecurity arms race progresses? Can ensemble defenses achieve adversarial robustness to different types of attacks simultaneously and resist the continually adjusted adaptive attacks? Unfortunately, these critical questions remain unresolved as there are no platforms for comprehensive evaluation of ensemble adversarial attacks and defenses in the cybersecurity domain. In this paper, we propose a general Cybersecurity Adversarial Robustness Evaluation (CARE) platform aiming to bridge this gap.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 66 references that are in the map. First posted on arXiv 20 Jan 2024.
Builds on
36 of 66 references.
References · 36
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
- 2016Adversarial examples in the physical worldcited by 1,762
- 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
- 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 950
- 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
- 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
- 2013Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programmingcited by 716
- 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
- 2020On Adaptive Attacks to Adversarial Example Defensescited by 547
- 2018Black-box Adversarial Attacks with Limited Queries and Informationcited by 499
24 more
- 2017Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networkscited by 484
- 2017Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Modelscited by 459
- 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
- 2019HopSkipJumpAttack: A Query-Efficient Decision-Based Attackcited by 328
- 2019Adversarial Training and Robustness for Multiple Perturbationscited by 182
- 2018Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detectioncited by 121
- 2019Adversarial Robustness Against the Union of Multiple Perturbation Modelscited by 88
- 2019ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimizationcited by 75
- 2019Improving Adversarial Robustness of Ensembles with Diversity Trainingcited by 69
- 2018Improving the Generalization of Adversarial Training with Domain Adaptationcited by 48
- 2018Exploring Adversarial Examples in Malware Detectioncited by 41
- 2021Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systemscited by 39
- 2020Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detectioncited by 33
- 2018Adversarial Deep Learning for Robust Detection of Binary Encoded Malwarecited by 32
- 2020Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectorscited by 25
- 2019Dynamic Malware Analysis with Feature Engineering and Feature Learningcited by 23
- 2017Robustness to Adversarial Examples through an Ensemble of Specialistscited by 21
- 2017Foolbox: A Python toolbox to benchmark the robustness of machine learning modelscited by 19
- 2018Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examplescited by 19
- 2021Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Artcited by 18
- 2020Robust Spammer Detection by Nash Reinforcement Learningcited by 17
- 2020Arms Race in Adversarial Malware Detection: A Surveycited by 16
- 2020Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion Detectioncited by 10
- 2020Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attackcited by 3
Built on by
Citing papers per year: 2024: 1.
1 paper.