Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey

• URL: http://arxiv.org/abs/2303.06302v1
• Date: Sat, 11 Mar 2023 04:19:31 GMT
• Title: Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
• Authors: Yulong Wang, Tong Sun, Shenghong Li, Xin Yuan, Wei Ni, Ekram Hossain, H. Vincent Poor
• Abstract summary: Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
• Score: 114.17568992164303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the Internet and relevant scenarios. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularization-based methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.

Related papers

• A Review of the Duality of Adversarial Learning in Network Intrusion: Attacks and Countermeasures [0.0]
  Adversarial attacks, particularly those targeting vulnerabilities in deep learning models, present a nuanced and substantial threat to cybersecurity. Our study delves into adversarial learning threats such as Data Poisoning, Test Time Evasion, and Reverse Engineering. Our research lays the groundwork for strengthening defense mechanisms to address the potential breaches in network security and privacy posed by adversarial attacks.
  arXiv  Detail & Related papers  (2024-12-18T14:21:46Z)
• Sustainable Self-evolution Adversarial Training [51.25767996364584]
  We propose a Sustainable Self-Evolution Adversarial Training (SSEAT) framework for adversarial training defense models. We introduce a continual adversarial defense pipeline to realize learning from various kinds of adversarial examples. We also propose an adversarial data replay module to better select more diverse and key relearning data.
  arXiv  Detail & Related papers  (2024-12-03T08:41:11Z)
• FEDLAD: Federated Evaluation of Deep Leakage Attacks and Defenses [50.921333548391345]
  Federated Learning is a privacy preserving decentralized machine learning paradigm. Recent research has revealed that private ground truth data can be recovered through a gradient technique known as Deep Leakage. This paper introduces the FEDLAD Framework (Federated Evaluation of Deep Leakage Attacks and Defenses), a comprehensive benchmark for evaluating Deep Leakage attacks and defenses.
  arXiv  Detail & Related papers  (2024-11-05T11:42:26Z)
• Robust Image Classification: Defensive Strategies against FGSM and PGD Adversarial Attacks [0.0]
  Adversarial attacks pose significant threats to the robustness of deep learning models in image classification. This paper explores and refines defense mechanisms against these attacks to enhance the resilience of neural networks.
  arXiv  Detail & Related papers  (2024-08-20T02:00:02Z)
• A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification [35.061430235135155]
  Defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks. Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies.
  arXiv  Detail & Related papers  (2024-07-09T12:28:38Z)
• Explainability-Informed Targeted Malware Misclassification [0.0]
  Machine learning models for malware classification into categories have shown promising results. Deep neural networks have shown vulnerabilities against intentionally crafted adversarial attacks. Our paper explores such adversarial vulnerabilities of neural network based malware classification system.
  arXiv  Detail & Related papers  (2024-05-07T04:59:19Z)
• Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook [2.1771693754641013]
  We aim to provide a thorough understanding of the concept of physical adversarial attacks, analyzing their key characteristics and distinguishing features. Our article delves into various physical adversarial attack methods, categorized according to their target tasks in different applications. We assess the performance of these attack methods in terms of their effectiveness, stealthiness, and robustness.
  arXiv  Detail & Related papers  (2023-08-11T15:02:19Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution [83.84968082791444]
  Deep neural networks are vulnerable to intentionally crafted adversarial examples. Various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models.
  arXiv  Detail & Related papers  (2021-08-29T08:11:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.