mlsanityData to 14 Oct 2026

Diffusion-based Adversarial Purification for Intrusion Detection

arXiv 2406.17606 ↗First posted 25 Jun 2024cs.CR cs.AI cs.LGcited by 1

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

The escalating sophistication of cyberattacks has encouraged the integration of machine learning techniques in intrusion detection systems, but the rise of adversarial examples presents a significant challenge. These crafted perturbations mislead ML models, enabling attackers to evade detection or trigger false alerts. As a reaction, adversarial purification has emerged as a compelling solution, particularly with diffusion models showing promising results. However, their purification potential remains unexplored in the context of intrusion detection. This paper demonstrates the effectiveness of diffusion models in purifying adversarial examples in network intrusion detection. Through a comprehensive analysis of the diffusion parameters, we identify optimal configurations maximizing adversarial robustness with minimal impact on normal performance. Importantly, this study reveals insights into the relationship between diffusion noise and diffusion steps, representing a novel contribution to the field. Our experiments are carried out on two datasets and against 5 adversarial attacks. The implementation code is publicly available.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 22 of its 35 references that are in the map. First posted on arXiv 25 Jun 2024.

Builds on

22 of 35 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Adversarial Guided Diffusion Models for Adversarial Purification24 Mar
  2. 2023DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification27 Oct
  3. 2022Guided Diffusion Model for Adversarial Purification from Random Noise22 Jun
  4. 2022Guided Diffusion Model for Adversarial Purification30 May
  5. 2022Diffusion Models for Adversarial Purification16 May

Other references · 17

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  6. 2013Intriguing properties of neural networkscited by 5,155
  7. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  8. 2016Adversarial examples in the physical worldcited by 1,769
  9. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  10. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  11. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 546
  12. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 351
5 more
  1. 2017PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examplescited by 244
  2. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 52
  3. 2019A Survey of Network-based Intrusion Detection Data Setscited by 46
  4. 2023Black-box Backdoor Defense via Zero-shot Image Purificationcited by 45
  5. 2023DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detectioncited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Diff-DDoS: Realistic Cyber-Physical Attack Synthesis and Robust Detection for 5G-Enabled CPS Using Tabular Diffusion Modelscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023Purify++: Improving Diffusion-Purification with Advanced Diffusion Models and Control of Randomness
  2. 2025Defending Against Frequency-Based Attacks with Diffusion Models
  3. 2024Watch the Watcher! Backdoor Attacks on Security-Enhancing Diffusion Models
  4. 2024Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective
  5. 2025Towards more transferable adversarial attack in black-box manner
  6. 2024Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information
  7. 2025Diffusion or Non-Diffusion Adversarial Defenses: Rethinking the Relation between Classifier and Adversarial Purifier
  8. 2024ADBM: Adversarial diffusion bridge model for reliable adversarial purification