mlsanityData to 14 Oct 2026

SoK: Systematic analysis of adversarial threats against deep learning approaches for autonomous anomaly detection systems in SDN-IoT networks

arXiv 2509.26350 ↗First posted 30 Sep 2025cs.CR cs.AIcited by 0

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Abstract (arXiv)

Integrating SDN and the IoT enhances network control and flexibility. DL-based AAD systems improve security by enabling real-time threat detection in SDN-IoT networks. However, these systems remain vulnerable to adversarial attacks that manipulate input data or exploit model weaknesses, significantly degrading detection accuracy. Existing research lacks a systematic analysis of adversarial vulnerabilities specific to DL-based AAD systems in SDN-IoT environments. This SoK study introduces a structured adversarial threat model and a comprehensive taxonomy of attacks, categorising them into data, model, and hybrid-level threats. Unlike previous studies, we systematically evaluate white, black, and grey-box attack strategies across popular benchmark datasets. Our findings reveal that adversarial attacks can reduce detection accuracy by up to 48.4%, with Membership Inference causing the most significant drop. C&W and DeepFool achieve high evasion success rates. However, adversarial training enhances robustness, and its high computational overhead limits the real-time deployment of SDN-IoT applications. We propose adaptive countermeasures, including real-time adversarial mitigation, enhanced retraining mechanisms, and explainable AI-driven security frameworks. By integrating structured threat models, this study offers a more comprehensive approach to attack categorisation, impact assessment, and defence evaluation than previous research. Our work highlights critical vulnerabilities in existing DL-based AAD models and provides practical recommendations for improving resilience, interpretability, and computational efficiency. This study serves as a foundational reference for researchers and practitioners seeking to enhance DL-based AAD security in SDN-IoT networks, offering a systematic adversarial threat model and conceptual defence evaluation based on prior empirical studies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 55 references that are in the map. First posted on arXiv 30 Sep 2025.

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References · 14

  1. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  3. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 685
  4. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 468
  5. 2018Stronger Data Poisoning Attacks Break Data Sanitization Defensescited by 135
  6. 2020SoK: Certified Robustness for Deep Neural Networkscited by 105
  7. 2017Efficient Defenses Against Adversarial Attackscited by 53
  8. 2021Automated Discovery of Adaptive Attacks on Adversarial Defensescited by 22
  9. 2021DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networkscited by 10
  10. 2022Are Gradients on Graph Structure Reliable in Gray-box Attacks?cited by 10
  11. 2023Towards Adversarially Robust Continual Learningcited by 8
  12. 2020A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networkscited by 7
2 more
  1. 2019CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detectioncited by 2
  2. 2022Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identificationcited by 1

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