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

arXiv: https://arxiv.org/abs/2509.26350 · HTML: https://mlsanity.com/paper/2509.26350/

## 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.

## Builds on (most cited first, 14 of 14)

- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [Certified Robustness to Adversarial Examples with Differential Privacy](https://mlsanity.com/paper/1802.03471/) (2018)
- [Stronger Data Poisoning Attacks Break Data Sanitization Defenses](https://mlsanity.com/paper/1811.00741/) (2018)
- [SoK: Certified Robustness for Deep Neural Networks](https://mlsanity.com/paper/2009.04131/) (2020)
- [Efficient Defenses Against Adversarial Attacks](https://mlsanity.com/paper/1707.06728/) (2017)
- [Automated Discovery of Adaptive Attacks on Adversarial Defenses](https://mlsanity.com/paper/2102.11860/) (2021)
- [DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks](https://mlsanity.com/paper/2110.07305/) (2021)
- [Are Gradients on Graph Structure Reliable in Gray-box Attacks?](https://mlsanity.com/paper/2208.05514/) (2022)
- [Towards Adversarially Robust Continual Learning](https://mlsanity.com/paper/2303.17764/) (2023)
- [A Data Augmentation-based Defense Method Against Adversarial Attacks in Neural Networks](https://mlsanity.com/paper/2007.15290/) (2020)
- [CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection](https://mlsanity.com/paper/1907.10442/) (2019)
- [Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification](https://mlsanity.com/paper/2212.14677/) (2022)

## 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.