Nhien-An Le-Khac

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.

The rapid advancement of generators (e.g., StyleGAN, Midjourney, DALL-E) has produced highly realistic synthetic images, posing significant challenges to digital media authenticity. These generators are typically based on a few core architectural families, primarily Generative Adversarial Networks (GANs) and Diffusion Models (DMs). A critical vulnerability in current forensics is the failure of detectors to achieve cross-generator generalization, especially when crossing architectural boundaries (e.g., from GANs to DMs). We hypothesize that this gap stems from fundamental differences in the artifacts produced by these \textbf{distinct architectures}. In this work, we provide a theoretical analysis explaining how the distinct optimization objectives of the GAN and DM architectures lead to different manifold coverage behaviors. We demonstrate that GANs permit partial coverage, often leading to boundary artifacts, while DMs enforce complete coverage, resulting in over-smoothing patterns. Motivated by this analysis, we propose the \textbf{Tri}archy \textbf{Detect}or (TriDetect), a semi-supervised approach that enhances binary classification by discovering latent architectural patterns within the "fake" class. TriDetect employs balanced cluster assignment via the Sinkhorn-Knopp algorithm and a cross-view consistency mechanism, encouraging the model to learn fundamental architectural distincts. We evaluate our approach on two standard benchmarks and three in-the-wild datasets against 13 baselines to demonstrate its generalization capability to unseen generators.