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

• URL: http://arxiv.org/abs/2509.26350v1
• Date: Tue, 30 Sep 2025 14:54:42 GMT
• Title: SoK: Systematic analysis of adversarial threats against deep learning approaches for autonomous anomaly detection systems in SDN-IoT networks
• Authors: Tharindu Lakshan Yasarathna, Nhien-An Le-Khac,
• Abstract summary: This study introduces a structured adversarial threat model and a comprehensive taxonomy of attacks, categorising them into data, model, and hybrid-level threats.<n>Our findings reveal that adversarial attacks can reduce detection accuracy by up to 48.4%, with Membership Inference causing the most significant drop.<n>We propose adaptive countermeasures, including real-time adversarial mitigation, enhanced retraining mechanisms, and explainable AI-driven security frameworks.
• Score: 1.4610038284393163
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Quantifying Catastrophic Forgetting in IoT Intrusion Detection Systems [1.7297586889191063]
  Distribution shifts in attack patterns within RPL-based IoT networks pose a critical threat to the reliability and security of large-scale connected systems.<n>Intrusion Detection Systems (IDS) trained on static datasets often fail to generalize to unseen threats.<n>We propose a method-agnostic IDS framework that can integrate diverse continual learning strategies.
  arXiv  Detail & Related papers  (2026-02-27T23:00:36Z)
• Leveraging the Power of Ensemble Learning for Secure Low Altitude Economy [64.39232788946173]
  Low Altitude Economy (LAE) holds immense promise for enhancing societal well-being and driving economic growth.<n>This paper investigates ensemble learning for secure LAE, covering research focuses, solutions, and a case study.
  arXiv  Detail & Related papers  (2026-02-07T23:15:58Z)
• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Adaptive Intrusion Detection System Leveraging Dynamic Neural Models with Adversarial Learning for 5G/6G Networks [2.062593640149623]
  This paper presents an advanced IDS framework that leverages adversarial training and dynamic neural networks in 5G/6G networks.<n>Unlike conventional models, which require costly retraining to update knowledge, the proposed framework integrates incremental learning algorithms, reducing the need for frequent retraining.
  arXiv  Detail & Related papers  (2025-12-11T13:40:37Z)
• Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting [5.35811141279537]
  We propose a novel adversarial detection model that enhances the robustness of IoT IDS against adversarial attacks.<n>We extract attribution fingerprints from network traffic features, enabling the IDS to reliably distinguish between clean and adversarially perturbed inputs.<n>We evaluate the model on a standard IoT benchmark dataset, where it significantly outperformed a state-of-the-art method in detecting adversarial attacks.
  arXiv  Detail & Related papers  (2025-11-09T02:56:54Z)
• A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles [1.6017263994482716]
  Internet of Vehicles (IoV) is prone to various cyber threats, ranging from spoofing and Distributed Denial of Services (DDoS) attacks to malware.<n>To safeguard the IoV ecosystem from intrusions, malicious activities, policy violations, intrusion detection systems (IDS) play a critical role by continuously monitoring and analyzing network traffic to identify and mitigate potential threats in real-time.<n>This paper proposes an effective hierarchical classification framework tailored for IoV networks.
  arXiv  Detail & Related papers  (2025-05-22T04:30:26Z)
• Intrusion Detection System Using Deep Learning for Network Security [0.6554326244334868]
  This paper proposes an experimental evaluation of IDS models based on deep learning techniques.<n>We focus on the classification of network traffic into malicious and benign categories.<n>Among the tested models, the best achieved an accuracy of 96 percent.
  arXiv  Detail & Related papers  (2025-05-09T06:04:58Z)
• Feature Selection via GANs (GANFS): Enhancing Machine Learning Models for DDoS Mitigation [0.0]
  We introduce a novel Generative Adversarial Network-based Feature Selection (GANFS) method for detecting Distributed Denial of Service (DDoS) attacks.<n>By training a GAN exclusively on attack traffic, GANFS effectively ranks feature importance without relying on full supervision.<n>Results point to the potential of integrating generative learning models into cybersecurity pipelines to build more adaptive and scalable detection systems.
  arXiv  Detail & Related papers  (2025-04-21T20:27:33Z)
• Beyond the Surface: An NLP-based Methodology to Automatically Estimate CVE Relevance for CAPEC Attack Patterns [42.63501759921809]
  We propose a methodology leveraging Natural Language Processing (NLP) to associate Common Vulnerabilities and Exposure (CAPEC) vulnerabilities with Common Attack Patternion and Classification (CAPEC) attack patterns.<n> Experimental evaluations demonstrate superior performance compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2025-01-13T08:39:52Z)
• CANEDERLI: On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems [17.351539765989433]
  A growing integration of vehicles with external networks has led to a surge in attacks targeting their Controller Area Network (CAN) internal bus. As a countermeasure, various Intrusion Detection Systems (IDSs) have been suggested in the literature to prevent and mitigate these threats. Most of these systems rely on data-driven approaches such as Machine Learning (ML) and Deep Learning (DL) models. In this paper, we present CANEDERLI, a novel framework for securing CAN-based IDSs.
  arXiv  Detail & Related papers  (2024-04-06T14:54:11Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Enhancing IoT Security Against DDoS Attacks through Federated Learning [0.0]
  Internet of Things (IoT) has ushered in transformative connectivity between physical devices and the digital realm. Traditional DDoS mitigation approaches are ill-equipped to handle the intricacies of IoT ecosystems. This paper introduces an innovative strategy to bolster the security of IoT networks against DDoS attacks by harnessing the power of Federated Learning.
  arXiv  Detail & Related papers  (2024-03-16T16:45:28Z)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  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.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)
• Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training [62.77129284830945]
  This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach. We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
  arXiv  Detail & Related papers  (2022-06-14T04:55:11Z)
• Adversarial defense for automatic speaker verification by cascaded self-supervised learning models [101.42920161993455]
  More and more malicious attackers attempt to launch adversarial attacks at automatic speaker verification (ASV) systems. We propose a standard and attack-agnostic method based on cascaded self-supervised learning models to purify the adversarial perturbations. Experimental results demonstrate that the proposed method achieves effective defense performance and can successfully counter adversarial attacks.
  arXiv  Detail & Related papers  (2021-02-14T01:56:43Z)

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.