Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in IoT Networks
Not on the map. Why?
Abstract (arXiv)
The integration of machine learning (ML) algorithms into Internet of Things (IoT) applications has introduced significant advantages alongside vulnerabilities to adversarial attacks, especially within IoT-based intrusion detection systems (IDS). While theoretical adversarial attacks have been extensively studied, practical implementation constraints have often been overlooked. This research addresses this gap by evaluating the feasibility of evasion attacks on IoT network-based IDSs, employing a novel black-box adversarial attack. Our study aims to bridge theoretical vulnerabilities with real-world applicability, enhancing understanding and defense against sophisticated threats in modern IoT ecosystems. Additionally, we propose a defense scheme tailored to mitigate the impact of evasion attacks, thereby reinforcing the resilience of ML-based IDSs. Our findings demonstrate successful evasion attacks against IDSs, underscoring their susceptibility to advanced techniques. In contrast, we proposed a defense mechanism that exhibits robust performance by effectively detecting the majority of adversarial traffic, showcasing promising outcomes compared to current state-of-the-art defenses. By addressing these critical cybersecurity challenges, our research contributes to advancing IoT security and provides insights for developing more resilient IDS.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 72 references that are in the map. First posted on arXiv 24 Mar 2026.
Builds on
23 of 72 references.
References · 23
- 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 953
- 2021Tabular Data: Deep Learning is Not All You Needcited by 535
- 2017MagNet: a Two-Pronged Defense against Adversarial Examplescited by 269
- 2017On the (Statistical) Detection of Adversarial Examplescited by 180
- 2018Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Datasetcited by 99
- 2021Towards Transferable Adversarial Attacks on Vision Transformerscited by 80
- 2021Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systemscited by 41
- 2021Towards a Standard Feature Set for Network Intrusion Detection System Datasetscited by 40
- 2023SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimizationcited by 35
- 2022Zero-Query Transfer Attacks on Context-Aware Object Detectorscited by 17
- 2023Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systemscited by 14
- 2020Defense-PointNet: Protecting PointNet Against Adversarial Attackscited by 13
11 more
- 2023MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimizationcited by 13
- 2019Hardening Random Forest Cyber Detectors Against Adversarial Attackscited by 12
- 2022Understanding Adversarial Robustness Against On-manifold Adversarial Examplescited by 12
- 2020Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion Detectioncited by 10
- 2022TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systemscited by 7
- 2021Adversarial Training for Deep Learning-based Intrusion Detection Systemscited by 6
- 2023A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection Systemcited by 6
- 2021Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networkscited by 5
- 2022LiteLSTM Architecture for Deep Recurrent Neural Networkscited by 5
- 2021Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systemscited by 3
- 2023Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systemscited by 3
Built on by
Not cited yet.