Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in IoT Networks
2026-03-24 • Cryptography and Security
Cryptography and SecurityArtificial Intelligence
AI summaryⓘ
The authors looked at how machine learning-based security systems in IoT devices can be tricked by sneaky attacks called evasion attacks. They tested a new way to fool these systems without knowing all their details, showing that these attacks can really work in practice. To help fix this, the authors also created a defense method that better detects these tricky attacks. Their work helps us understand how real-world attacks happen and how to protect IoT security better.
Internet of Things (IoT)Machine Learning (ML)Intrusion Detection System (IDS)Adversarial AttackEvasion AttackBlack-box AttackCybersecurityNetwork SecurityAttack Defense Mechanism
Authors
Islam Debicha, Tayeb Kenaza, Ishak Charfi, Salah Mosbah, Mehdi Sehaki, Jean-Michel Dricot
Abstract
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.