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cs.CR2026
Online AutoML: Evaluating Poisoning Attacks on Cost-Utility Adversarial Training Defence Strategy for IoT Networks
Chukwunonso Henry Nwokoye, Khalil El-Khatib, Li Yang
Machine learning (ML)-powered poisoning attack vectors are adversarial maneuvers whereby an attacker intentionally inserts, corrupts, or alters training data to distort an ML model…
cs.CR2026
Evasion Attacks on Cost-Utility-Based Adversarial Training for Online AutoML in IoT Networks
Chukwunonso Henry Nwokoye, Wajiha Zaheer, Khalil El-Khatib +1
As Internet of Things (IoT) networks increasingly depend on machine learning for anomaly, malware, intrusion detection, and network monitoring, such systems have become attractive…