الباحثون

Khalil El-Khatib

المنشورات 2

نسخة أولية وصول مفتوح

Online AutoML: Evaluating Poisoning Attacks on Adversarial Training Defense Strategy in IoT Networks

Chukwunonso Henry Nwokoye, Khalil El-Khatib, Li Yang · 2026

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's learning process. The objective is to diminish model efficacy, instill biases, induce misclassifications, or include concealed backdoors …

نسخة أولية وصول مفتوح

Evasion Attacks on Cost-Utility-Based Adversarial Training for Online AutoML in IoT Networks

Chukwunonso Henry Nwokoye, Wajiha Zaheer, Khalil El-Khatib وآخرون · 2026

As Internet of Things (IoT) networks increasingly depend on machine learning for anomaly, malware, intrusion detection, and network monitoring, such systems have become attractive targets for evasion attacks. Evasion attacks pose a major security risk because an adversary intentionally modifies input data to mislead a …

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