1 citations · 1 across the 3 of their papers we have counts for
6 papers
Recording-Aware Evaluation of Multimodal OT Intrusion Detection
Mustafa Umut Ozbek, Khalil El-Khatib, Li Yang
Machine-learning-based intrusion detection systems (IDSs) for operational technology (OT) are commonly evaluated on benchmark datasets in which normal and attack data are collected…
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…
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…
Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination
Mustafa Umut Ozbek, Taiwo Ojo, Pooria Madani +2
Machine-learning-based anomaly detection is increasingly used in industrial control systems (ICS), yet most studies assume that detector training data is trustworthy. In practice,…
An Efficient Intrusion Detection System for Safeguarding Radiation Detection Systems
Nathanael Coolidge, Jaime González Sanz, Li Yang +5
Radiation Detection Systems (RDSs) are used to measure and detect abnormal levels of radioactive material in the environment. These systems are used in many applications to mitigat…
Securing Radiation Detection Systems with an Efficient TinyML-Based IDS for Edge Devices
Einstein Rivas Pizarro, Wajiha Zaheer, Li Yang +2
Radiation Detection Systems (RDSs) play a vital role in ensuring public safety across various settings, from nuclear facilities to medical environments. However, these systems are…