Eman Maali

Eman Maali

Emaan is a fourth-year Ph.D. candidate at Adaptive Emergent Systems Engineering Laboratory (AESE) at the Department of Computing at Imperial. She is working under the supervision of Professor Julie McCann. Her research interest is in the field of secured IoT environments. In 2017, she completed her MSc in Electromagnetic Sensor Networks, at the University of Birmingham. The focus of the Masters was on electromagnetic, antennas, propagation, computer communications networks, and RF and microwave engineering. Moreover, she completed her BA in Computer Systems Engineering from Birzeit University in Palestine.

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Are we there yet in terms of practicality and ML-based IoT device identification for security?

Lightning Talk

In my presentation, I will show how we systematically evaluated ML-based IoT identification systems, leveraging curated datasets and model explainability. Through the presentation, I will show several factors that impact the performance of IoT identification models in network deployments. These factors include variability in device operation modes, spatial, temporal, and portability. For example, when using identical devices in different environments, the performance of IoT identification models declines dramatically. Additionally, the performance of machine learning-based identification models for IoT devices can diminish within as early as two weeks.