IoT Device Identification with Machine Learning: Common Pitfalls and Best Practices
It addresses reproducibility issues for researchers in IoT security, but is incremental as it focuses on best practices rather than new breakthroughs.
This paper tackles the problem of IoT device identification using machine learning by analyzing common pitfalls in existing methods, resulting in guidelines to improve reproducibility and generalizability of security models.
This paper critically examines the device identification process using machine learning, addressing common pitfalls in existing literature. We analyze the trade-offs between identification methods (unique vs. class based), data heterogeneity, feature extraction challenges, and evaluation metrics. By highlighting specific errors, such as improper data augmentation and misleading session identifiers, we provide a robust guideline for researchers to enhance the reproducibility and generalizability of IoT security models.