Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling
Identification of IoT device types from passive traffic is increasingly used for security management in enterprise and ISP networks. However, the performance of machine learning-based classifiers gradually degrades under concept drift as device behavior evolves. Therefore, maintaining classification performance requires periodic retraining with newly labeled deployment traffic. The operational challenge is determining how much and which deployment traffic instances to label for maintaining classification performance. We show that these two decisions should be treated separately. While retraining solely on instances selected by a drift detector is prone to systematically overlooking parts ...
| Authors | Shayan Azizi, Norihiro Okui, Masataka Nakahara et al. |
|---|---|
| Primary category | cs.NI |
| Published | 16 August 2026 |
| Last updated at source | 24 August 2026 |
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