ScottishFold: CatBoost-Enabled Lightweight Autonomous Smart Home Device Classification

Massive deployed IoT devices in smart homes pose security management challenges due to different hardware and firmware. To address these challenges, an automated IoT-device-type identification has been used as the first step in autonomous networks in which IoT security policy is automatically managed. Although machine-learning-based traffic patterns were proven high accuracy on a prediction while heavy on preprocessing, we argue that good stability and less computational cost should be considered. We focus on constructing an IoT-device-type classification based on the common features extracted from the network traffic. Inspired by the categorical boosting algorithm, we propose ScottishFold, a classification model, that is constructed from the dirty categorical IoT traffic features with less preprocessing. ScottishFold can classify the IoT device type from a single packet (either a signaling handshake or a data packet) that makes the classification process light. We boost the classification accuracy by integrating a machine reasoning approach into the classification process. The experimental results confirm the effectiveness and robustness of our proposed technique.