Flow-Based Detection and Identification of Zero-Day IoT Cameras

Abstract

The majority of consumer IoT devices lack mechanisms for administrators to monitor and control them, hindering tailored security policies. A key challenge is identifying whether a new device, especially a streaming IoT camera, has joined the network. We present zCamInspector, a system for identifying known IoT cameras with supervised classifiers (zCamClassifier) and detecting zero-day cameras with one-class classifiers (zCamDetector). We analyzed ~40GB of traffic across three datasets: Set I (six commercial IoT cameras), Set II (five open-source IoT cameras, ~1.5GB), and Set III (four conferencing and two video-sharing applications as non-IoT traffic). From each, 62 flow-based features were extracted using CICFlowmeter. zCamInspector employs seven supervised models (ET, DT, RF, KNN, XGB, LKSVM, GNB) and four one-class models (OCSVM, SGDOCSVM, IF, DeepSVDD). Results show that XGB identifies IoT cameras with >99% accuracy and false negatives as low as 0.3%, outperforming state-of-the-art methods. For zero-day detection, accuracies reached 93.20% (OCSVM), 96.55% (SGDOCSVM), 78.65% (IF), and 92.16% (DeepSVDD). When all devices were treated as zero-day, DeepSVDD performed best with mean training/testing accuracies of 96.03%/74.51%. zCamInspector also achieved >95% accuracy for specific devices, such as Spy Clock cameras, demonstrating its robustness for identifying and detecting zero-day IoT cameras in diverse network environments.