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The Internet has recently witnessed unprecedented growth of a class ofconnected assets called the Internet of Things (IoT). Due to relativelyimmature manufacturing processes and limited computing resources, IoTs haveinadequate device-level security measures, exposing the Internet to variouscyber risks. Prior research leveraged predictable patterns in IoT networktraffic to develop inference models. However, they fall short of expectationsin addressing practical challenges, preventing them from being deployed inproduction settings. This thesis identifies four practical challenges anddevelops techniques to address them which can help secure businesses andprotect user privacy against growing cyber threats.  My first contribution balances prediction gains against computing costs oftraffic features for IoT traffic classification and monitoring. My secondcontribution addresses the challenges of measurement costs and data quality. Idevelop an inference method that uses stochastic and deterministic modeling topredict IoT devices in home networks from opaque and coarse-grained IPFIX flowdata. Evaluations show that false positive rates can be reduced by 75% comparedto related work without significantly affecting true positives. My thirdcontribution focuses on the challenge of concept drifts by analyzing over sixmillion flow records collected from 12 real home networks. Finally, my fourthcontribution studies the resilience of machine learning models againstadversarial attacks with a specific focus on decision tree-based models.

Managing Networked IoT Assets Using Practical and Scalable Traffic Inference