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The development and adoption of Internet of Things (IoT) devices will growsignificantly in the coming years to enable Industry 4.0. Many forms of IoTdevices will be developed and used across industry verticals. However, theeuphoria of this technology adoption is shadowed by the solemn presence ofcyber threats that will follow its growth trajectory. Cyber threats wouldeither embed their malicious code or attack vulnerabilities in IoT that couldinduce significant consequences in cyber and physical realms. In order tomanage such destructive effects, incident responders and cyber investigatorsrequire the capabilities to find these rogue IoT and contain them quickly. Suchonline devices may only leave network activity traces. A collection of relevanttraces could be used to infer the IoT's network behaviorial fingerprints and inturn could facilitate investigative find of these IoT. However, the challengeis how to infer these fingerprints when there is limited network activitytraces. This research proposes the novel model construct that learns to inferthe network behaviorial fingerprint of specific IoT based on limited networkactivity traces using a One-Card Time Series Meta-Learner called DeepNetPrint.Our research also demonstrates the application of DeepNetPrint to identify IoTdevices that performs comparatively well against leading supervised learningmodels. Our solution would enable cyber investigator to identify specific IoTof interest while overcoming the constraints of having only limited networktraces of the IoT.

IoT Network Behavioral Fingerprint Inference with Limited Network Trace for Cyber Investigation: A Meta Learning Approach