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