  The advent of the Internet of Things (IoT) has brought forth additional
intricacies and difficulties to computer networks. These gadgets are
particularly susceptible to cyber-attacks because of their simplistic design.
Therefore, it is crucial to recognise these devices inside a network for the
purpose of network administration and to identify any harmful actions. Network
traffic fingerprinting is a crucial technique for identifying devices and
detecting anomalies. Currently, the predominant methods for this depend heavily
on machine learning (ML). Nevertheless, machine learning (ML) methods need the
selection of features, adjustment of hyperparameters, and retraining of models
to attain optimal outcomes and provide resilience to concept drifts detected in
a network. In this research, we suggest using locality-sensitive hashing (LSH)
for network traffic fingerprinting as a solution to these difficulties. Our
study focuses on examining several design options for the Nilsimsa LSH
function. We then use this function to create unique fingerprints for network
data, which may be used to identify devices. We also compared it with ML-based
traffic fingerprinting and observed that our method increases the accuracy of
state-of-the-art by 12% achieving around 94% accuracy in identifying devices in
a network.
