  Classification of IoT devices into different types is of paramount
importance, from multiple perspectives, including security and privacy aspects.
Recent works have explored machine learning techniques for fingerprinting (or
classifying) IoT devices, with promising results. However, existing works have
assumed that the features used for building the machine learning models are
readily available or can be easily extracted from the network traffic; in other
words, they do not consider the costs associated with feature extraction. In
this work, we take a more realistic approach, and argue that feature extraction
has a cost, and the costs are different for different features. We also take a
step forward from the current practice of considering the misclassification
loss as a binary value, and make a case for different losses based on the
misclassification performance. Thereby, and more importantly, we introduce the
notion of risk for IoT device classification. We define and formulate the
problem of cost-aware IoT device classification. This being a combinatorial
optimization problem, we develop a novel algorithm to solve it in a fast and
effective way using the Cross-Entropy (CE) based stochastic optimization
technique. Using traffic of real devices, we demonstrate the capability of the
CE based algorithm in selecting features with minimal risk of misclassification
while keeping the cost for feature extraction within a specified limit.
