Cost-aware Feature Selection for IoT Device Classification

TL;DR

This paper introduces a cost-aware approach to IoT device classification that considers feature extraction costs and variable misclassification risks, optimizing feature selection with a novel stochastic algorithm.

Contribution

It formulates a new cost-aware classification problem for IoT devices and proposes a fast, effective CE-based algorithm to optimize feature selection considering costs and risks.

Findings

The CE-based algorithm effectively minimizes misclassification risk within feature extraction cost limits.

Real device traffic experiments demonstrate the algorithm's practical effectiveness.

The approach balances classification accuracy and feature extraction costs in IoT security.

Abstract

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.