Cost-aware Feature Selection for IoT Device Classification

Biswadeep Chakraborty, Dinil Mon Divakaran, Ido Nevat, Gareth W. Peters, Mohan Gurusamy

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
81 Downloads (Pure)


The classification of Internet-of-Things (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, the 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.

Original languageEnglish
Pages (from-to)11052-11064
Number of pages13
JournalIEEE Internet of Things Journal
Issue number14
Early online date13 Jan 2021
Publication statusPublished - 15 Jul 2021


  • Identification
  • Internet-of-Things (IoT)
  • machine learning
  • network
  • optimization

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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