Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics

By Arunan Sivanathan, H. Gharakheili, F. Loi, Adam Radford, C. Wijenayake, A. Vishwanath, V. Sivaraman. IEEE Transactions on Mobile Computing, 2019

733 citations recorded by the source corpus.

The Internet of Things (IoT) is being hailed as the next wave revolutionizing our society, and smart homes, enterprises, and cities are increasingly being equipped with a plethora of IoT devices. Yet, operators of such smart environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. In this paper, we address this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the network level. Our contributions are fourfold. First, we instrument a smart environment with 28 different IoT devices spanning cameras, lights, plugs, motion sensors, appliances, and health-monitors. We collect and synthesize traffic traces from this infrastructure for a period of six months, a subset of which we release as open data for the community to use. Second, we present insights into the underlying network traffic characteristics using statistical attributes such as activity cycles, port numbers, signalling patterns, and cipher suites. Third, we develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99 percent accuracy based on their network activity. Finally, we discuss the trade-offs between cost, speed, and performance involved in deploying the classification framework in real-time. Our study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/56a57b99b100694f74fa38b9a18b9269d6c9f937

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

FLAME: Taming Backdoors in Federated Learning (2022, methodology): “Based on the FL anomaly detection system DÏoT [44], we use three datasets called DIoT-Benign, DIoT-Attack, and UNSW-Benign [44,53] from real-world home and office deployments (four homes and two offices located in Germany and Australia).”
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems (2020, background): “Sivanathan et al. [17] came up later with IoT-based datasets for the classiﬁcation of IoT devices based on network trafﬁc characteristics.”
Information Exposure From Consumer IoT Devices: A Multidimensional, Network-Informed Measurement Approach (2019, background): “[42] characterize destinations as first party and third parties, and identify protocols used by a set of 28 devices.”