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Emulation-Based Dataset EmuIoT-VT for NIDS in IoT Systems.

Antanas Čenys¹, Simran Kaur Hora¹, Nikolaj Goranin¹

¹Department of Information Systems, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This summary is machine-generated.

A new balanced dataset, EmuIoT-VT, addresses class imbalance in anomaly-based Network Intrusion Detection Systems (NIDS) for Internet of Things (IoT) security. This dataset enables more reliable detection of network threats.

Keywords:

Internet of Things IoT dataset IoT security anomaly detection deep learning machine learning network intrusion detection

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Area of Science:

Cybersecurity
Network Intrusion Detection Systems (NIDS)
Machine Learning in IoT Security

Background:

The proliferation of Internet of Things (IoT) devices necessitates robust security mechanisms.
Anomaly-based NIDS using machine learning (ML) and deep learning (DL) are vital for detecting network anomalies.
Class data imbalance in existing datasets hinders the performance of ML/DL-based anomaly NIDS.

Purpose of the Study:

To introduce EmuIoT-VT, a novel, balanced dataset for developing and evaluating anomaly-based NIDS for IoT.
To present a new emulation-based method for generating realistic IoT network traffic.
To address the challenge of class data imbalance in IoT security datasets.

Main Methods:

Developed a novel emulation-based method to create virtual IoT device replicas for realistic traffic generation.
Collected network traffic in an isolated offline environment to ensure data integrity.
Designed the EmuIoT-VT dataset to be balanced-by-design, with 28,000 evenly distributed labeled records.

Main Results:

Created the EmuIoT-VT dataset, featuring 82 extracted features from PCAP data.
The dataset includes balanced representation across devices, classes, and subclasses.
Supports both binary and multiclass classification tasks, covering attack categories like DoS, brute force, reconnaissance, and exploitation.

Conclusions:

The EmuIoT-VT dataset provides a solution to the class imbalance problem in IoT network security datasets.
Facilitates the development of more accurate and reliable anomaly-based NIDS for IoT environments.
Offers a foundation for future research in IoT network security and threat detection.