AAnirudh07 / Aposemat-IoT23-Network-Classification

Binary classification of network data using machine learning algorithms

Aposemat-IoT23-Network-Classification

The IoT-23 Dataset

IoT-23 is a new dataset of network traffic from Internet of Things (IoT) devices. Its goal is to offer a large dataset of real and labeled IoT malware infections and IoT benign traffic for researchers to develop machine learning algorithms.

The IoT-23 Dataset contains 20 captures of malware executed in IoT devices, and 3 captures of benign IoT devices traffic. The dataset contains more than 760 million packets and 325 million labeled flows of more than 500 hours of traffic. The captures were taken during 2018 and 2019 at the Stratosphere Laboratory, AIC group, FEL, CTU University, Czech Republic. This dataset and its research is funded by Avast Software, Prague.

Link: https://www.stratosphereips.org/blog/2020/1/22/aposemat-iot-23-a-labeled-dataset-with-malicious-and-benign-iot-network-traffic

07/08/2022 - Update: Our paper titled "Malicious IoT traffic detection using machine learning techniques" has been accepted by the Informatica Journal(Vol. 47)!