It is very challenging to find malicious users for actual desired traffic. This is particualry a big deal in IoT devices as they are usually poorly protected. IoT-23 is a new dataset of network traffic from Internet of Things (IoT) devices. It has 20 malware captures executed in IoT devices, and 3 captures for benign IoT devices traffic. It was first published in January 2020, with captures ranging from 2018 to 2019. This IoT network traffic was captured in the Stratosphere Laboratory, AIC group, FEL, CTU University, Czech Republic. 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 student will develop novel Machine Learning methods for Anomaly Detection. These could range from Probabilistic Bayesian Detection to Deep Neural Networks. The student will then prove the vailidity of their solution on the IoT-23 dataset!
10% Theory, 70% Simulations, 20%Writing.
Alessandro Chiumento (email@example.com)