Network Anomaly Detection using Federated Learning for the Internet of Things

Cholakoska, Ana; Jakimovski, Bojan; Pfitzner, Bjarne; GJoreski, Hristijan; Arnrich, Bert; Kalendar, Marija; Efnusheva, Danijela

Network Anomaly Detection using Federated Learning for the Internet of Things

Date Issued

2022

Author(s)

Jakimovski, Bojan

Pfitzner, Bjarne

Arnrich, Bert

Efnusheva, Danijela

Abstract

The widespread use of IoT devices has contributed greatly to the continuous digitisation and modernisation of areas such as healthcare, facility management, transportation, and household. These devices allow for real-time mobile sensing, use input and then simplify and automate everyday tasks. However, like all other devices connected to a network, IoT devices are also subject to anomalous behaviour primarily due to security vulnerabilities or malfunction. Apart from this, they have limited resources and can hardly cope with such anomalies and attacks. Therefore, early detection of anomalies is of great importance for the proper functioning of the network and the protection of users’ personal data above all. In this paper, deep learning and federated learning algorithms are applied in order to detect anomalies in IoT network tra c. The results obtained show that all the models achieve high accuracy, with the FL models providing slight worse results compared to the DL models. However, with the increase in the amount of user data, the model based on federated learning is expected to have better results over time.