IoT Malware Analysis Using Federated Learning: A Comprehensive Survey

Abstract

The Internet of Things (IoT) has paved the way to a highly connected society where all things are interconnected and exchanging information has become more accessible through the internet. With the use of IoT devices, the threat of malware has increased rapidly. The increased number of existing and new malware variants has made protecting IoT devices and networks challenging. The malware can hide in the systems and disables its activity when there are attempts to discover and detect them. With technological advances, there are various emerging techniques to address this problem. However, they still encounter issues concerning the privacy and security of the user’s data and suffer from a single point of failure. To address this issue, there are recent research developments conducted to use Federated Learning (FL). FL is a decentralized technique that trains the user’s data on-device and exchanges the parameters without sharing the user’s data. FL is implemented to secure the user’s data, provide safe and accurate models, and prevent the single point of failure in the centralized models. This paper provides an overview of different approaches that integrate FL with IoT. Finally, we discuss the applications of FL, the research challenges, and future research directions.