What question did this study set out to answer?

This research investigates the susceptibility of federated learning in IoT intrusion detection systems to poisoning attacks, especially backdoor attacks.

January 1, 2020Open Access

Poisoning Attacks on Federated Learning-based IoT Intrusion Detection System

Key Points

This research investigates the susceptibility of federated learning in IoT intrusion detection systems to poisoning attacks, especially backdoor attacks.
Analysis of federated learning mechanisms in IoT environments
Identification of potential vulnerabilities in the training process
Evaluation of attacks on models deployed across several IoT gateways
Observed significant degradation in model accuracy under backdoor attacks
Quantified privacy preservation measures compromised due to attack vectors
Presented potential strategies to mitigate such vulnerabilities effectively

Abstract

Federated Learning (FL) is an appealing method for applying machine learning to large scale systems due to the privacy and efficiency advantages that its training mechanism provides. One important field for FL deployment is emerging IoT applications. In particular, FL has been recently used for IoT intrusion detection systems where clients, e.g., a home security gateway, monitors traffic data generated by IoT devices in its network, trains a local intrusion detection model, and send this model to a central entity, the aggregator, who then computes a global model (using the models of all gateways) that is distributed back to clients. This approach protects the privacy of users as it does not require local clients to share their potentially private IoT data with any other parties, and it is in general more efficient than a centralized system. However, FL schemes have been subject to poising attacks, in particular to backdoor attacks.

AI에게 질문

Bookmark

View Full Paper