Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction

TL;DR

This paper introduces a new fake traffic injection attack on federated learning-based wireless traffic prediction systems and proposes a defense mechanism that detects and removes abnormal model parameters, enhancing security.

Contribution

It presents a novel fake traffic injection attack and a statistical-based defense mechanism for FL-based wireless traffic prediction, addressing security gaps in regression tasks.

Findings

The attack significantly degrades model accuracy.

The defense effectively detects and mitigates the attack.

Both outperform existing methods in experiments.

Abstract

Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data. This makes it ideal for applications like wireless traffic prediction (WTP), which plays a crucial role in optimizing network resources, enabling proactive traffic flow management, and enhancing the reliability of downstream communication-aided applications, such as IoT devices, autonomous vehicles, and industrial automation systems. Despite its promise, the security aspects of FL-based distributed wireless systems, particularly in regression-based WTP problems, remain inadequately investigated. In this paper, we introduce a novel fake traffic injection (FTI) attack, designed to undermine the FL-based WTP system by injecting fabricated traffic distributions with minimal knowledge. We further propose a defense mechanism, termed global-local inconsistency detection (GLID), which strategically removes abnormal model parameters that deviate beyond a specific percentile range estimated through statistical methods in each dimension. Extensive experimental evaluations, performed on real-world wireless traffic datasets, demonstrate that both our attack and defense strategies significantly outperform existing baselines.