Adapt-LFA: Adaptive Gradient-Guided Label Flipping Attack Against Federated Learning-based Intrusion Detection in IoT
Hadiseh Rezaie, Rahim Taheri, Ivan Jordanov, Stavros N. Shiaeles · 2025
Adopting Federated Learning (FL) in Intrusion Detection Systems (IDS) for IoT enhances privacy by decentralizing model training. However, FL is still vulnerable to adversarial threats, particularly Label-Flipping Attacks (LFA) that manipulate local training data to degrade model performance. This paper introduces the Adaptive Gradient-Guided Label Flipping Attack (Adapt-LFA), which strategically flips training labels using gradient-based optimization to maximize classification errors while minimizing detection risks. Evaluations on the CSE-CICIDS2018 and CICIoV2024 datasets show that Adapt-LFA reduces accuracy by $10 \%$ in Recurrent Neural Networks (RNN) and 13% in Convolutional Neural Networks (CNN), outperforming baseline LFA in disrupting FL-based IDS. These results highlight the effectiveness of the attack in degrading IDS performance within FL-based IoT environments.