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Adapt-LFA: Adaptive Gradient-Guided Label Flipping Attack Against Federated Learning-based Intrusion Detection in IoT

Summary:

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.

Author(s):

Hadiseh Rezaei    
University of Portsmouth
United Kingdom

Rahim Taheri    
University of Portsmouth
United Kingdom

Ivan Jordanov    
University of Portsmouth
United Kingdom

Stavros Shiaeles    
University of Portsmouth
United Kingdom