Adapt-LFA: adaptive gradient-guided label flipping attack against federated learning-based intrusion detection in IoT

Hadiseh Rezaei; Rahim Taheri; Ivan Jordanov; Stavros Shiaeles

doi:10.1109/CSR64739.2025.11130071

Adapt-LFA: adaptive gradient-guided label flipping attack against federated learning-based intrusion detection in IoT

Hadiseh Rezaei
, Rahim Taheri^*
, Ivan Jordanov
, Stavros Shiaeles

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

34 Downloads (Pure)

Abstract

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.

Original language	English
Title of host publication	2025 IEEE International Conference on Cyber Security and Resilience (CSR)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	407-412
Number of pages	6
ISBN (Electronic)	9798331535919
ISBN (Print)	9798331535926
DOIs	https://doi.org/10.1109/CSR64739.2025.11130071
Publication status	Published - 26 Aug 2025
Event	2025 IEEE International Conference on Cyber Security and Resilience - Chania, Crete, Greece Duration: 4 Aug 2025 → 6 Aug 2025 https://www.ieee-csr.org/#technical-program-committee

Conference

Conference	2025 IEEE International Conference on Cyber Security and Resilience
Country/Territory	Greece
City	Crete
Period	4/08/25 → 6/08/25
Internet address	https://www.ieee-csr.org/#technical-program-committee

Keywords

Federated Learning
Intrusion Detection Systems
Adversarial Attack
Label Flipping Attack

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10.1109/CSR64739.2025.11130071

AGLFA_Post_PrintAccepted author manuscript (Post-print), 535 KBLicence: CC BY

Cite this

Rezaei, H., Taheri, R., Jordanov, I., & Shiaeles, S. (2025). Adapt-LFA: adaptive gradient-guided label flipping attack against federated learning-based intrusion detection in IoT. In 2025 IEEE International Conference on Cyber Security and Resilience (CSR) (pp. 407-412). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSR64739.2025.11130071

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title = "Adapt-LFA: adaptive gradient-guided label flipping attack against federated learning-based intrusion detection in IoT",

abstract = "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.",

keywords = "Federated Learning, Intrusion Detection Systems, Adversarial Attack, Label Flipping Attack",

author = "Hadiseh Rezaei and Rahim Taheri and Ivan Jordanov and Stavros Shiaeles",

year = "2025",

month = aug,

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doi = "10.1109/CSR64739.2025.11130071",

language = "English",

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booktitle = "2025 IEEE International Conference on Cyber Security and Resilience (CSR)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "2025 IEEE International Conference on Cyber Security and Resilience<br/> ; Conference date: 04-08-2025 Through 06-08-2025",

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}

Rezaei, H, Taheri, R , Jordanov, I & Shiaeles, S 2025, Adapt-LFA: adaptive gradient-guided label flipping attack against federated learning-based intrusion detection in IoT. in 2025 IEEE International Conference on Cyber Security and Resilience (CSR). Institute of Electrical and Electronics Engineers Inc., pp. 407-412, 2025 IEEE International Conference on Cyber Security and Resilience
, Crete, Greece, 4/08/25. https://doi.org/10.1109/CSR64739.2025.11130071

Adapt-LFA: adaptive gradient-guided label flipping attack against federated learning-based intrusion detection in IoT. / Rezaei, Hadiseh; Taheri, Rahim ; Jordanov, Ivan et al.
2025 IEEE International Conference on Cyber Security and Resilience (CSR). Institute of Electrical and Electronics Engineers Inc., 2025. p. 407-412.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Adapt-LFA: adaptive gradient-guided label flipping attack against federated learning-based intrusion detection in IoT

AU - Rezaei, Hadiseh

AU - Taheri, Rahim

AU - Jordanov, Ivan

AU - Shiaeles, Stavros

PY - 2025/8/26

Y1 - 2025/8/26

N2 - 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.

AB - 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.

KW - Federated Learning

KW - Intrusion Detection Systems

KW - Adversarial Attack

KW - Label Flipping Attack

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DO - 10.1109/CSR64739.2025.11130071

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BT - 2025 IEEE International Conference on Cyber Security and Resilience (CSR)

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Adapt-LFA: adaptive gradient-guided label flipping attack against federated learning-based intrusion detection in IoT

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