Poisoning Attacks on Federated Learning-based IoT Intrusion Detection System
Open accessAbstract
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.In this paper, we show that FL-based IoT intrusion detection systems are vulnerable to backdoor attacks.We present a novel data poisoning attack that allows an adversary to implant a backdoor into the aggregated detection model to incorrectly classify malicious traffic as benign.We show that the adversary can gradually poison the detection model by only using compromised IoT devices (and not gateways/clients) to inject small amounts of malicious data into the training process and remain undetected.Our extensive evaluation on three real-world IoT datasets generated from 46 IoT devices shows the effectiveness of our attack in injecting backdoors and circumventing state of the art defenses against FL poisoning.Finally, we discuss shortly possible mitigation approaches.
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The defense algorithm proposed in [191] can be applied to multiple scenarios.
“Thus, the defense algorithm proposed in [191] can be applied to multiple scenarios.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [191]
A turning point exists after which the Clean ACC remains high and the Backdoor ACC reduces rapidly.
“Furthermore, a turning point exists after which the Clean ACC remains high and the Backdoor ACC reduces rapidly.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [191]
The backdoor attack success rate under the defense mechanism reduces to zero, while the stability-based defense can ensure a high success rate for the main task.
“The backdoor attack success rate under the defense mechanism reduces to zero, while the stability-based defense can ensure a high success rate for the main task.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [191]
The turning points between Clean ACC and Backdoor ACC are similar among different types of backdoor attacks.
“This work discusses that the turning points between Clean ACC and Backdoor ACC are similar among different types of backdoor attacks.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [191]
The probability that the trained model correctly classifies the normal inputs (Clean ACC) is proportional to the probability that the trained model misclassifies the backdoored inputs (Backdoor ACC).
“It has been discovered that the probability that the trained model correctly classifies the normal inputs (Clean ACC) is proportional to the probability that the trained model misclassifies the backdoored inputs (Backdoor ACC).”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [191]
In the experimental setup of [191], the number of participants is set to 10, and the training data are IID.
“In the experimental setup, the number of participants is set to 10, and the training data are IID.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [191]
Mixing a small number of malicious updates with numerous benign updates can avoid server detection, but the server can still identify the attack by comparing historical updates from the client.
“Although Thien et al. [80] propose that it can mix a small number of malicious updates with numerous benign updates to avoid server detection, the server can still identify the attack by comparing historical updates from the client.”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [80]
An attacker can learn characteristics of benign clients, such as controlling the Euclidean distance between malicious and benign models or controlling the poisoning data rate (PDR), to reduce detection risk.
“First, the attacker can learn the characteristics of benign clients, such as controlling the Euclidean distance between the malicious and benign models [28] or controlling the poisoning data rate (PDR) [80] to reduce the risk of detection by the server.”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [80]
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- Authors
- Nguyen, T. D., Rieger, P., Miettinen, M., & Sadeghi, A.-R.
- Affiliations
- Technische Universität Darmstadt (DE)
- Venue
- Proceedings 2020 Workshop on Decentralized IoT Systems and Security
- Publisher
- Internet Society
- Published
- 2020
- OpenAlex
- W3030742901
- Cited by
- 200
- References
- 35
- Citations by year
- 2026: 13 · 2025: 35 · 2024: 54 · 2023: 38 · 2022: 36 · 2021: 19 · 2020: 5
- FWCI
- 9.36
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Workshop on Decentralized IoT Systems and Security (DISS) (publishedVersion)
- TUbilio (Technical University of Darmstadt) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (SFB 1119, 236615297)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- federated learning, data poisoning, backdoor attacks, IoT intrusion detection, privacy-preserving machine learning, malicious traffic classification, attack mitigation
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