Federated learning-based cognitive detection of jamming attack in flying Ad-Hoc network
Open accessAbstract
Flying Ad-hoc Network (FANET) is a decentralized communication system solely formed by Unmanned Aerial Vehicles (UAVs). In FANET, the UAV clients are vulnerable to various malicious attacks such as the jamming attack. The aerial adversaries in the jamming attack disrupt the communication of the victim network through interference on the receiver side. Jamming attack detection in FANET poses new challenges for its key differences from other ad-hoc networks. First, because of the varying communication range and power consumption constraints, any centralized detection system becomes trivial in FANET. Second, the existing decentralized solutions, disregarding the unbalanced sensory data from new spatial environments, are unsuitable for the highly mobile and spatially heterogeneous UAVs in FANET. Third, given a huge number of UAV clients, the global model may need to choose a sub-group of UAV clients for providing a timely global update. Recently, federated learning has gained attention, as it addresses unbalanced data properties besides providing communication efficiency, thus making it a suitable choice for FANET. Therefore, we propose a federated learning-based on-device jamming attack detection security architecture for FANET. We enhance the proposed federated learning model with a client group prioritization technique leveraging the Dempster-Shafer theory. The proposed client group prioritization mechanism allows the aggregator node to identify better client groups for calculating the global update. We evaluated our mechanism with datasets from publicly available standardized jamming attack scenarios by CRAWDAD and the ns-3 simulated FANET architecture and showed that, in terms of accuracy, our proposed solution (82.01% for the CRAWDAD dataset and 89.73% for the ns-3 simulated FANET dataset) outperforms the traditional distributed solution (49.11% for the CRAWDAD dataset and 65.62% for the ns-3 simulated FANET dataset). Moreover, the Dempster-Shafer-based client group prioritization mechanism identifies the best client groups out of 56 client group combinations for efficient federated averaging.
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The decentralized FL scheme in [53] achieves better accuracy and privacy protection over traditional centralized FL approaches.
“Experiments from practical datasets show the promising results of the proposed decentralized FL scheme with better accuracy and privacy protection over traditional centralized FL approaches.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [53]
The Abstract reports higher accuracy on the CRAWDAD and simulated FANET datasets than a traditional distributed model, and the Introduction says that sending only weight updates preserves local data privacy. But the paper does not show better accuracy or privacy than centralized FL: its proposed scheme uses a centralized controller and global aggregator, and its accuracy comparator is a distributed model, not traditional centralized FL. (LLM, conf=0.88)
Experimental — accuracy not yet measuredFL can be combined with differential privacy to improve the privacy of local updates by integrating it into gradient descent training for secure and robust FL sharing.
“Particularly, FL can be combined with other privacy techniques such as differential privacy [53] to improve the privacy of local updates, by integrating it into gradient descent training for enabling secure and robust FL sharing.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [53]
The Introduction says that sharing only weight updates preserves the privacy of UAVs’ local sensory data, and “B. Local Execution and Upload” describes gradient-based local training. Neither passage says that differential privacy is integrated into gradient descent or that it improves the privacy of shared updates; a search for “differential privacy” found no such discussion. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFL is suitable for malicious attacks detection in communication system composed by Unmanned Aerial Vehicles (UAVs).
“Apart from image detection and representation, FL is suitable for malicious attacks detection in communication system composed by Unmanned Aerial Vehicles (UAVs) (Mowla et al., 2020).”
From A review of applications in federated learning (2020) · cites this paper as Mowla et al., 2020
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- Authors
- Mowla, N. I., Tran, N. H., Doh, I., & Chae, K.
- Affiliations
- Ewha Womans University (KR)
- The University of Sydney (AU)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 4338-4350
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2997853417
- Cited by
- 147
- References
- 40
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 21 · 2023: 32 · 2022: 21 · 2021: 33 · 2020: 10
- FWCI
- 8.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea
- Korean Government (2019R1F1A1063194)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Mobile Ad Hoc Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Security in Wireless Sensor Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- jamming attack detection, flying ad hoc networks, unmanned aerial vehicles, federated learning, Dempster-Shafer theory, class imbalance, UAV security, communication interference
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