Abstract
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.
Highlights
The deployment of a group of Unmanned Aerial Vehicles (UAVs) is on the rise as they help to perform dangerous, dull, dirty and dumb tasks
We used the standard public datasets of the jamming attack in a vehicular ad-hoc network [33] and pre-processed them to extract 3000 instances with 100 features each associated with Received Signal Strength Indicator (RSSI) and Packet Delivery Rate (PDR)
We integrate an on-device jamming attack detection federated learning model devised in the UAV clients of the Flying Ad-hoc Network (FANET)
Summary
The deployment of a group of Unmanned Aerial Vehicles (UAVs) is on the rise as they help to perform dangerous, dull, dirty and dumb tasks. Federated learning allows lowlevel weight updates from the local devices to be sent and received from the global model [24], [25] This property of the federated learning can help in extracting the fine-grained properties of the jamming data instances to reduce the effect of the imbalance in the data faced by the UAVs in FANET. We address the need to identify the subgroup of UAV clients from a huge pool of UAV clients to provide the timely global update This becomes challenging as the UAV clients make varying contributions to the global model because of varying feature collection and associated training. In the case of our proposal based on federated learning, only weight updates are sent to the global model; and this effectively preserves the privacy of the local sensory data of the UAVs [25], [26].
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