Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration

• URL: http://arxiv.org/abs/2505.10585v1
• Date: Wed, 14 May 2025 15:04:04 GMT
• Title: Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration
• Authors: Azim Akhtarshenas, Ramin Toosi, David López-Pérez, Tohid Alizadeh, Alireza Hosseini,
• Abstract summary: Malicious Unmanned Aerial Vehicles (UAVs) present a significant threat to next-generation networks (NGNs)<n>This paper proposes an integrated (AE)-classifier system to detect malicious UAVs.
• Score: 4.119508697466852
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Malicious Unmanned Aerial Vehicles (UAVs) present a significant threat to next-generation networks (NGNs), posing risks such as unauthorized surveillance, data theft, and the delivery of hazardous materials. This paper proposes an integrated (AE)-classifier system to detect malicious UAVs. The proposed AE, based on a 4-layer Tri-orientated Spatial Mamba (TSMamba) architecture, effectively captures complex spatial relationships crucial for identifying malicious UAV activities. The first phase involves generating residual values through the AE, which are subsequently processed by a ResNet-based classifier. This classifier leverages the residual values to achieve lower complexity and higher accuracy. Our experiments demonstrate significant improvements in both binary and multi-class classification scenarios, achieving up to 99.8 % recall compared to 96.7 % in the benchmark. Additionally, our method reduces computational complexity, making it more suitable for large-scale deployment. These results highlight the robustness and scalability of our approach, offering an effective solution for malicious UAV detection in NGN environments.

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