Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications
Due to Internet of Drones (IoD) technology, drone networks have proliferated, transforming surveillance, logistics, and disaster management. Distributed Denial of Service (DDoS) attacks, malware infections, and communication abnormalities increase cybersecurity dangers to these networks, threatening...
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/46 |
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| author | Mohammad Aldossary Ibrahim Alzamil Jaber Almutairi |
| author_facet | Mohammad Aldossary Ibrahim Alzamil Jaber Almutairi |
| author_sort | Mohammad Aldossary |
| collection | DOAJ |
| description | Due to Internet of Drones (IoD) technology, drone networks have proliferated, transforming surveillance, logistics, and disaster management. Distributed Denial of Service (DDoS) attacks, malware infections, and communication abnormalities increase cybersecurity dangers to these networks, threatening operational safety and efficiency. Current Intrusion Detection Systems (IDSs) fail to handle drone transmission data’s dynamic, high-dimensional nature, resulting in inadequate real-time anomaly identification and mitigation. This study presents the Cross-Layer Convolutional Attention Network (CLCAN), a new IDS architecture for IoD networks. CLCAN accurately detects complex cyber threats using multi-scale convolutional processing, hierarchical contextual attention, and dynamic feature fusion. Preprocessing methods like weighted differential scaling and gradient-based adaptive resampling improve data quality and reduce class imbalances. Contextual attribute transformation captures the nuanced network behaviors needed for anomaly identification. The proposed technique is shown to be necessary and effective by real-world drone communication dataset evaluations. CLCAN outperforms CNN, LSTM, and XGBoost with 98.4% accuracy, 98.7% recall, and 98.1% F1-score. The model has a remarkable AUC of 0.991. CLCAN can handle datasets of over 118,000 balanced data records in 85 s, compared to 180 s for comparable frameworks. This study pioneers a unified security solution for Drone-to-Drone (D2D) and Drone-to-Base Station (D2BS) communications, filling a crucial IoD security gap. It protects mission-critical drone operations with a strong, efficient, and scalable IDS from emerging cyber threats. |
| format | Article |
| id | doaj-art-005ec47996e94a3297ecf15ac39fc781 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-005ec47996e94a3297ecf15ac39fc7812025-01-24T13:29:46ZengMDPI AGDrones2504-446X2025-01-01914610.3390/drones9010046Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station CommunicationsMohammad Aldossary0Ibrahim Alzamil1Jaber Almutairi2Department of Computer Engineering and Information, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Al-Dawasir 11991, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Majmaah University, Majmaah 11952, Saudi ArabiaDepartment of Computer Science, College of Computer Science and Engineering, Taibah University, Al-Madinah 42353, Saudi ArabiaDue to Internet of Drones (IoD) technology, drone networks have proliferated, transforming surveillance, logistics, and disaster management. Distributed Denial of Service (DDoS) attacks, malware infections, and communication abnormalities increase cybersecurity dangers to these networks, threatening operational safety and efficiency. Current Intrusion Detection Systems (IDSs) fail to handle drone transmission data’s dynamic, high-dimensional nature, resulting in inadequate real-time anomaly identification and mitigation. This study presents the Cross-Layer Convolutional Attention Network (CLCAN), a new IDS architecture for IoD networks. CLCAN accurately detects complex cyber threats using multi-scale convolutional processing, hierarchical contextual attention, and dynamic feature fusion. Preprocessing methods like weighted differential scaling and gradient-based adaptive resampling improve data quality and reduce class imbalances. Contextual attribute transformation captures the nuanced network behaviors needed for anomaly identification. The proposed technique is shown to be necessary and effective by real-world drone communication dataset evaluations. CLCAN outperforms CNN, LSTM, and XGBoost with 98.4% accuracy, 98.7% recall, and 98.1% F1-score. The model has a remarkable AUC of 0.991. CLCAN can handle datasets of over 118,000 balanced data records in 85 s, compared to 180 s for comparable frameworks. This study pioneers a unified security solution for Drone-to-Drone (D2D) and Drone-to-Base Station (D2BS) communications, filling a crucial IoD security gap. It protects mission-critical drone operations with a strong, efficient, and scalable IDS from emerging cyber threats.https://www.mdpi.com/2504-446X/9/1/46drone networksintrusion detectioncybersecurityanomaly detectioncross-layer attentionmachine learning |
| spellingShingle | Mohammad Aldossary Ibrahim Alzamil Jaber Almutairi Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications Drones drone networks intrusion detection cybersecurity anomaly detection cross-layer attention machine learning |
| title | Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications |
| title_full | Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications |
| title_fullStr | Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications |
| title_full_unstemmed | Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications |
| title_short | Enhanced Intrusion Detection in Drone Networks: A Cross-Layer Convolutional Attention Approach for Drone-to-Drone and Drone-to-Base Station Communications |
| title_sort | enhanced intrusion detection in drone networks a cross layer convolutional attention approach for drone to drone and drone to base station communications |
| topic | drone networks intrusion detection cybersecurity anomaly detection cross-layer attention machine learning |
| url | https://www.mdpi.com/2504-446X/9/1/46 |
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