Internet of Drones Intrusion Detection Using Deep Learning
Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET tec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/21/2633 |
| _version_ | 1797512666233700352 |
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| author | Rabie A. Ramadan Abdel-Hamid Emara Mohammed Al-Sarem Mohamed Elhamahmy |
| author_facet | Rabie A. Ramadan Abdel-Hamid Emara Mohammed Al-Sarem Mohamed Elhamahmy |
| author_sort | Rabie A. Ramadan |
| collection | DOAJ |
| description | Flying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches. |
| first_indexed | 2024-03-10T06:04:54Z |
| format | Article |
| id | doaj.art-b3a531e3ad334fce942644c3e96759ae |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T06:04:54Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b3a531e3ad334fce942644c3e96759ae2023-11-22T20:38:34ZengMDPI AGElectronics2079-92922021-10-011021263310.3390/electronics10212633Internet of Drones Intrusion Detection Using Deep LearningRabie A. Ramadan0Abdel-Hamid Emara1Mohammed Al-Sarem2Mohamed Elhamahmy3Computer Engineering Departmental, Faculty of Engineering, Cairo University, Giza 12613, EgyptDepartment of Computers and Systems Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11884, EgyptCollege of Computer Science and Engineering, Taibah University, Medina 41477, Saudi ArabiaHigher Institute of Computer Science and Information Systems, Fifth Settlement, Cairo 11477, EgyptFlying Ad Hoc Network (FANET) or drones’ technologies have gained much attraction in the last few years due to their critical applications. Therefore, various studies have been conducted on facilitating FANET applications in different fields. In fact, civil airspaces have gradually adopted FANET technology in their systems. However, FANET’s special roles made it complex to support emerging security threats, especially intrusion detection. This paper is a step forward towards the advances in FANET intrusion detection techniques. It investigates FANET intrusion detection threats by introducing a real-time data analytics framework based on deep learning. The framework consists of Recurrent Neural Networks (RNN) as a base. It also involves collecting data from the network and analyzing it using big data analytics for anomaly detection. The data collection is performed through an agent working inside each FANET. The agent is assumed to log the FANET real-time information. In addition, it involves a stream processing module that collects the drones’ communication information, including intrusion detection-related information. This information is fed into two RNN modules for data analysis, trained for this purpose. One of the RNN modules resides inside the FANET itself, and the second module resides at the base station. An extensive set of experiments were conducted based on various datasets to examine the efficiency of the proposed framework. The results showed that the proposed framework is superior to other recent approaches.https://www.mdpi.com/2079-9292/10/21/2633intrusion detectionFANETRNNLSTMdeep learning |
| spellingShingle | Rabie A. Ramadan Abdel-Hamid Emara Mohammed Al-Sarem Mohamed Elhamahmy Internet of Drones Intrusion Detection Using Deep Learning Electronics intrusion detection FANET RNN LSTM deep learning |
| title | Internet of Drones Intrusion Detection Using Deep Learning |
| title_full | Internet of Drones Intrusion Detection Using Deep Learning |
| title_fullStr | Internet of Drones Intrusion Detection Using Deep Learning |
| title_full_unstemmed | Internet of Drones Intrusion Detection Using Deep Learning |
| title_short | Internet of Drones Intrusion Detection Using Deep Learning |
| title_sort | internet of drones intrusion detection using deep learning |
| topic | intrusion detection FANET RNN LSTM deep learning |
| url | https://www.mdpi.com/2079-9292/10/21/2633 |
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