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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Autores principales:	Rabie A. Ramadan, Abdel-Hamid Emara, Mohammed Al-Sarem, Mohamed Elhamahmy
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	intrusion detection FANET RNN LSTM deep learning Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/b3a531e3ad334fce942644c3e96759ae
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id	oai:doaj.org-article:b3a531e3ad334fce942644c3e96759ae
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spelling	oai:doaj.org-article:b3a531e3ad334fce942644c3e96759ae2021-11-11T15:38:48ZInternet of Drones Intrusion Detection Using Deep Learning10.3390/electronics102126332079-9292https://doaj.org/article/b3a531e3ad334fce942644c3e96759ae2021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2633https://doaj.org/toc/2079-9292Flying 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.Rabie A. RamadanAbdel-Hamid EmaraMohammed Al-SaremMohamed ElhamahmyMDPI AGarticleintrusion detectionFANETRNNLSTMdeep learningElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2633, p 2633 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intrusion detection FANET RNN LSTM deep learning Electronics TK7800-8360
spellingShingle	intrusion detection FANET RNN LSTM deep learning Electronics TK7800-8360 Rabie A. Ramadan Abdel-Hamid Emara Mohammed Al-Sarem Mohamed Elhamahmy Internet of Drones Intrusion Detection Using Deep Learning
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.
format	article
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
title	Internet of Drones Intrusion Detection Using Deep Learning
title_short	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_sort	internet of drones intrusion detection using deep learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b3a531e3ad334fce942644c3e96759ae
work_keys_str_mv	AT rabiearamadan internetofdronesintrusiondetectionusingdeeplearning AT abdelhamidemara internetofdronesintrusiondetectionusingdeeplearning AT mohammedalsarem internetofdronesintrusiondetectionusingdeeplearning AT mohamedelhamahmy internetofdronesintrusiondetectionusingdeeplearning
_version_	1718434693385289728

Internet of Drones Intrusion Detection Using Deep Learning

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