Abstract
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.
Highlights
Today, the Internet has become an important and correlative part of our life with a progressive increase in the number of devices connected to it, especially Internet of Things (IoT) devices
This study aims to provide an innovative distributed framework for drone intrusion detection based on deep learning (DL) techniques, namely, LSTM-Recurrent Neural Networks (RNN) architecture
RNN-LSTM is compared to Linear Regression (LR) and K-Nearest Neighbors (KNN) algorithms
Summary
The Internet has become an important and correlative part of our life with a progressive increase in the number of devices connected to it, especially Internet of Things (IoT) devices. Internet of Things (IoT) is one of the most recent technologies that connect devices through the Internet resulting in progressive development and supporting human lives, professions, and education [1,2]. IoT can deal with all types of connected devices in daily life, called the Internet of Everything (IoE) [3]. Internet of Drones (IoD) is constructed from IoT by exchanging “Things” with “Drones” while holding incomparable properties. IoD, which is defined by Gharibi et al [6] as a “layered network control architecture,” plays an essential role in the development of Unmanned Aerial Vehicles (UAVs). This paper uses the terms FANET, drones, and UAV interchangeably to refer to the same meaning
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