Crystal Structure Optimization with Deep-Autoencoder-Based Intrusion Detection for Secure Internet of Drones Environment

Abstract

Drone developments, especially small-sized drones, usher in novel trends and possibilities in various domains. Drones offer navigational inter-location services with the involvement of the Internet of Things (IoT). On the other hand, drone networks are highly prone to privacy and security risks owing to their strategy flaws. In order to achieve the desired efficiency, it is essential to create a secure network. The purpose of the current study is to have an overview of the privacy and security problems that recently impacted the Internet of Drones (IoD). An Intrusion Detection System (IDS) is an effective approach to determine the presence of intrusions in the IoD environment. The current study focuses on the design of Crystal Structure Optimization with Deep-Autoencoder-based Intrusion Detection (CSODAE-ID) for a secure IoD environment. The aim of the presented CSODAE-ID model is to identify the occurrences of intrusions in IoD environment. In the proposed CSODAE-ID model, a new Modified Deer Hunting Optimization-based Feature Selection (MDHO-FS) technique is applied to choose the feature subsets. At the same time, the Autoencoder (AE) method is employed for the classification of intrusions in the IoD environment. The CSO algorithm, inspired by the formation of crystal structures based on the lattice points, is employed at last for the hyperparameter-tuning process. To validate the enhanced performance of the proposed CSODAE-ID model, multiple simulation analyses were performed and the outcomes were assessed under distinct aspects. The comparative study outcomes demonstrate the superiority of the proposed CSODAE-ID model over the existing techniques.

Risks of Drone Use in Light of Literature Studies

“…Because they are autonomous devices, the issue of cyber security, therefore, becomes a critical research aspect. Cyberattacks threaten both small, commercial drones used for daily activities [ 242 , 243 ] and drone networks [ 239 , 240 , 241 ]. Indeed, vulnerabilities in security systems can be exploited by third parties to launch an attack [ 244 ].…”

A Novel Intrusion Detection System Based on Artificial Neural Network and Genetic Algorithm With a New Dimensionality Reduction Technique for UAV Communication

“…The study in [25] designs a method for detecting intrusions in an Internet of Drones (IoD) network using crystal structure optimization with deep autoencoders based intrusion detection (CSODAE-ID). The proposed CSODAE-ID model employs a novel Modified Deer Hunting Optimization-based Feature Selection (MDHO-FS) method to choose the feature subsets and the Autoencoder (AE) approach to classify attacks in the IoD environment.…”

Detecting Unbalanced Network Traffic Intrusions With Deep Learning

“…This study presents a CGAN-based collaborative intrusion detection approach for UAV networks, employing a blockchain-empowered distributed federated learning framework [20]. Additionally, a data normalization technique is proposed for detecting cyberattacks on UAVs [21]. Furthermore, crystal structure optimization is conducted using a deep-autoencoder-based intrusion detection system for a secure Internet of Drones environment [22].…”

“…It converts the raw output scores into probabilities. (21) Where N is the number of classes, and xi is the raw output score for the i-th class.…”