Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 7; p. 2630
• Main Authors: Aldaej, Abdulaziz, Ahanger, Tariq Ahamed, Atiquzzaman, Mohammed, Ullah, Imdad, Yousufudin, Muhammad
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.03.2022; MDPI
• Subjects: Access control; Artificial intelligence; Blockchain; Communication; Computer Security; Computer software industry; Cybersecurity; Cyberterrorism; Data integrity; Data security; Drones; Internet; Internet of Things; Machine Learning; Military aspects; Military deployment; Privacy; Reproducibility of Results; Safety and security measures; security; Sensors; Smart cities; Surveillance; Unmanned Aerial Devices; Unmanned aerial vehicles; United States; Germany; security; drones; Internet of Things; machine learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22072630

Drone advancements have ushered in new trends and possibilities in a variety of sectors, particularly for small-sized drones. Drones provide navigational interlocation services, which are made possible by the Internet of Things (IoT). Drone networks, on the other hand, are subject to privacy and security risks due to design flaws. To achieve the desired performance, it is necessary to create a protected network. The goal of the current study is to look at recent privacy and security concerns influencing the network of drones (NoD). The current research emphasizes the importance of a security-empowered drone network to prevent interception and intrusion. A hybrid ML technique of logistic regression and random forest is used for the purpose of classification of data instances for maximal efficacy. By incorporating sophisticated artificial-intelligence-inspired techniques into the framework of a NoD, the proposed technique mitigates cybersecurity vulnerabilities while making the NoD protected and secure. For validation purposes, the suggested technique is tested against a challenging dataset, registering enhanced performance results in terms of temporal efficacy (34.56 s), statistical measures (precision (97.68%), accuracy (98.58%), recall (98.59%), F-measure (99.01%), reliability (94.69%), and stability (0.73).