TY - JOUR
AU - Rizwan Majeed
AU - Nurul Azma Abdullah
AU - Muhammad Faheem Mushtaq
AU - Muhammad Umer
AU - Michele Nappi
AB - Developments in drones have opened new trends and opportunities in different fields, particularly in small drones. Drones provide interlocation services for navigation, and this interlink is provided by the Internet of Things (IoT). However, architectural issues make drone networks vulnerable to privacy and security threats. It is critical to provide a safe and secure network to acquire desired performance. Small drones are finding new paths for progress in the civil and defense industries, but also posing new challenges for security and privacy as well. The basic design of the small drone requires a modification in its data transformation and data privacy mechanisms, and it is not yet fulfilling domain requirements. This paper aims to investigate recent privacy and security trends that are affecting the Internet of Drones (IoD). This study also highlights the need for a safe and secure drone network that is free from interceptions and intrusions. The proposed framework mitigates the cyber security threats by employing intelligent machine learning models in the design of IoT-aided drones by making them secure and adaptable. Finally, the proposed model is evaluated on a benchmark dataset and shows robust results. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
BT - MDPI AG
DA - 2021-11-25
DO - 10.3390/electronics10232926
N1 - This paper proposes IoT-aided cyber-security for drone-based networks using a voting ensemble of machine learning algorithms. This framework utilizes IoT-based data from sensors, drones, and network information to achieve security-level patterns and identify the security attacks using these patterns. With this framework, the model can identify attacks in the network data. The proposed framework is tested with the drone dataset and shows robust results in real-time cyber attack identification. The accuracy achieved by the model is 99.99%, which is greater than previous approaches. The accuracy, precision, recall, and F1-score are calculated to estimate the performance. The proposed LRRF model works by identifying attack types accurately and proves its generalizability and robustness. In the future, the proposed framework will be tested on other domains for intrusion detection.
N2 - Developments in drones have opened new trends and opportunities in different fields, particularly in small drones. Drones provide interlocation services for navigation, and this interlink is provided by the Internet of Things (IoT). However, architectural issues make drone networks vulnerable to privacy and security threats. It is critical to provide a safe and secure network to acquire desired performance. Small drones are finding new paths for progress in the civil and defense industries, but also posing new challenges for security and privacy as well. The basic design of the small drone requires a modification in its data transformation and data privacy mechanisms, and it is not yet fulfilling domain requirements. This paper aims to investigate recent privacy and security trends that are affecting the Internet of Drones (IoD). This study also highlights the need for a safe and secure drone network that is free from interceptions and intrusions. The proposed framework mitigates the cyber security threats by employing intelligent machine learning models in the design of IoT-aided drones by making them secure and adaptable. Finally, the proposed model is evaluated on a benchmark dataset and shows robust results. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021
T2 - MDPI AG
TI - Intelligent cyber-security system for iot-aided drones using voting classifier
UR - https://www.mdpi.com/2079-9292/10/23/2926/pdf
SN - 2079-9292
ER -