Smart City Gnosys
Smart city article details
| Title | Iot Empowered Smart Cybersecurity Framework For Intrusion Detection In Internet Of Drones |
|---|---|
| ID_Doc | 33786 |
| Authors | Ashraf S.N.; Manickam S.; Zia S.S.; Abro A.A.; Obaidat M.; Uddin M.; Abdelhaq M.; Alsaqour R. |
| Year | 2023 |
| Published | Scientific Reports, 13, 1 |
| DOI | http://dx.doi.org/10.1038/s41598-023-45065-8 |
| Abstract | The emergence of drone-based innovative cyber security solutions integrated with the Internet of Things (IoT) has revolutionized navigational technologies with robust data communication services across multiple platforms. This advancement leverages machine learning and deep learning methods for future progress. In recent years, there has been a significant increase in the utilization of IoT-enabled drone data management technology. Industries ranging from industrial applications to agricultural advancements, as well as the implementation of smart cities for intelligent and efficient monitoring. However, these latest trends and drone-enabled IoT technology developments have also opened doors to malicious exploitation of existing IoT infrastructures. This raises concerns regarding the vulnerability of drone networks and security risks due to inherent design flaws and the lack of cybersecurity solutions and standards. The main objective of this study is to examine the latest privacy and security challenges impacting the network of drones (NoD). The research underscores the significance of establishing a secure and fortified drone network to mitigate interception and intrusion risks. The proposed system effectively detects cyber-attacks in drone networks by leveraging deep learning and machine learning techniques. Furthermore, the model's performance was evaluated using well-known drones’ CICIDS2017, and KDDCup 99 datasets. We have tested the multiple hyperparameter parameters for optimal performance and classify data instances and maximum efficacy in the NoD framework. The model achieved exceptional efficiency and robustness in NoD, specifically while applying B-LSTM and LSTM. The system attains precision values of 89.10% and 90.16%, accuracy rates up to 91.00–91.36%, recall values of 81.13% and 90.11%, and F-measure values of 88.11% and 90.19% for the respective evaluation metrics. © 2023, The Author(s). |
| Author Keywords |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
36064  | 0.888 | Alfahaid A.; Alalwany E.; Almars A.M.; Alharbi F.; Atlam E.; Mahgoub I. | Machine Learning-Based Security Solutions For Iot Networks: A Comprehensive Survey | Sensors, 25, 11 (2025) |
| 23748 | 0.879 | Gomes D.; Hasan M.; Philip S.R. | Enhancing Capabilities And Security Features Of Drone Networks Through Machine Learning: A Comprehensive Overview | Advances in Science, Technology and Innovation, Part F372 (2025) |
| 47811 | 0.879 | Baig Z.; Syed N.; Mohammad N. | Securing The Smart City Airspace: Drone Cyber Attack Detection Through Machine Learning | Future Internet, 14, 7 (2022) |
| 47589 | 0.876 | Ntizikira E.; Lei W.; Alblehai F.; Saleem K.; Lodhi M.A. | Secure And Privacy-Preserving Intrusion Detection And Prevention In The Internet Of Unmanned Aerial Vehicles | Sensors, 23, 19 (2023) |
| 36080 | 0.874 | Ahanger T.A.; Ullah I.; Algamdi S.A.; Tariq U. | Machine Learning-Inspired Intrusion Detection System For Iot: Security Issues And Future Challenges | Computers and Electrical Engineering, 123 (2025) |
| 21066 | 0.87 | Nguyen H.P.D.; Nguyen D.D. | Drone Application In Smart Cities: The General Overview Of Security Vulnerabilities And Countermeasures For Data Communication | Studies in Systems, Decision and Control, 332 (2021) |
| 33508 | 0.87 | Saini K.S.; Chaudhary S. | Investigation On Attack Detection In Iot Networks: A Study And Analysis Of The Existing Machine Learning And Deep Learning Techniques | 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025 (2025) |
| 50770 | 0.868 | Veerappan C.S.; Loh P.K.K.; Chennattu R.J. | Smart Drone Controller Framework—Toward An Internet Of Drones | Studies in Computational Intelligence, 1002 (2022) |
| 41565 | 0.866 | Zhukabayeva T.; Ahmad Z.; Adamova A.; Karabayev N.; Mardenov Y.; Satybaldina D. | Penetration Testing And Machine Learning-Driven Cybersecurity Framework For Iot And Smart City Wireless Networks | IEEE Access, 13 (2025) |
| 24715 | 0.866 | Ali M.; Pervez S.; Hosseini S.E.; Siddhu M.K. | Evaluation And Detection Of Cyberattack In Iot-Based Smart City Networks Using Machine Learning On The Unsw-Nb15 Dataset | International Journal of Online and Biomedical Engineering, 21, 2 (2025) |