Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection
The emergence of small-drone technology has revolutionized the way we use drones. Small drones leverage the Internet of Things (IoT) to deliver location-based navigation services, making them versatile tools for various applications. Unmanned aerial vehicle (UAV) communication networks and smart gri...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
|
| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1491332/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850107524955504640 |
|---|---|
| author | Raed Alharthi |
| author_facet | Raed Alharthi |
| author_sort | Raed Alharthi |
| collection | DOAJ |
| description | The emergence of small-drone technology has revolutionized the way we use drones. Small drones leverage the Internet of Things (IoT) to deliver location-based navigation services, making them versatile tools for various applications. Unmanned aerial vehicle (UAV) communication networks and smart grid communication protocols share several similarities, particularly in terms of their architecture, the nature of the data they handle, and the security challenges they face. To ensure the safe, secure, and reliable operation of both, it is imperative to establish a secure and dependable network infrastructure and to develop and implement robust security and privacy mechanisms tailored to the specific needs of this domain. The research evaluates the performance of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), CNN-LSTM, and convolutional long short-term memory (ConvLSTM), in detecting intrusions within UAV communication networks. The study utilizes five diverse and realistic datasets, namely, KDD Cup-99, NSL-KDD, WSN-DS, CICIDS 2017, and Drone, to simulate real-world intrusion scenarios. Notably, the ConvLSTM model consistently achieves an accuracy of 99.99%, showcasing its potential in securing UAVs from cyber threats. By demonstrating its superior performance, this work highlights the importance of tailored security mechanisms in safeguarding UAV technology against evolving cyber threats. Ultimately, this research contributes to the growing body of knowledge on UAV security, emphasizing the necessity of high-quality datasets and advanced models in ensuring the safe, secure, and reliable operation of UAV systems across various industries. |
| format | Article |
| id | doaj-art-2a2ad299325a46be90fedeaafa19a056 |
| institution | OA Journals |
| issn | 2296-598X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-2a2ad299325a46be90fedeaafa19a0562025-08-20T02:38:33ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-12-011210.3389/fenrg.2024.14913321491332Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detectionRaed AlharthiThe emergence of small-drone technology has revolutionized the way we use drones. Small drones leverage the Internet of Things (IoT) to deliver location-based navigation services, making them versatile tools for various applications. Unmanned aerial vehicle (UAV) communication networks and smart grid communication protocols share several similarities, particularly in terms of their architecture, the nature of the data they handle, and the security challenges they face. To ensure the safe, secure, and reliable operation of both, it is imperative to establish a secure and dependable network infrastructure and to develop and implement robust security and privacy mechanisms tailored to the specific needs of this domain. The research evaluates the performance of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM), CNN-LSTM, and convolutional long short-term memory (ConvLSTM), in detecting intrusions within UAV communication networks. The study utilizes five diverse and realistic datasets, namely, KDD Cup-99, NSL-KDD, WSN-DS, CICIDS 2017, and Drone, to simulate real-world intrusion scenarios. Notably, the ConvLSTM model consistently achieves an accuracy of 99.99%, showcasing its potential in securing UAVs from cyber threats. By demonstrating its superior performance, this work highlights the importance of tailored security mechanisms in safeguarding UAV technology against evolving cyber threats. Ultimately, this research contributes to the growing body of knowledge on UAV security, emphasizing the necessity of high-quality datasets and advanced models in ensuring the safe, secure, and reliable operation of UAV systems across various industries.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1491332/fullsmart gridunmanned aerial vehiclescommunication securityintrusion detectioncyber resilience |
| spellingShingle | Raed Alharthi Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection Frontiers in Energy Research smart grid unmanned aerial vehicles communication security intrusion detection cyber resilience |
| title | Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection |
| title_full | Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection |
| title_fullStr | Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection |
| title_full_unstemmed | Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection |
| title_short | Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection |
| title_sort | enhancing unmanned aerial vehicle and smart grid communication security using a convlstm model for intrusion detection |
| topic | smart grid unmanned aerial vehicles communication security intrusion detection cyber resilience |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1491332/full |
| work_keys_str_mv | AT raedalharthi enhancingunmannedaerialvehicleandsmartgridcommunicationsecurityusingaconvlstmmodelforintrusiondetection |