Research Article
DoS Attacks Detection in the Network of Drones: An Efficient Decision Tree-Based Model
@INPROCEEDINGS{10.1007/978-3-031-33614-0_12, author={Tarek Gaber and Xin Fan Guo and Said Salloum}, title={DoS Attacks Detection in the Network of Drones: An Efficient Decision Tree-Based Model}, proceedings={Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings}, proceedings_a={BDTA}, year={2023}, month={5}, keywords={DoS attack Smart cities Machine learning Decision Tree Algorithm Latency}, doi={10.1007/978-3-031-33614-0_12} }- Tarek Gaber
Xin Fan Guo
Said Salloum
Year: 2023
DoS Attacks Detection in the Network of Drones: An Efficient Decision Tree-Based Model
BDTA
Springer
DOI: 10.1007/978-3-031-33614-0_12
Abstract
This study examines the detection of the denial of service (DoS) attacks on Wi-Fi-based unmanned aerial vehicles (UAV). The paper proposed an efficient DoS attack detection method based on Decision Tree classifier. The method consists of preprocessing, feature extraction, and DoS attack detection. The preprocessing was proved to save drones’ resources and improve the detection rate. The investigation of different classifiers, i.e., KNN, Random Forest, Logistic Regression, and Decision Tree, the latter was concluded to be the best in detecting DoS attacks of types of De-authentication and UDP/TCP flood within the shortest runtime. The evaluation further showed that proposed DoS detection method is better than the most related work where it achieved detection with F1-score of 0.989 and with the shortest latency.
