Supervised Machine Learning For Detecting Drop Attack in UAV Ad-hoc Network
- DOI
- 10.2991/978-94-6463-496-9_22How to use a DOI?
- Keywords
- UAV Ad-hoc Network (UANET); Supervised Machine Learning; Path Credibility Matrix; Node Credibility Matrix; Node Presence Matrix
- Abstract
UAV Ad hoc Networks (UANETs) play a critical role in applications that necessitate secure and resilient communication, including data collection and surveillance. UANETs encounter substantial security challenges as a result of their decentralized and dynamic characteristics. One such challenge is the potential for malicious nodes to disrupt operations through the discarding of packets. The Drop Attack Detection Algorithm (DADA-UANET), which utilises supervised machine learning to improve network security, is the subject of this research. A combination of Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbours (KNN) is utilised to differentiate between normal and malicious nodes efficiently. Our methodology incorporates an original implementation of linear regression to evaluate the credibility of nodes on a periodic basis by analysing their past actions. The experimental findings derived from a comparative analysis demonstrate that our approach attains an enhanced level of performance, surpassing established methodologies by as much as 92% in classification accuracy when LR and KNN are employed. The integration of DADA-UANET substantially enhances the robustness of unmanned aerial vehicle (UAV) communication in the face of advanced cyber threats, thereby guaranteeing more dependable functioning of vital applications.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Said Neciri AU - Noureddine Chaib AU - Chabane Djeddi PY - 2024 DA - 2024/08/31 TI - Supervised Machine Learning For Detecting Drop Attack in UAV Ad-hoc Network BT - Proceedings of the International Conference on Emerging Intelligent Systems for Sustainable Development (ICEIS 2024) PB - Atlantis Press SP - 286 EP - 297 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-496-9_22 DO - 10.2991/978-94-6463-496-9_22 ID - Neciri2024 ER -