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.

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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  -