Cyber Attack Detection Analysis Using Machine Learning for IoT-Based UAV Network
Haider, Usman Shoukat, Hina Ayub, Muhammad Yaseen Tashfeen, Muhammad Tehmasib Ali Bhatia, Tarandeep Kaur Khan, Inam Ullah
Haider, Usman
Shoukat, Hina
Ayub, Muhammad Yaseen
Tashfeen, Muhammad Tehmasib Ali
Bhatia, Tarandeep Kaur
Khan, Inam Ullah
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2024
Type
Book chapter
Genre
Keywords
Computer Science,Economics,Finance,Business & industry
Subjects (LCSH)
Citation
Haider, U., Shoukat, H., Ayub, M.Y., Tashfeen, M.T.A., Bhatia, T.K., & Khan, I.U. (2024). Cyber Attack Detection Analysis Using Machine Learning for IoT-Based UAV Network. In I.U. Khan, M. Ouaissa, M. Ouaissa, Z.A.E. Houda, & M.F. Ijaz (Eds.), Cyber Security for Next-Generation Computing Technologies (pp. 253-264). CRC Press. https://doi.org/10.1201/9781003404361-13
Abstract
Artificial intelligence helps to design better solutions to detect possible attacks. Intrusion detection systems using machine learning classifiers easily justify the trust factor in terms of accuracy. This chapter provides a comparative study of machine learning techniques to investigate the trust of IoT-based aerial ad hoc networks. Aerial vehicles move in three dimensions. Due to the highly mobile topological design of the UAV network, it is vulnerable. Intruders take advantage of the vulnerabilities and deploy cyber attacks to unbalance the overall network. The five machine learning techniques--XGBoost, naïve Bayes, complement naïve Bayes, ADA boost, and GaussianNB--are simulated using the UNSW-NB 15 dataset. For a detailed comparison, metrics like accuracy, precision, recall, F1-score, and support are used. Moreover, theoretical analysis of IDS using machine learning is also incorporated. Simulation results indicate that ADA boost performs better in comparison with the other techniques.
Table of Contents
Description
Click on the DOI link to access this book chapter (may not be free).
Publisher
CRC Press
Journal
Book Title
Series
Cyber Security for Next-Generation Computing Technologies