Authors: Miss. Kathula Lakshmi, Miss. Savarapu Suhasini
Abstract: The rapid adoption of Unmanned Aerial Vehicles (UAVs) in applications such as surveillance, precision agriculture, disaster response, logistics, and intelligent transportation has significantly increased the demand for secure and reliable communication networks. However, the wireless nature of UAV communication exposes these systems to a wide range of cyber threats, including GPS spoofing, data injection, denial-of-service (DoS), and network intrusion attacks, which can compromise mission integrity and operational safety. To address these security challenges, this paper presents a machine learning-based cyber attack detection framework for UAV communication networks. The proposed framework employs comprehensive data preprocessing, feature engineering, and intelligent classification techniques to analyze UAV telemetry data, communication signals, and operational parameters for identifying malicious activities. Multiple machine learning models are utilized to distinguish normal UAV behavior from cyber attack scenarios through behavioral pattern analysis and anomaly detection. The framework is evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, to assess its detection capability and reliability. Experimental results demonstrate that the proposed framework effectively detects various cyber attacks with high detection accuracy, low false positive rates, and efficient response time. By integrating intelligent machine learning algorithms into UAV cybersecurity, the proposed approach enhances communication security, improves system resilience, and supports the development of reliable and autonomous drone operations in dynamic network environments.
