Forensic Examination of Drones: A Comprehensive Study of Frameworks, Challenges, and Machine Learning Applications

Abstract

Unmanned Aerial Vehicles (UAVs) have evolved into necessary assets across various sectors, motivating a need for strong controllability technologies in applications like flight path enhancement and avoiding obstacles. This survey offers a comprehensive exploration of drone forensics, providing an extensive literature review on models and methodologies for examining malfunctions and attacks. Including key challenges, from machine learning applications to autopilot systems, the survey spans evidence collection techniques, incorporating neural network architectures like Transformers in forensic investigations. Real-world scenarios and forensic examination tools employed by law enforcement are discussed, illuminating the complex process of drone analysis, particularly in conflict areas. The paper delves into the role of machine learning in intrusion detection and attack classification, highlighting both challenges and recent advancements in drone detection. Outlining future research opportunities for the field of study, it highlights the importance of standardized methodologies in drone forensics. These research directions aim to overcome current obstacles and contribute to more effective solutions for detecting evasive malware. This investigation contributes valuable insights into the multifaceted landscape of drone forensics, offering a roadmap for ongoing research at the intersection of technology and law.

Keywords

• UAV
• drone forensics
• machine learning
• intrusion detection