An Improved Transformer‐Based Model for Drone Forensic Analysis

In recent years, drones have become ubiquitous across various industries, raising concerns about their potential malfunctions. Investigating such malfunctions requires effective forensic analysis techniques. The purpose of this research is to enhance Transformer architecture to address the specific challenge of analyzing drone malfunctions using log data. The proposed method, Transformer for Forensic (TransFor), uses positional encoding and embedding scaling to enhance the Transformer’s ability to interpret drone log data, using which we can identify the chronological sequence of events that led to malfunctions. As a result, the model can detect patterns that indicate various types of problems or malfunctions. Experimental evaluations have confirmed that the modified Transformers outperform existing models in identifying drone malfunctions with great accuracy. Because of its inherent strengths, including the focus on positional information, the TransFor is capable of being used in forensic analyses of drone malfunctions.