Named Entity Recognition for Drone Forensic Using BERT and DistilBERT

Abstract

The increase in UAV usage and popularity in many fields opens new opportunities and challenges. Many business sectors are benefiting from the UAV device employment. The wide range of drone implementation is varied, from business purposes to crime. Hence, further mechanisms are needed to deal with drone crime and attacks both administratively and technically. From a technical view, the security protocol is needed to keep the drone safe from various logical or physical attacks. In case a drone experiences incidents, a forensic protocol is needed to perform analysis and investigation to uncover the incident, understand the attack behavior, and mitigate the incident risk. Among the existing drone forensic research efforts, there is limited attempt to utilize specific drone artifacts to perform forensic analysis. Therefore, this paper investigates the potential of NER (Named Entity Recognition) as an initial step to perform information extraction from drone flight logs data. We use Transformers-based techniques to perform NER and assist the forensic investigation. BERT and DistilBERT pre-trained models are fine-tuned using the annotated data and get the F1 scores of 98.63% and of 95.9%, respectively.