Purpose Event recognition (ER) in drone flight logs is a task in forensic investigations to extract key events from unstructured log messages. However, drone flight logs often exhibit inconsistencies in punctuation and capitalization, which make traditional ER approaches unreliable. This paper aims to propose a novel noise-resistant method that can automate the process of finding key events and extract a brief insight about the investigated artifacts quickly. Design/methodology/approach The problem is modeled as named entity recognition (NER) to tackle both sentence boundary detection (SBD) and ER, simultaneously. A new drone flight log event detection data set is constructed and used to evaluate multiple pre-trained language models, including BERT, XLNET and RoBERTa. In addition, the authors introduce a noise-introducing oversampling technique to improve the model’s robustness against log structure inconsistencies. Findings The results demonstrate that the proposed noise-introducing oversampling technique successfully reduces the models’ sensitivity to the punctuation presence. Moreover, lowercasing the input logs naturally eliminates the challenge raised by the inconsistencies in capital letter usage. The proposed evaluation metric, Fused-F1 (F-F1), better captures the overall model’s performance by accounting for the hierarchical dependency between SBD and ER tasks, compared to the standard metric. To promote reproducibility, the authors release the data set, code and evaluation scripts in a public repository. Practical implications An open-source tool is released along with this paper as the implementation of the proposed framework. The tool is expected to be an intelligent assistive technology for drone forensic investigators in analyzing forensic timelines more efficiently. Originality/value To the best of the authors’ knowledge, this paper is the first attempt to apply NER-based modeling to perform ER in the drone forensics domain.
Silalahi et al. (Wed,) studied this question.