DFLER: Drone Flight Log Entity Recognizer to Support Forensic Investigation on Drone Device

DFLER is an open-source CLI-based tool developed using Python programming language and supported by a fine-tuned BERT model to perform named entity recognition on drone flight log data, specifically the log messages. The BERT model is hosted on the HuggingFace platform to make it publicly available and accessible. The tool expects decrypted DJI flight log files as input and generates a forensic report in a PDF containing a forensic timeline with the highlighted parts on the mentioned entities in the log messages. The generated file can be used as an attachment to a complete forensic report and help the forensic investigator pinpoint critical events on the constructed forensic timeline.

DFLER has three core features i.e., forensic timeline construction, entity recognition and forensic report generation. The needed input are flight log files that can be acquired from controller devices such as Android-based or iOS-based smartphone.

Files Structure

Here is the folder structure of DFLER. There will be more files in flight_logs and sample_output folder, as we prepared a number of decrypted flight log files for input.

.
├── flight_logs                 # Source evidence for input
│   ├── android
│   ├── ios
├── model                       # To store the NER model
├── outputs
│   ├── 20221128_201545         # Generated folder to store output files
├── sample_output               # Sample output of a successful execution.
│   ├── parsed                  # Parsed message found in flight log
├── .gitignore
├── config.json                 # Some configuration parameter
├── dfler.py                    # Main file to run
├── generate_report.py          # Function to generate the forensic report
├── LICENSE
├── parse.py                    # Function to parse the flight log message
├── README.md
└── requirements.txt            # List of dependency packages

How to run

Before starting the tool, make sure all the dependencies listed in requirements.txt are installed. To convert the generated HTML report file into PDF, we use the wkhtmltopdf engine. Therefore, make sure to install the engine first, and set the executable path in the config.json file accordingly. Usually, the executable path is in C:\\Program Files\\wkhtmltopdf\\bin\\wkhtmltopdf.exe path. While for a Linux-based system like Ubuntu, it is stored in /usr/bin/wkhtmltopdf folder by default.

We have provided several flight log files that we extracted from Android- and iOS-based controller devices of VTO Labs drone forensic image dataset for samples. To perform named entity recognition, we need the fine-tuned BERT model. The mode is hosted on a public Huggingface repository. Clone the repository by issuing the command:

git lfs install
git clone https://huggingface.co/swardiantara/droner

After finish downloading the model, copy the model file (pytorch_model.bin) to the model folder. Having all the dependency packages, engine, model and input files prepared, simply run the command python dfler.py to run the tools. The results of every step will be saved into /outputs/yyyymmdd_HHMMSS folder.

Note: Run the step sequentially to avoid error, because every step is depending on the previous step.

Sample Output

We provide a sample of output if all steps are running successfully. There will be several files resulted as listed in sample_output folder. The final output report file contains a forensic report along with an attached highlighted forensic timeline that is constructed from several flight log files. The highlighted timeline is expected to be able to support the forensic investigator in pinpointing mentioned entities. We have tested the tool both on Windows and Ubuntu Operating System. The tool is running smoothly, except for the generated PDF file, which resulting in a slightly different file.