DFLER: Drone Flight Log Entity Recognizer to support forensic investigation on drone device[Formula presented]

Swardiantara Silalahi; Tohari Ahmad; Hudan Studiawan

doi:10.1016/j.simpa.2022.100457

DFLER: Drone Flight Log Entity Recognizer to support forensic investigation on drone device[Formula presented]

Swardiantara Silalahi, Tohari Ahmad^*, Hudan Studiawan

^*Corresponding author for this work

Institut Teknologi Sepuluh Nopember

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

DFLER is an open-source CLI-based tool developed using Python and supported by a fine-tuned BERT model to perform named entity recognition on drone flight log data, specifically the log messages. This 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 highlighting parts of the mentioned entities in the log messages. The generated file is an attachment to a complete forensic report and helps forensic investigators pinpoint critical events on the constructed forensic timeline.

Original language	English
Article number	100457
Journal	Software Impacts
Volume	15
DOIs	https://doi.org/10.1016/j.simpa.2022.100457
Publication status	Published - Mar 2023

Keywords

Deep learning
Drone forensics
Forensic tools
Information extraction
Named entity recognition
Network infrastructure
Transformer

Access to Document

10.1016/j.simpa.2022.100457

Cite this

@article{6f7718eaadc345bd96f58c423569d5be,

title = "DFLER: Drone Flight Log Entity Recognizer to support forensic investigation on drone device[Formula presented]",

abstract = "DFLER is an open-source CLI-based tool developed using Python and supported by a fine-tuned BERT model to perform named entity recognition on drone flight log data, specifically the log messages. This 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 highlighting parts of the mentioned entities in the log messages. The generated file is an attachment to a complete forensic report and helps forensic investigators pinpoint critical events on the constructed forensic timeline.",

keywords = "Deep learning, Drone forensics, Forensic tools, Information extraction, Named entity recognition, Network infrastructure, Transformer",

author = "Swardiantara Silalahi and Tohari Ahmad and Hudan Studiawan",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2023",

month = mar,

doi = "10.1016/j.simpa.2022.100457",

language = "English",

volume = "15",

journal = "Software Impacts",

issn = "2665-9638",

publisher = "Elsevier BV",

}

TY - JOUR

T1 - DFLER

T2 - Drone Flight Log Entity Recognizer to support forensic investigation on drone device[Formula presented]

AU - Silalahi, Swardiantara

AU - Ahmad, Tohari

AU - Studiawan, Hudan

PY - 2023/3

Y1 - 2023/3

N2 - DFLER is an open-source CLI-based tool developed using Python and supported by a fine-tuned BERT model to perform named entity recognition on drone flight log data, specifically the log messages. This 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 highlighting parts of the mentioned entities in the log messages. The generated file is an attachment to a complete forensic report and helps forensic investigators pinpoint critical events on the constructed forensic timeline.

AB - DFLER is an open-source CLI-based tool developed using Python and supported by a fine-tuned BERT model to perform named entity recognition on drone flight log data, specifically the log messages. This 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 highlighting parts of the mentioned entities in the log messages. The generated file is an attachment to a complete forensic report and helps forensic investigators pinpoint critical events on the constructed forensic timeline.

KW - Deep learning

KW - Drone forensics

KW - Forensic tools

KW - Information extraction

KW - Named entity recognition

KW - Network infrastructure

KW - Transformer

UR - http://www.scopus.com/inward/record.url?scp=85144797158&partnerID=8YFLogxK

U2 - 10.1016/j.simpa.2022.100457

DO - 10.1016/j.simpa.2022.100457

M3 - Article

AN - SCOPUS:85144797158

SN - 2665-9638

VL - 15

JO - Software Impacts

JF - Software Impacts

M1 - 100457

ER -

DFLER: Drone Flight Log Entity Recognizer to support forensic investigation on drone device[Formula presented]

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this