Pengembangan Lini Masa Forensik Drone Melalui Analisis Artefak Penerbangan

Zidan, Muhammad Zien (2025) Pengembangan Lini Masa Forensik Drone Melalui Analisis Artefak Penerbangan. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Perkembangan pesat teknologi drone memperluas aplikasinya di berbagai sektor. Salah satu model drone yang paling popular digunakan adalah DJI. Drone sering kali disalahgunakan dalam kegiatan ilegal. Hal ini, menjadikan forensik digital menjadi sangat penting, terutama terkait pembangunan lini masa untuk mengidentifikasi jejak digital. Penelitian ini fokus pada pengembangan parser untuk file binary format TXT yang penyimpanan data log atau aktivitas drone DJI Mavic Pro pada tool Log2timeline Plaso. pada tool Log2timeline Plaso. Meskipun Plaso banyak digunakan untuk analisis lini masa forensik, tool ini belum mendukung pembacaan artefak dalam format binary TXT yang dihasilkan oleh controller drone Android dan iOS. Metode pengembangan melibatkan empat tahapan utama. Tahap pertama, penyusunan lini masa forensik menggunakan Docker image Plaso untuk memastikan artefak tersedia. Tahap kedua, mengkonfirmasi lokasi artefak binary TXT menggunakan Autopsy. Tahap ketiga, pola record dianalisis dengan HexEdit untuk memetakan struktur data penerbangan drone. Tahap keempat, pengembangan parser dilakukan dengan mengacu pada arsitektur Plaso yang
sepenuhnya berbasis Python guna mengekstrak data log atau aktivitas penerbangan. Parser yang dikembangkan mampu mengekstraksi enam atribut penting, yaitu waktu penerbangan,
longitude, latitude, height, distance dan speed. Hasil parser divalidasi melalui pengujian pada file log penerbangan binary TXT DJI Mavic Pro serta beberapa varian drone DJI lainnya yakni Mavic Air, Mavic 2, Mavic 2 Enterprise, dan Phantom 4. Dari validasi tersebut, didapatkan bahwa parser berhasil mengekstraksi data penerbangan dari varian drone DJI yang diuji.
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The rapid advancement of drone technology has expanded its applications across numerous sectors. DJI drones, in particular, are among the most widely deployed; however,
they are also frequently misused for illicit activities. Such misuse underscores the critical importance of digital forensics, especially timeline analysis to identify digital footprints. This study addresses that need by developing a parser for binary TXT files the flight-log storage
format of the DJI Mavic Pro within the Log2timeline Plaso tool. Although Plaso is a leading tool for forensic timeline generation, it lacks support for reading binary TXT artefacts produced by Android and iOS based controllers.
Our development methodology comprises four main phases. First, a forensic timeline environment is assembled using the Plaso Docker image to guarantee artefact availability.
Second, the location of binary TXT artefacts is confirmed with Autopsy. Third, record patterns are examined using HexEdit to map the internal structure of the flight-log data. Fourth, the parser is implemented in Python, conforming to Plaso architectural conventions, to extract
relevant flight information. The resulting parser successfully extracts six essential attributes namely flight timestamp, longitude, latitude, altitude, distance, and speed. Validation tests were conducted on DJI Mavic Pro flight-log files and additional DJI variants, namely Mavic Air, Mavic 2, Mavic 2 Enterprise, and Phantom 4, demonstrating consistent and accurate data extraction across all models.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Mobile Forensic, Binary TXT, Drone Forensic, Forensic Timeline, Log2timeline plaso, Binary TXT, Forensik Perangkat Bergerak, Forensik Drone, Lini masa Forensik, Log2timeline plaso.
Subjects:	Q Science > QA Mathematics > QA76.9.A25 Computer security. Digital forensic. Data encryption (Computer science)
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Muhammad Zien Zidan
Date Deposited:	27 Jul 2025 07:17
Last Modified:	27 Jul 2025 07:17
URI:	http://repository.its.ac.id/id/eprint/121748

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