Analisis Forensik Digital pada Sistem Drone terhadap Serangan Siber Menggunakan Platform Damn Vulnerable Drone

Ardhana, Alvin Rayhaan (2026) Analisis Forensik Digital pada Sistem Drone terhadap Serangan Siber Menggunakan Platform Damn Vulnerable Drone. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Meningkatnya penggunaan Unmanned Aerial Vehicles (UAV) atau drone di berbagai bidang belum diimbangi dengan penerapan mekanisme keamanan siber yang memadai, sehingga menciptakan peluang bagi berbagai serangan siber terhadap sistem drone. Ancaman keamanan terhadap drone tidak hanya berdampak pada kegagalan sistem, tetapi juga berpotensi menimbulkan risiko keselamatan dan kerugian material. Namun, dampak tersebut sering kali sulit dibedakan dari kegagalan teknis biasa, sehingga diperlukan investigasi forensik digital untuk mengidentifikasi dan membuktikan bahwa suatu drone telah mengalami serangan siber. Penelitian ini bertujuan untuk melakukan simulasi serangan siber pada sistem drone, menganalisis artefak digital yang dihasilkan melalui pendekatan forensik digital, serta memetakan korelasi antara teknik serangan dengan artefak digital yang ditinggalkan. Simulasi dilakukan dalam lingkungan terkontrol menggunakan platform Damn Vulnerable Drone (DVD) berbasis Software-in-the-Loop (SITL) yang dijalankan pada Docker di sistem operasi Kali Linux. Lima skenario serangan diujikan, yaitu Wi-Fi Analysis and Cracking, Waypoint Manipulation, Status Spoofing, Communication Link Flooding, dan Companion Computer Web UI Login Brute Force. Analisis forensik dilakukan terhadap artefak command history (.zsh_history), telemetry log MAVLink (mav.tlog), flight log ArduPilot (.BIN), dan network capture. Hasil penelitian menunjukkan bahwa setiap skenario serangan meninggalkan jejak forensik yang khas pada artefak yang berbeda. Tidak adanya mekanisme autentikasi pada protokol MAVLink menjadi celah utama yang dieksploitasi pada sebagian besar skenario serangan.
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The increasing use of Unmanned Aerial Vehicles (UAVs), or drones, in various fields has not been matched by the implementation of adequate cybersecurity mechanisms, thereby creating opportunities for various cyberattacks against drone systems. Security threats to drones not only result in system failures but also have the potential to cause safety risks and material losses. However, these impacts are often difficult to distinguish from ordinary technical failures, making digital forensic investigations necessary to identify and prove that a drone has been subjected to a cyberattack. This study aims to simulate cyberattacks on drone systems, analyze the resulting digital artifacts using a digital forensic approach, and map the correlation between attack techniques and the digital artifacts they leave behind. The simulations were conducted in a controlled environment using the Software-in-the-Loop (SITL)-based Damn Vulnerable Drone (DVD) platform running on Docker in the Kali Linux operating system. Five attack scenarios were tested, namely Wi-Fi Analysis and Cracking, Waypoint Manipulation, Status Spoofing, Communication Link Flooding, and Companion Computer Web UI Login Brute Force. Forensic analysis was performed on command history (.zsh_history), MAVLink telemetry logs (mav.tlog), ArduPilot flight logs (.BIN), and network captures. The results show that each attack scenario leaves distinctive forensic traces in different artifacts. The absence of an authentication mechanism in the MAVLink protocol is the primary vulnerability exploited in most of the attack scenarios.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Damn Vulnerable Drone, Forensik Digital, MAVLink, Serangan Siber, Unmanned Aerial Vehicle, Damn Vulnerable Drone, Cyberattack, Digital Forensics, MAVLink, Unmanned Aerial Vehicle
Subjects:	Q Science > QA Mathematics > QA76.9 Computer algorithms. Virtual Reality. Computer simulation. Q Science > QA Mathematics > QA76.9.A25 Computer security. Digital forensic. Data encryption (Computer science) U Military Science > UG1242 Drone aircraft--Control systems. (unmanned vehicle)
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Alvin Rayhaan Ardhana
Date Deposited:	26 Jul 2026 12:47
Last Modified:	26 Jul 2026 12:47
URI:	http://repository.its.ac.id/id/eprint/138090

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