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Digital forensics for drone data – intelligent clustering using self organising maps

Version 3 2024-06-18, 17:48
Version 2 2024-05-30, 11:41
Version 1 2019-11-08, 17:37
conference contribution
posted on 2024-06-18, 17:48 authored by Sri Harsha MekalaSri Harsha Mekala, Zubair BaigZubair Baig
Drones or unmanned aerial vehicles (UAVs) have been rapidly adopted for a range of applications over the past decade. Considering their capabilities of information acquisition and surveillance for intelligence, and increasing access to the common public, have led to a rise in the threat associated with cyber crime associated with drones, in recent times. In order to mitigate the threats and to prevent cyber-crime, digital forensics on drone data is both critical as well as lacking in terms of efficacy studies. In this paper, we define a digital forensic methodology for analyzing drone data, and we propose a self organizing map (SOM)-based method for aiding such analysis. Experiments were conducted on two images obtained from the CFReDS project, namely, ArduPilot DIY Drone and DJI Phantom 4, with the purpose of producing admissible digital forensic evidence for the court of law, as part of a cyber-crime investigation. We also highlight the individual capabilities of several digital forensic tools based on experiments conducted on drone data.

History

Volume

1113

Pagination

172-189

Location

Melbourne, Vic.

Start date

2019-11-27

End date

2019-11-29

ISSN

1865-0929

eISSN

1865-0937

ISBN-13

9783030343538

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

Ram Mohan Doss R, Piramuthu S, Zhou W

Title of proceedings

FNSS 2019 : Proceedings of the 5th International Conference of Future Network Systems and Security

Event

Future Network Systemsand Security. International Conference (5th : 2019 : Melbourne, Victoria)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

Communications in Computer and Information Science