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LiveBox: A Self-Adaptive Forensic-Ready Service for Drones

Yu, Yijun; Barthaud, Danny; Price, Blaine; Bandara, Arosha; Zisman, Andrea and Nuseibeh, Bashar (2019). LiveBox: A Self-Adaptive Forensic-Ready Service for Drones. IEEE Access (Early Access).

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DOI (Digital Object Identifier) Link: https://doi.org/10.1109/ACCESS.2019.2942033
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Abstract

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly expected to operate in spaces populated by humans while avoiding injury to people or damaging property. However, incidents and accidents can, and increasingly do, happen. Traditional investigations of aircraft incidents require on-board flight data recorders (FDRs); however, these physical FDRs only work if the drone can be recovered. A further complication is that physical FDRs are too heavy to mount on light drones, hence not suitable for forensic digital investigations of drone flights. In this paper, we propose a self-adaptive software architecture, LiveBox, to make drones both forensic-ready and regulation compliant. We studied the feasibility of using distributed technologies for implementing the LiveBox reference architecture. In particular, we found that updates and queries of drone flight data and constraints can be treated as transactions using decentralised ledger technology (DLT), rather than a generic time-series database, to satisfy forensic tamper-proof requirements. However, DLTs such as Ethereum, have limits on throughput (i.e. transactions-per-second), making it harder to achieve regulation-compliance at runtime. To overcome this limitation, we present a self-adaptive reporting algorithm to dynamically reduce the precision of flight data without sacrificing the accuracy of runtime verification. Using a real-life scenario of drone delivery, we show that our proposed algorithm achieves a 46% reduction in bandwidth without losing accuracy in satisfying both tamper-proof and regulation-compliant requirements.

Item Type: Journal Item
Copyright Holders: 2019 IEEE
Project Funding Details:
Funded Project NameProject IDFunding Body
Adaptive Security And Privacy (XC-11-004-BN)291652EC (European Commission): FP (inc.Horizon2020, ERC schemes)
SAUSE: Secure, Adaptive, Usable Software EngineeringEP/R013144/1 (previous: EP/R005095/1)EPSRC (Engineering and Physical Sciences Research Council)
The Drone IdentityNo 783287EU H2020 SESAR EngageKTN
Flying High: Shaping the Future of Drones in UK CitiesNot SetNESTA
Keywords: Unmanned Aerial Vehicles (Drones); Software Engineering; Self-Adaptive Systems; Forensic Readiness; Flight Data Recorders; Simulators; Unmanned Traffic Management
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 66694
Depositing User: Yijun Yu
Date Deposited: 20 Sep 2019 09:50
Last Modified: 20 Sep 2019 10:50
URI: http://oro.open.ac.uk/id/eprint/66694
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