Drone forensic framework: Sensor and data identification and verification

Jain, U. C.; Rogers, Marcus; Matson, Eric T.

doi:10.1109/sas.2017.7894059

Cited by 42 publications

(18 citation statements)

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“…The results included in this paper have been obtained by using the popular drones' firmware ArduCopter, within the Ardupilot operating system. Thus, consistently with other recent work in the literature such as [17], beyond the 3DR SOLO drone used in this paper, our results are fully applicable also to over 30 products, including DJI and HobbyKing vehicles, to name a few 34 .…”

Section: Introductionsupporting

confidence: 91%

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

et al. 2020

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We propose Picking a Needle in a Haystack (PiNcH ), a methodology to detect the presence of a drone, its current status, and its movements by leveraging just the communication traffic exchanged between the drone and its Remote Controller (RC). PiNcH is built applying standard classification algorithms to the eavesdropped traffic, analyzing features such as packets inter-arrival time and size. PiNcH is fully passive and it requires just cheap and general-purpose hardware. To evaluate the effectiveness of our solution, we collected real communication traces originated by a drone running the widespread ArduCopter open-source firmware, currently mounted on-board of a wide range (30+) of commercial amateur drones. Then, we tested our solution against different publicly available wireless traces. The results prove that PiNcH can efficiently and effectively: (i) identify the presence of the drone in several heterogeneous scenarios; (ii) identify the current state of a powered-on drone, i.e., flying or lying on the ground; (iii) discriminate the movements of the drone; and, finally, (iv) enjoy a reduced upper bound on the time required to identify a drone with the requested level of assurance. The effectiveness of PiNcH has been also evaluated in the presence of both heavy packet loss and evasion attacks. In this latter case, the adversary modifies on purpose the profile of the traffic of the drone-RC link to avoid the detection. In both the cited cases, PiNcH continues enjoying a remarkable performance. Further, the comparison against state of the art solution confirms the superior performance of PiNcH in several scenarios. Note that all the drone-controller generated data traces have been released as open-source, to allow replicability and foster follow-up. Finally, the quality and viability of our solution, do prove that network traffic analysis can be successfully adopted for drone identification and status discrimination, and pave the way for future research in the area.

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Section: Introductionsupporting

confidence: 91%

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

et al. 2020

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“…To achieve that, Renduchintala et al visualized the UAV's flight information and provided a 3D representation of its path using Google maps. Another work by Jain et al [51] analyzed the UAVs' architecture and came with a generic drone forensic model. It consists of twelve linear phases that should be considered as a waterfall model, and include examining drone components such as camera, Wi-Fi, memory card, as well as operations like checking for customization and searching for paired devices.…”

Section: ) Blockchain-based Investigation Frameworkmentioning

confidence: 99%

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

Stoyanova

Nikoloudakis

Panagiotakis

et al. 2020

IEEE Commun. Surv. Tutorials

540

238

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Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multitenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users' right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.

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“…An interesting work by [24] compared the architecture of five selected commercial drones to highlight software incompatibility when replacing certain pieces of hardware component (e.g., gimbal, LED, propellers, etc.). This work holds a valuable contribution to UAV forensic investigators to identify anti-forensic techniques; however, there are several other advanced techniques through software and/or network protocols that pose complicated challenges in the field of UAV forensics.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges

Salamh

Karabiyik

Rogers

et al. 2021

Drones

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The raising accessibility of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, is rapidly increasing. Recent studies have discussed challenges that may come in tow with the growing use of this technology. These studies note that in-depth examination is required, especially when addressing challenges that carry a high volume of software data between sensors, actuators, and control commands. This work underlines static and live digital evidence traceability challenges to further enhance the UAV incident response plan. To study the live UAV forensic traceability issues, we apply the `purple-teaming’ exercise on small UAVs while conducting UAV forensic examination to determine technical challenges related to data integrity and repeatability. In addition, this research highlights current static technical challenges that could pose more challenges in justifying the discovered digital evidence. Additionally, this study discusses potential drone anti-forensic techniques and their association with the type of use, environment, attack vector, and level of expertise. To this end, we propose the UAV Kill Chain and categorize the impact and complexity of all highlighted challenges based on the conducted examination and the presented scientific contribution in this work. To the best of our knowledge, there has not been any contribution that incorporates `Purple-Teaming’ tactics to evaluate UAV-related research in cybersecurity and digital forensics. This work also proposes a categorization model that classifies the discovered UAV static and live digital evidence challenges based on their complexity and impact levels

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Drone forensic framework: Sensor and data identification and verification

Cited by 42 publications

References 1 publication

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

PiNcH: An effective, efficient, and robust solution to drone detection via network traffic analysis

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

A Comparative UAV Forensic Analysis: Static and Live Digital Evidence Traceability Challenges

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