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Relevant bibliographies by topics / Drone forensics

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Academic literature on the topic 'Drone forensics'

Author: Grafiati

Published: 23 August 2023

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Journal articles on the topic "Drone forensics"

1

Al-Room, Khalifa, Farkhund Iqbal, Thar Baker, Babar Shah, Benjamin Yankson, Aine MacDermott, and Patrick C. K. Hung. "Drone Forensics." International Journal of Digital Crime and Forensics 13, no. 1 (January 2021): 1–25. http://dx.doi.org/10.4018/ijdcf.2021010101.

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Drones (a.k.a. unmanned aerial vehicles – UAV) have become a societal norm in our daily lives. The ability of drones capture high-quality photos from an aerial view and store and transmit such data presents a multi-facet problem. These actions possess privacy challenges to innocent users who can be spied on or drone owner's data which may be intercepted by a hacker. With all technological paradigms, utilities can be misused, and this is an increasing occurrence with drones. As a result, it is imperative to develop a novel methodological approach for the digital forensic analysis of a seized drone. This paper investigates six brands of drones commonly used in criminal activities and extracts forensically relevant data such as location information, captured images and videos, drones' flight paths, and data related to the ownership of the confiscated drone. The experimental results indicate that drone forensics would facilitate law enforcement in collecting significant information necessary for criminal investigations.

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Bouafif, Hana, Faouzi Kamoun, and Farkhund Iqbal. "Towards a Better Understanding of Drone Forensics." International Journal of Digital Crime and Forensics 12, no. 1 (January 2020): 35–57. http://dx.doi.org/10.4018/ijdcf.2020010103.

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Unmanned aerial vehicles (drones) have gained increased popularity as their innovative uses continue to expand across various fields. Despite their numerous beneficial uses, drones have unfortunately been misused, through many reported cases, to launch illegal and sometimes criminal activities that pose direct threats to individuals, organizations, public safety and national security. These threats have recently led law enforcement agencies and digital forensic investigators to pay special attention to the forensic aspects of drones. This important research topic, however, remains underexplored. This study aims to further explore drone forensics in terms of challenges, forensic investigation procedures and experimental results through a forensic investigation study performed on a Parrot AR drone 2.0. In this study, the authors present new insights on drone forensics in terms of forensic approaches, access to drone's digital containers and the retrieval of key information that can assist digital forensic investigators establish ownership, recuperate flight data and gain access to media files.

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Al-Dhaqm, Arafat, Richard A. Ikuesan, Victor R. Kebande, Shukor Razak, and Fahad M. Ghabban. "Research Challenges and Opportunities in Drone Forensics Models." Electronics 10, no. 13 (June 23, 2021): 1519. http://dx.doi.org/10.3390/electronics10131519.

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The emergence of unmanned aerial vehicles (also referred to as drones) has transformed the digital landscape of surveillance and supply chain logistics, especially in terrains where such was previously deemed unattainable. Moreover, the adoption of drones has further led to the proliferation of diverse drone types and drone-related criminality, which has introduced a myriad of security and forensics-related concerns. As a step towards understanding the state-of-the-art research into these challenges and potential approaches to mitigation, this study provides a detailed review of existing digital forensic models using the Design Science Research method. The outcome of this study generated in-depth knowledge of the research challenges and opportunities through which an effective investigation can be carried out on drone-related incidents. Furthermore, a potential generic investigation model has been proposed. The findings presented in this study are essentially relevant to forensic researchers and practitioners towards a guided methodology for drone-related event investigation. Ultimately, it is important to mention that this study presents a background for the development of international standardization for drone forensics.

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Alotaibi, Fahad Mazaed, Arafat Al-Dhaqm, Yasser D. Al-Otaibi, and Abdulrahman A. Alsewari. "A Comprehensive Collection and Analysis Model for the Drone Forensics Field." Sensors 22, no. 17 (August 29, 2022): 6486. http://dx.doi.org/10.3390/s22176486.

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Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain.

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Alotaibi, Fahad Mazaed, Arafat Al-Dhaqm, and Yasser D. Al-Otaibi. "A Novel Forensic Readiness Framework Applicable to the Drone Forensics Field." Computational Intelligence and Neuroscience 2022 (February 28, 2022): 1–13. http://dx.doi.org/10.1155/2022/8002963.

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The Drone Forensics (DRFs) field is a branch of digital forensics, which involves the identification, capture, preservation, reconstruction, analysis, and documentation of drone incidents. Several models have been proposed in the literature for the DRF field, which generally discusses DRF from a reactive forensic perspective; however, the proactive forensic perspective is missing. Therefore, this paper proposes a novel forensic readiness framework called Drone Forensics Readiness Framework (DRFRF) using the design science method. It consists of two stages: (i) proactive forensic stage and (ii) reactive forensic stage. It considers centralized logging of all events of all the applicants within the drone device in preparation for an examination. It will speed up gathering data when an investigation is needed, permitting the forensic investigators to handle the examination and analysis directly. Additionally, digital forensics analysts can increase the possible use of digital evidence while decreasing the charge of performing forensic readiness. Thus, both the time and cost required to perform forensic readiness could be saved. The completeness, logicalness, and usefulness of DRFRF were compared to those of other models already existing in the DRF domain. The results showed the novelty and efficiency of DRFRF and its applicability to the situations before and after drone incidents.

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Sihag, Vikas, Gaurav Choudhary, Pankaj Choudhary, and Nicola Dragoni. "Cyber4Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones." Drones 7, no. 7 (June 28, 2023): 430. http://dx.doi.org/10.3390/drones7070430.

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Cyber Security and forensics for Unmanned Aerial Vehicles (UAVs) pose unique requirements, solutions, and challenges. As UAVs become increasingly prevalent for legitimate and illegal use, ensuring their security and data integrity is important. Solutions have been developed to tackle these security requirements. Drone forensics enables the investigation of security incidents involving UAVs, aiding in identifying attackers or determining the cause of accidents. However, challenges persist in the domain of UAV security and forensics. This paper surveys drone threat models, security, and privacy aspects. In particular, we present the taxonomy of drone forensics for investigating drone systems and talk about relevant artifacts, tools, and benchmark datasets. While solutions exist, challenges such as evolving technology and complex operational environments must be addressed through collaboration, updated protocols, and regulatory frameworks to ensure drones’ secure and reliable operation. Furthermore, we also point out the field’s difficulties and potential future directions.

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Baig, Zubair, Majid Ali Khan, Nazeeruddin Mohammad, and Ghassen Ben Brahim. "Drone Forensics and Machine Learning: Sustaining the Investigation Process." Sustainability 14, no. 8 (April 18, 2022): 4861. http://dx.doi.org/10.3390/su14084861.

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Drones have been increasingly adopted to address several critical challenges faced by humanity to provide support and convenience . The technological advances in the broader domains of artificial intelligence and the Internet of Things (IoT) as well as the affordability of off-the-shelf devices, have facilitated modern-day drone use. Drones are readily available for deployment in hard to access locations for delivery of critical medical supplies, for surveillance, for weather data collection and for home delivery of purchased goods. Whilst drones are increasingly beneficial to civilians, they have also been used to carry out crimes. We present a survey of artificial intelligence techniques that exist in the literature in the context of processing drone data to reveal criminal activity. Our contribution also comprises the proposal of a novel model to adopt the concepts of machine learning for classification of drone data as part of a digital forensic investigation. Our main conclusions include that properly trained machine-learning models hold promise to enable an accurate assessment of drone data obtained from drones confiscated from a crime scene. Our research work opens the door for academics and industry practitioners to adopt machine learning to enable the use of drone data in forensic investigations.

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Li, Zhengxiong, Baicheng Chen, Xingyu Chen, Chenhan Xu, Yuyang Chen, Feng Lin, Changzhi Li, Karthik Dantu, Kui Ren, and Wenyao Xu. "Reliable Digital Forensics in the Air." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 2 (July 4, 2022): 1–25. http://dx.doi.org/10.1145/3534598.

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As the drone becomes widespread in numerous crucial applications with many powerful functionalities (e.g., reconnaissance and mechanical trigger), there are increasing cases related to misused drones for unethical even criminal activities. Therefore, it is of paramount importance to identify these malicious drones and track their origins using digital forensics. Traditional drone identification techniques for forensics (e.g., RF communication, ID landmarks using a camera, etc.) require high compliance of drones. However, malicious drones will not cooperate or even spoof these identification techniques. Therefore, we present an exploration for a reliable and passive identification approach based on unique hardware traits in drones directly (e.g., analogous to the fingerprint and iris in humans) for forensics purposes. Specifically, we investigate and model the behavior of the parasitic electronic elements under RF interrogation, a particular passive parasitic response modulated by an electronic system on drones, which is distinctive and unlikely to counterfeit. Based on this theory, we design and implement DroneTrace, an end-to-end reliable and passive identification system toward digital drone forensics. DroneTrace comprises a cost-effective millimeter-wave (mmWave) probe, a software framework to extract and process parasitic responses, and a customized deep neural network (DNN)-based algorithm to analyze and identify drones. We evaluate the performance of DroneTrace with 36 commodity drones. Results show that DroneTrace can identify drones with the accuracy of over 99% and an equal error rate (EER) of 0.009, under a 0.1-second sensing time budget. Moreover, we test the reliability, robustness, and performance variation under a set of real-world circumstances, where DroneTrace maintains accuracy of over 98%. DroneTrace is resilient to various attacks and maintains functionality. At its best, DroneTrace has the capacity to identify individual drones at the scale of 104 with less than 5% error.

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Stanković, Miloš, Mohammad Meraj Mirza, and Umit Karabiyik. "UAV Forensics: DJI Mini 2 Case Study." Drones 5, no. 2 (June 1, 2021): 49. http://dx.doi.org/10.3390/drones5020049.

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Rapid technology advancements, especially in the past decade, have allowed off-the-shelf unmanned aerial vehicles (UAVs) that weigh less than 250 g to become available for recreational use by the general population. Many well-known manufacturers (e.g., DJI) are now focusing on this segment of UAVs, and the new DJI Mini 2 drone is one of many that falls under this category, which enables easy access to be purchased and used without any Part 107 certification and Remote ID registration. The versatility of drones and drone models is appealing for customers, but they pose many challenges to forensic tools and digital forensics investigators due to numerous hardware and software variations. In addition, different devices can be associated and used for controlling these drones (e.g., Android and iOS smartphones). Moreover, according to the Federal Aviation Administration (FAA), the adoption of Remote ID is not going to be required for people without the 107 certifications for this segment at least until 2023, which creates finding personally identifiable information a necessity in these types of investigations. In this research, we conducted a comprehensive investigation of DJI Mini 2 and its data stored across multiple devices (e.g., SD cards and mobile devices) that are associated with the drone. The aim of this paper is to (1) create several criminal-like scenarios, (2) acquire and analyze the created scenarios using leading forensics software (e.g., Cellebrite and Magnet Axiom) that are commonly used by law enforcement agencies, (3) and present findings associated with potential criminal activities.

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Alhussan, Amel Ali, Arafat Al-Dhaqm, Wael M. S. Yafooz, Shukor Bin Abd Razak, Abdel-Hamid M. Emara, and Doaa Sami Khafaga. "Towards Development of a High Abstract Model for Drone Forensic Domain." Electronics 11, no. 8 (April 7, 2022): 1168. http://dx.doi.org/10.3390/electronics11081168.

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Drone Forensics (DRF) is one of the subdomains of digital forensics, which aims to capture and analyse the drone’s incidents. It is a diverse, unclear, and complex domain due to various drone field standards, operating systems, and infrastructure-based networks. Several DRF models and frameworks have been designed based on different investigation processes and activities and for the specific drones’ scenarios. These models make the domain more complex and unorganized among domain forensic practitioners. Therefore, there is a lack of a generic model for managing, sharing, and reusing the processes and activities of the DRF domain. This paper aims to develop A Drone Forensic Metamodel (DRFM) for the DRF domain using the metamodeling development process. The metamodeling development process is used for constructing and validating a metamodel and ensuring that the metamodel is complete and consistent. The developed DRFM consists of three main stages: (1) identification stage, (2) acquisition and preservation stage, and (3) examination and data analysis stage. It is used to structure and organize DRF domain knowledge, which facilitates managing, organizing, sharing, and reusing DRF domain knowledge among domain forensic practitioners. That aims to identify, recognize, extract and match different DRF processes, concepts, activities, and tasks from other DRF models in a developed DRFM. Thus, allowing domain practitioners to derive/instantiate solution models easily. The consistency and applicability of the developed DRFM were validated using metamodel transformation (vertical transformation). The results indicated that the developed DRFM is consistent and coherent and enables domain forensic practitioners to instantiate new solution models easily by selecting and combining concept elements (attribute and operations) based on their model requirement.

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Dissertations / Theses on the topic "Drone forensics"

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TENG, TAI-YU, and 鄧泰于. "The Interception and Forensic Research of the Drones." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/552a78.

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(11023221), Fahad Salamh. "A 3-DIMENSIONAL UAS FORENSIC INTELLIGENCE-LED TAXONOMY (U-FIT)." Thesis, 2021.

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Although many counter-drone systems such as drone jammers and anti-drone guns have been implemented, drone incidents are still increasing. These incidents are categorized as deviant act, a criminal act, terrorist act, or an unintentional act (aka system failure). Examples of reported drone incidents are not limited to property damage, but include personal injuries, airport disruption, drug transportation, and terrorist activities. Researchers have examined only drone incidents from a technological perspective. The variance in drone architectures poses many challenges to the current investigation practices, including several operation approaches such as custom commutation links. Therefore, there is a limited research background available that aims to study the intercomponent mapping in unmanned aircraft system (UAS) investigation incorporating three critical investigative domains---behavioral analysis, forensic intelligence (FORINT), and unmanned aerial vehicle (UAV) forensic investigation. The UAS forensic intelligence-led taxonomy (U-FIT) aims to classify the technical, behavioral, and intelligence characteristics of four UAS deviant actions --- including individuals who flew a drone too high, flew a drone close to government buildings, flew a drone over the airfield, and involved in drone collision. The behavioral and threat profiles will include one criminal act (i.e., UAV contraband smugglers). The UAV forensic investigation dimension concentrates on investigative techniques including technical challenges; whereas, the behavioral dimension investigates the behavioral characteristics, distinguishing among UAS deviants and illegal behaviors. Moreover, the U-FIT taxonomy in this study builds on the existing knowledge of current UAS forensic practices to identify patterns that aid in generalizing a UAS forensic intelligence taxonomy. The results of these dimensions supported the proposed UAS forensic intelligence-led taxonomy by demystifying the predicted personality traits to deviant actions and drone smugglers. The score obtained in this study was effective in distinguishing individuals based on certain personality traits. These novel, highly distinguishing features in the behavioral personality of drone users may be of particular importance not only in the field of behavioral psychology but also in law enforcement and intelligence.

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(10716420), Taegyu Kim. "Cyber-Physical Analysis and Hardening of Robotic Aerial Vehicle Controllers." Thesis, 2021.

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Robotic aerial vehicles (RAVs) have been increasingly deployed in various areas (e.g., commercial, military, scientific, and entertainment). However, RAVs’ security and safety issues could not only arise from either of the “cyber” domain (e.g., control software) and “physical” domain (e.g., vehicle control model) but also stem in their interplay. Unfortunately, existing work had focused mainly on either the “cyber-centric” or “control-centric” approaches. However, such a single-domain focus could overlook the security threats caused by the interplay between the cyber and physical domains.
In this thesis, we present cyber-physical analysis and hardening to secure RAV controllers. Through a combination of program analysis and vehicle control modeling, we first developed novel techniques to (1) connect both cyber and physical domains and then (2) analyze individual domains and their interplay. Specifically, we describe how to detect bugs after RAV accidents using provenance (Mayday), how to proactively find bugs using fuzzing (RVFuzzer), and how to patch vulnerable firmware using binary patching (DisPatch). As a result, we have found 91 new bugs in modern RAV control programs, and their developers confirmed 32 cases and patch 11 cases.

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Book chapters on the topic "Drone forensics"

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Atkinson, S., G. Carr, C. Shaw, and S. Zargari. "Drone Forensics: The Impact and Challenges." In Digital Forensic Investigation of Internet of Things (IoT) Devices, 65–124. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60425-7_4.

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Mantas, Evangelos, and Constantinos Patsakis. "GRYPHON: Drone Forensics in Dataflash and Telemetry Logs." In Advances in Information and Computer Security, 377–90. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26834-3_22.

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Dhamija, Rishi, Pavni Parghi, and Animesh Kumar Agrawal. "Bebop Drone GCS Forensics Using Open-Source Tools." In Algorithms for Intelligent Systems, 369–77. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5747-4_32.

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Barton, Thomas Edward Allen, and M. A. Hannan Bin Azhar. "Open Source Forensics for a Multi-platform Drone System." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 83–96. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73697-6_6.

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Mekala, Sri Harsha, and Zubair Baig. "Digital Forensics for Drone Data – Intelligent Clustering Using Self Organising Maps." In Communications in Computer and Information Science, 172–89. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34353-8_13.

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Parghi, Pavni, Rishi Dhamija, and Animesh Kumar Agrawal. "Innovative Approach to Onboard Media Forensic of a Drone." In IOT with Smart Systems, 307–14. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3945-6_30.

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Viswanathan, Sowmya, and Zubair Baig. "Digital Forensics for Drones: A Study of Tools and Techniques." In Applications and Techniques in Information Security, 29–41. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4706-9_3.

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Latzo, Tobias, Andreas Hellmich, Annika Knepper, Lukas Hardi, Tim Phillip Castello-Waldow, Felix Freiling, and Andreas Attenberger. "Maraudrone’s Map: An Interactive Web Application for Forensic Analysis and Visualization of DJI Drone Log Data." In Secure IT Systems, 329–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22295-5_18.

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Duraković, Adnan, Miodrag N. Simović, and Sabina Duraković. "UPOTREBA DOKAZA PRIKUPLJENIH DRONOVIMA U KRIMINALISTIČKIM ISTRAŽIVANJIMA." In DIGITALIZACIJA U KAZNENOM PRAVU I PRAVOSUĐU=Digitalization in Penal Law and Judiciary, 99–116. Institut za uporedno pravo; Institut za kriminološka i sociološka istraživanja, 2022. http://dx.doi.org/10.56461/zr_22.dukpp.08.

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Drones have an increasing role in criminal investigation, primarily in conducting investigations, but also in other criminal investigation activities, especially searching the scene after the event is over or monitoring event that is still active. The body conducting the investigation, which is first and foremost the police, must quickly and visibly secure the scene as well as all objects and traces on it. Search, recording and documentation, collection and analysis from the site must be performed without harassment and contamination. The entry of staff into the area carries risk and requires time, staff and complex activities. The seizure of the drone, as well as basic and forensic analysis of the drone and its contents is the basis for gathering evidence. In addition to drones, smartphones play a key role in this process because they are the basis for telephone - drone communication, and can be crucial for determining the status in flight, and lead to all products of drone activity - in the form of photos and videos. All this should shed light on the role of the user or owner of the drone if someone else has misused that communication and taken control over of the drone. Parts of the drone have unique markings and the analysis of physical components is carried out as part of the forensics of physical parts, as well as data generated during the flight that are analyzed as part of digital forensics. All this will enable the drone flight to be shown. On the other hand, adequate development of protection against illegal use of drones as well as investigations related to them implies monitoring and following the trends in this area. What is especially important to point out is that drone operations differ significantly in times of peace, crisis and war. The fight against the misuse of drones includes the use of all available means and methods, as well as the exploitation of all the weaknesses that drones in general and certain types of drones have. First of all, drone deactivation refers to the focus on the drone itself rather than on other components of the system such as remote control, communication and personnel operating the aircraft.

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Rafi, Sumbul, and Nasheed Imtiaz. "Cyberwar." In Advances in Digital Crime, Forensics, and Cyber Terrorism, 108–27. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6741-1.ch006.

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Invasion, terrorist attacks, drone strikes, insurgency and counterinsurgency, civil strife, and foreign intervention have already spread around the world, but war is now spreading and intensifying in a new form waged across digital networks: cyberwar. This war is fought through the use of computers and the internet. Today, the central part of organizational activities has been converted to digital format. Cybercriminals are continually developing new attack types, tools, and strategies that enable them to access settings that are more complicated or well-controlled, do more harm, and even remain undiscovered. This chapter discusses the history of cyber war, its emergence, and the different forms of cyberattacks and various repercussions of cybercrime, including its influence on the health of organizations, the psychological and social threats it poses to workers, and other outcomes. Cybercrime is defined and exposed by specialist literature.

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Conference papers on the topic "Drone forensics"

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Renduchintala, Ankit L. P. S., Abdulsahib Albehadili, and Ahmad Y. Javaid. "Drone Forensics: Digital Flight Log Examination Framework for Micro Drones." In 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2017. http://dx.doi.org/10.1109/csci.2017.15.

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Bouafif, Hana, Faouzi Kamoun, Farkhund Iqbal, and Andrew Marrington. "Drone Forensics: Challenges and New Insights." In 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS). IEEE, 2018. http://dx.doi.org/10.1109/ntms.2018.8328747.

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Kumar, Santosh, R. Senthil Kumar, Harshit Pant, Muskan Agrawal, and Ankita Tandon. "Drone Clean-Zilla." In 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS). IEEE, 2021. http://dx.doi.org/10.1109/fabs52071.2021.9702544.

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Yousef, Maryam, Farkhund Iqbal, and Mohammed Hussain. "Drone Forensics: A Detailed Analysis of Emerging DJI Models." In 2020 11th International Conference on Information and Communication Systems (ICICS). IEEE, 2020. http://dx.doi.org/10.1109/icics49469.2020.239530.

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Hamdi, Dua'a Abu, Farkhund Iqbal, Saiqa Alam, Abdulla Kazim, and Aine MacDermott. "Drone Forensics: A Case Study on DJI Phantom 4." In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2019. http://dx.doi.org/10.1109/aiccsa47632.2019.9035302.

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Akmalov, Artem E., Sergei K. Belenok, Anastasia Kadyrova, Veronika Kirillova, Vitaly A. Kostarev, Gennadii E. Kotkovskii, and Alexander A. Chistyakov. "Portable aerosol collector with liquid circulation mounted on a drone." In Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies IV, edited by Henri Bouma, Robert J. Stokes, Yitzhak Yitzhaky, and Radhakrishna Prabhu. SPIE, 2020. http://dx.doi.org/10.1117/12.2572653.

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Lan, James Kin Wah, and Frankie Kin Wah Lee. "Drone Forensics: A Case Study on DJI Mavic Air 2." In 2021 23rd International Conference on Advanced Communication Technology (ICACT). IEEE, 2021. http://dx.doi.org/10.23919/icact51234.2021.9370578.

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Yousef, Maryam, and Farkhund Iqbal. "Drone Forensics: A Case Study on a DJI Mavic Air." In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2019. http://dx.doi.org/10.1109/aiccsa47632.2019.9035365.

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Syed, Naeem, Majid Ali Khan, Nazeeruddin Mohammad, Ghassen Ben Brahim, and Zubair Baig. "Unsupervised Machine Learning for Drone Forensics through Flight Path Analysis." In 2022 10th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2022. http://dx.doi.org/10.1109/isdfs55398.2022.9800808.

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Lan, James Kin Wah, and Frankie Kin Wah Lee. "Drone Forensics: A Case Study on DJI Mavic Air 2." In 2022 24th International Conference on Advanced Communication Technology (ICACT). IEEE, 2022. http://dx.doi.org/10.23919/icact53585.2022.9728844.

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