Drone Forensics: An Innovative Approach to the Forensic Investigation of Drone Accidents Based on Digital Twin Technology
Abstract
:1. Introduction
- We discuss the current challenges and limitations associated with drone forensic investigations.
- We provide an overview of the key aspects of digital twin technology as a proposed solution.
- We showcase how the proposed solution can be used to investigate the cause of an accident in a specific drone accident scenario and its impact on the drone through a simulation.
2. Literature Review
2.1. Related Work and Comparative Analysis
2.2. Challenges of Digital Forensics for Drones
2.2.1. Physical Challenges
- Hardware component fragility: Drones are typically made of lightweight materials that can be easily damaged or destroyed during the investigation process. The variety of the components, their interconnectedness, and their interactions with the environment all contribute to the drone forensic domain’s complexity [20].
- Small size: Finding drones can be difficult because they are often small and can be easily destroyed or hidden by suspects [9].
- Environmental factors such as temperature, humidity, and dust may make it challenging for investigators to access actual evidence, such as parts of crashed drones or damage from an unusual flight occurrence [17].
- Power supply: Drones can be utilized in isolated areas where power sources may be hard to obtain or nonexistent. As a result, drones may not be able to stay in the air long enough for investigators to gather all the necessary evidence because of their short battery lives [6].
- Transportation: As drones are frequently used in remote areas, it is more difficult for investigators to reach them, locate them, or transport them to a lab for analysis for a forensic investigation [6]. This also presents issues for establishing a chain of custody.
2.2.2. Legal Challenges
- Jurisdictional concerns: The recovery of evidence from drones can be made more difficult by the use of cloud-based storage systems because data may be kept in various locations [5].
- Privacy concerns, particularly when drones are used to collect data from individuals without their knowledge or consent or fly over restricted areas that may limit or restrict access to certain types of data or information during an investigation. In addition, drones can offer a more thorough view of a crime scene than conventional techniques, but they also pose special data security and privacy issues [8].
- Powers and permissions: Analyzing and storing these data securely may require specialized hardware and software and access to specialist tools and equipment, as well as specialized knowledge and expertise; these are requirements for drone forensics which not all investigators may have [8].
- Ethical considerations: There is a risk of unauthorized access to UAVs due to weak authentication protocols or a lack of encryption [15] which must be considered when conducting a drone forensic investigation, including ensuring that any data gathered are used only for the investigation and are not shared with third parties without consent, transparency about methods and findings, and compliance with laws.
- Lack of regulation: There are no laws or regulations governing the use of drones for this purpose in many countries, which can lead to potential legal issues if the drone is used improperly or without proper authorization [15]. Additionally, some countries have restrictions on where drones can be flown.
- Potential evidence contamination: Certain traces are extremely fragile and are quickly altered by environmental, animal, or human activities [8]. There is also a risk that using a drone could damage or destroy evidence at a crime scene if it is not operated properly.
2.2.3. Technical Challenges
- Preservation: Due to the intricacy of the technology and the requirement for specialized training, the use of UAVs in forensic investigations has a unique set of difficulties [8] which include data that can be easily corrupted or lost due to hardware or software failures, malicious attacks, or other external reasons.
- Collection: A thorough investigation into a crime or incident using a drone may not be possible given its small size and limited storage capacity. In addition, the drone may not be able to stay in the air long enough for investigators to gather all necessary evidence because of its short battery life, making it difficult to collect a large amount of data in a short amount of time [6].
- Analysis: To understand what transpired during a certain flight or event, it is often necessary to examine the various sorts of data that drones frequently hold, including pictures, videos, and flight logs. In addition, it is challenging to collect and evaluate data from drones due to the complexity of the data contained there and the lack of standardized data formats [10].
- Storage: Data kept on remote servers or in on a cloud server can present additional difficulties because investigators require access to these services [19]. It may be possible for users to delete or modify data stored on drones, making it more difficult for investigators to gather evidence properly.
- Recovery: To properly recover deleted or corrupted data from a drone’s memory card or other storage device, specialist tools may be needed. The investigations are made more difficult by the lack of tools, particularly those intended for digital forensics on drones [8].
- Complexity: Drones use a variety of different software platforms that are constantly being updated and changed. This can make it difficult to accurately capture and analyze all relevant data from a drone’s system during an investigation. In addition, finding evidence that can be utilized to recreate the events that took place during a drone’s operation is the most difficult part of drone digital forensics [9].
- Encryption: Data encryption can make it difficult or even impossible for digital forensic investigators to access encrypted data stored on or transmitted by a drone, which poses a substantial barrier for them when working with drones [13]. So, either the drone maker or the user must provide investigators with the encryption key or password in the beginning.
- Lack of consistency: A significant problem occurs when the data collected from drones are inconsistent. This makes it difficult to create a uniform set of digital forensics tools and methodologies for UAVs [9]. This is brought on by several elements, such as the kind of drone and its operating system. Moreover, comparing data from various sources might be challenging because different drones may use different data formats.
- Lack of standardization: This makes it challenging to create a consistent set of digital forensics methods and procedures for UAVs [20]. So, there is currently no standard protocol for the use of drones in digital forensic investigations, making it difficult to ensure consistency and accuracy in the collection and analysis of data.
- Cost: Forensic investigators are faced with an additional obstacle due to the quick development of drone technology and related components [22]. Accordingly, drone forensic investigations can be costly due to the need for specialized equipment and personnel to keep pace with this development.
- Limited battery life: Drones may not be able to stay in the air long enough for investigators to gather all necessary evidence [6]. This means that drones have a limited operational time before requiring recharge.
- Connections: The majority of drones are connected to other devices through Wi-Fi or Bluetooth, so gathering evidence from these connections presents an additional challenge [8], making it vulnerable to interception by malicious attackers.
- Big data: Drones are frequently fitted with numerous sensors, cameras, and other devices that produce a significant amount of data, which further increases the complexity of the digital forensic procedure [17].
- Training: Skills and specialized knowledge are needed for drone forensics. To properly examine drones, forensic investigators need to have a good understanding of the system’s architecture. Due to the complexity of drone hardware and software, they must be familiar with various drone types and their components [20].
- Open-source software: It may be challenging for forensic investigators to recognize the source code used on a specific drone. This also makes it difficult to identify any modifications that have been made, the origin of malicious payloads, and activity on drones [26].
- Limited computing power: Several drone models cannot save a significant amount of data because of their low computational capacity, which might make it challenging for digital forensics investigators to gather all pertinent information throughout an investigation [11].
- Quality of images: The challenge of obtaining high-quality images from drones is that the resolution of the camera and the distance from which it captures images can have a significant impact on the quality of photographs taken by drones [18].
2.3. Methods Used in Investigating Drone Accidents
- Forensic imaging entails making a complete copy of a drone’s storage media for further analysis.
- Network forensics involves analyzing network traffic to detect any suspicious activities associated with a drone.
- Memory analysis involves examining the volatile memory of a drone to uncover any relevant running processes or data.
- Mobile device forensics involves analyzing any mobile devices that may have been used to control or communicate with a drone.
Methods | Source | Advantages | Disadvantages |
---|---|---|---|
Forensic Imaging | [29] | Creates a comprehensive copy of all data, allowing for an accurate analysis. Can recover deleted data and examine essential metadata such as GPS coordinates and timestamps. Ensures that all evidence gathered during the investigation is admissible in court. | Demands specialized equipment and expertise and can be time-consuming. May not capture all relevant data. Requires physical access to the drone’s storage device. It may not be possible to recover all data from a damaged or destroyed drone. |
Network Forensics | [30] | Can detect suspicious network activity. Provides details about a drone’s communication with other devices. Helps track down perpetrators without requiring physical access to the drone or its components. | Requires access to network logs or other data from third-party devices. May not be useful if the drone was not connected to a network during the accident. May not capture all relevant data and may be incomplete or corrupted due to issues with network connectivity. Requires specialized expertise. |
Memory Analysis | [31] | Can provide information about running processes on the drone. Can recover deleted data from volatile memory. Can be used to identify malware or other malicious activity. | Requires specialized tools and expertise. Volatile memory is easily overwritten or lost if power is lost. May not be able to provide information about external factors that contributed to the accident. |
Mobile Device Forensics | [32] | Can provide information about control or communication with a drone. May contain valuable evidence such as GPS location data or text messages related to the incident. Does not require physical access to the drone itself. Can provide valuable information about the drone’s flight path, altitude, and speed. | Requires access to the mobile device. May require specialized tools and expertise. May require access to third-party data such as cloud backups or application usage logs. May be subject to tampering or hacking, which could compromise their integrity. The accuracy of the data obtained may be affected by factors such as signal strength and interference. |
3. Digital Twin Technology
3.1. Digital Twin Concept
3.2. Digital Twin Architecture
3.3. Characteristics of a Digital Twin
3.4. How Does a Digital Twin Work?
3.5. Potential Vulnerabilities
- Cybersecurity: The data contained within digital twins can be compromised in terms of their confidentiality and integrity due to cyberattacks [44].
- Data privacy: If digital twins are accessed by unauthorized individuals, the sensitive information they contain could be exploited for harmful intentions [45].
- Interoperability: The usefulness of digital twins can be limited if they are unable to communicate with other systems or devices [46].
- Lack of standardization: At present, there is a lack of uniformity in the creation and utilization of digital twins, which may result in irregularities and weaknesses in their construction and execution [47].
- Ethical issues: The utilization of digital twins gives rise to ethical issues regarding confidentiality and privacy [48].
- Reliability: The dependability and precision of digital twins rely on the excellence of the information they hold, which may be jeopardized by inaccuracies or deliberate tampering [49].
- Integration with legacy systems: Incorporating digital twins into pre-existing legacy systems can prove to be a difficult task as there may be disparities in technology, standards, and protocols [50].
4. The Rational behind Choosing the Digital Twin Technology
- It provides a secure and controlled environment for drone incident investigation. Investigators can interact with and modify the digital twin without risking further damage to the physical drone or its surroundings. This can help preserve evidence and prevent contamination of the accident site, ensuring a thorough investigation.
- It supports the real-time monitoring and analysis of drone behavior. The data transmitted from cameras and sensors on an actual drone allow investigators to monitor the drone’s movements and behavior during and before the accident. This can aid in identifying the potential causes of an accident and implementing preventive measures to improve safety.
- By creating a virtual replica of the drone and its surroundings, investigators can test hypotheses, simulate different scenarios, examine various theories regarding the causes of the crash and identify the most probable scenario. This can save time and resources compared to traditional methods.
5. Simulation Scenario and Implementation
5.1. Drone Accident Scenario
5.2. Implementation of the Simulation
5.2.1. Hardware Components
- The simulation environment was developed and tested on a Dell computer.
- The computer has a 2.20 GHz Intel Core 2.19 processor, 63.7 GB of memory, and the operating system Windows 10.
5.2.2. Software Components
5.2.3. Simulation Experiment
6. Results and Discussion
- Orientation data: The graph in Figure 10, shows the orientation of the drone in the X, Y, and Z dimensions over time. The X, Y, and Z dimensions are represented by the blue, red and green lines, respectively. The time (in seconds) is represented by the x-axis. The graph shows that the drone’s orientation remains relatively stable in the X, Z, and Y dimensions but then experiences significant variations, which could indicate the drone’s response to a simulated wind of 20 mph in the y-axis direction, as mentioned earlier. This indicates that the simulation is efficient and provides valuable data for analyzing the stability of the drone and the response to various simulation parameters [25,27].
- 2.
- Image quality: The image quality analysis was conducted by visual inspection, which involved visually comparing the two images side by side and identifying any differences in image quality, such as differences in sharpness, color accuracy, and noise levels. As shown in Figure 11, a photograph was taken before the drone was exposed to the wind, while in Figure 12, a photograph was taken while the drone was exposed to the wind. In addition, ImageJ software (version 1.54f) was used for image analysis, which involved analyzing the image contrast factor, for which the contrast of image 1 (after) was 0.849, while the contrast of image 2 (before) was 1.160. This suggests that image 2 (before) has greater contrast than image 1 (after). As shown in Figure 13, this suggested that it was of higher quality in terms of image clarity and sharpness, demonstrating the impact of the accident on the camera system and the effectiveness of simulation in proving it [7,28].
- 3.
- Sonar data: Sonar data collected during the experiment were used to identify potential hazards and obstacles in the environment that may have contributed to the accident, as well as to provide a detailed view to study the drone’s flight path during the simulation process. From launch to collision and crash, the simulation of sonar data provides valuable insights into the drone’s behavior and the factors that contributed to the accident. Figure 14, shows sonar data in which the red color represents the obstacles surrounding the drone and the green color represents the drone’s path.
7. Conclusions and Future Work
7.1. Conclusions
7.2. Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Unmanned Aircraft Systems (UAS). Unmanned Aircraft Systems (UAS)|Federal Aviation Administration. Available online: https://www.faa.gov/uas (accessed on 6 May 2023).
- Milner, M.N.; Rice, S.; Winter, S.R.; Anania, E.C. The effect of political affiliation on support for police drone monitoring in the United States. J. Unmanned Veh. Syst. 2019, 7, 129–144. [Google Scholar] [CrossRef]
- Georgiou, A.; Masters, P.; Johnson, S.; Feetham, L. UAV-assisted real-time evidence detection in outdoor crime scene investigations. J. Forensic Sci. 2022, 67, 1221–1232. [Google Scholar] [CrossRef] [PubMed]
- Emerging Tech Impact Radar: 2023. Gartner. (n.d.). Available online: https://www.gartner.com/en/doc/emerging-technologies-and-trends-impact-radar-excerpt (accessed on 6 May 2023).
- Bouafif, H.; Kamoun, F.; Iqbal, F.; Marrington, A. Drone forensics: Challenges and new insights. In Proceedings of the 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, France, 26–28 February 2018; IEEE: Piscataway, NJ, USA; pp. 1–6. [Google Scholar]
- Gülataş, İ.; Baktir, S. Unmanned aerial vehicle digital forensic investigation framework. J. Nav. Sci. Eng. 2018, 14, 32–53. [Google Scholar]
- Hassija, V.; Chamola, V.; Agrawal, A.; Goyal, A.; Luong, N.C.; Niyato, D.; Yu, F.R.; Guizani, M. Fast, reliable, and secure drone communication: A comprehensive survey. IEEE Commun. Surv. Tutor. 2021, 23, 2802–2832. [Google Scholar] [CrossRef]
- Sharma, B.K.; Chandra, G.; Mishra, V.P. Comparitive analysis and implication of UAV and AI in forensic investigations. In Proceedings of the 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates, 4–6 February 2019; IEEE: Piscataway, NJ, USA; pp. 824–827. [Google Scholar]
- Kao, D.Y.; Chen, M.C.; Wu, W.Y.; Lin, J.S.; Chen, C.H.; Tsai, F. Drone forensic investigation: DJI spark drone as a case study. Procedia Comput. Sci. 2019, 159, 1890–1899. [Google Scholar] [CrossRef]
- Bouafif, H.; Kamoun, F.; Iqbal, F. Towards a better understanding of drone forensics: A case study of parrot AR drone 2.0. Int. J. Digit. Crime Forensics (IJDCF) 2020, 12, 35–57. [Google Scholar] [CrossRef]
- Al-Room, K.; Iqbal, F.; Baker, T.; Shah, B.; Yankson, B.; MacDermott, A.; Hung, P.C. Drone forensics: A case study of digital forensic investigations conducted on common drone models. Int. J. Digit. Crime Forensics (IJDCF) 2021, 13, 1–25. [Google Scholar] [CrossRef]
- Al-Dhaqm, A.; Ikuesan, R.A.; Kebande, V.R.; Razak, S.; Ghabban, F.M. Research challenges and opportunities in drone forensics models. Electronics 2021, 10, 1519. [Google Scholar] [CrossRef]
- Salamh, F.E.; Karabiyik, U.; Rogers, M.K.; Matson, E.T. A comparative UAV forensic analysis: Static and live digital evidence traceability challenges. Drones 2021, 5, 42. [Google Scholar] [CrossRef]
- Stanković, M.; Mirza, M.M.; Karabiyik, U. UAV forensics: DJI mini 2 case study. Drones 2021, 5, 49. [Google Scholar] [CrossRef]
- Mekdad, Y.; Aris, A.; Babun, L.; Fergougui, A.E.; Conti, M.; Lazzeretti, R.; Uluagac, A.S. A survey on security and privacy issues of UAVs. arXiv 2021, arXiv:2109.14442. [Google Scholar] [CrossRef]
- Salamh, F.E.; Mirza, M.M.; Karabiyik, U. UAV forensic analysis and software tools assessment: DJI Phantom 4 and Matrice 210 as case studies. Electronics 2021, 10, 733. [Google Scholar] [CrossRef]
- Moon, H.; Jin, E.; Kwon, H.; Lee, S.; Gibum, K. Digital forensic methodology for detection of abnormal flight of drones. J. Inf. Secur. Cybercrimes Res. 2021, 4, 27–35. [Google Scholar] [CrossRef]
- Atkinson, S.; Carr, G.; Shaw, C.; Zargari, S. Drone forensics: The impact and challenges. Digit. Forensic Investig. Internet Things (IoT) Devices 2021, 65–124. [Google Scholar] [CrossRef]
- Alotaibi, F.M.; Al-Dhaqm, A.; Al-Otaibi, Y.D. A Novel Forensic Readiness Framework Applicable to the Drone Forensics Field. Comput. Intell. Neurosci. 2022, 2022, 8002963. [Google Scholar] [CrossRef]
- Alhussan, A.A.; Al-Dhaqm, A.; Yafooz, W.M.; Razak, S.B.A.; Emara, A.H.M.; Khafaga, D.S. Towards Development of a High Abstract Model for Drone Forensic Domain. Electronics 2022, 11, 1168. [Google Scholar] [CrossRef]
- Liang, G.; Xin, J.; Wang, Q.; Ni, X.; Guo, X. Research on IoT Forensics System Based on Blockchain Technology. Secur. Commun. Netw. 2022, 2022, 4490757. [Google Scholar] [CrossRef]
- Alotaibi, F.M.; Al-Dhaqm, A.; Al-Otaibi, Y.D.; Alsewari, A.A. A comprehensive collection and analysis model for the drone forensics field. Sensors 2022, 22, 6486. [Google Scholar] [CrossRef]
- Studiawan, H.; Ahmad, T.; Santoso, B.J.; Shiddiqi, A.M.; Pratomo, B.A. DroneTimeline: Forensic timeline analysis for drones. SoftwareX 2022, 20, 101255. [Google Scholar] [CrossRef]
- Siddiqi, M.A.; Iwendi, C.; Jaroslava, K.; Anumbe, N. Analysis on security-related concerns of unmanned aerial vehicle: Attacks, limitations, and recommendations. Math. Biosci. Eng. 2022, 19, 2641–2670. [Google Scholar] [CrossRef]
- Muthanna, A.; AAteya, A.; Khakimov, A.; Gudkova, I.; Abuarqoub, A.; Samouylov, K.; Koucheryavy, A. Secure and reliable IoT networks using fog computing with software-defined networking and blockchain. J. Sens. Actuator Netw. 2019, 8, 15. [Google Scholar] [CrossRef]
- Abro, G.E.M.; Zulkifli, S.A.B.; Masood, R.J.; Asirvadam, V.S.; Laouti, A. Comprehensive review of UAV detection, security, and communication advancements to prevent threats. Drones 2022, 6, 284. [Google Scholar] [CrossRef]
- Ko, Y.; Kim, J.; Duguma, D.G.; Astillo, P.V.; You, I.; Pau, G. Drone secure communication protocol for future sensitive applications in military zone. Sensors 2021, 21, 2057. [Google Scholar] [CrossRef] [PubMed]
- Uhlenkamp, J.F.; Hribernik, K.; Wellsandt, S.; Thoben, K.D. Digital Twin Applications: A first systemization of their dimensions. In Proceedings of the 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Valbonne Sophia-Antipolis, France, 17–19 June 2019; IEEE: Piscataway, NJ, USA; pp. 1–8. [Google Scholar]
- Akbal, E.; Dogan, S. Forensics image acquisition process of digital evidence. Int. J. Comput. Netw. Inf. Secur. 2018, 10, 1–8. [Google Scholar] [CrossRef]
- Qureshi, S.; Tunio, S.; Akhtar, F.; Wajahat, A.; Nazir, A.; Ullah, F. Network Forensics: A Comprehensive Review of Tools and Techniques. Int. J. Adv. Comput. Sci. Appl. 2021, 12. [Google Scholar] [CrossRef]
- Azzery, Y.; Mulyanto, N.D.; Hidayat, T. Memory Forensic Development and Challenges in Identifying Digital Crime: A Review. TEKNOKOM 2022, 5, 96–102. [Google Scholar] [CrossRef]
- Al-Dhaqm, A.; Abd Razak, S.; Ikuesan, R.A.; Kebande, V.R.; Siddique, K. A review of mobile forensic investigation process models. IEEE Access 2020, 8, 173359–173375. [Google Scholar] [CrossRef]
- Glaessgen, E.H.; Stargel, D.S. The digital twin paradigm for future NASA and US Air Force vehicles. In Proceedings of the AIAA Modeling and Simulation Technologies Conference, Grapevine, TX, USA, 9–13 January 2017. [Google Scholar]
- Tao, F.; Zhang, M.; Liu, X.; Nee, A.Y.C.; Li, X. Digital twin in industry: State-of-the-art. IEEE Trans. Ind. Inform. 2018, 15, 2405–2415. [Google Scholar] [CrossRef]
- Wang, W.; Li, X.; Xie, L.; Lv, H.; Lv, Z. Unmanned aircraft system airspace structure and safety measures based on spatial digital twins. IEEE Trans. Intell. Transp. Syst. 2021, 23, 2809–2818. [Google Scholar] [CrossRef]
- Redelinghuys AJ, H.; Basson, A.H.; Kruger, K. A six-layer architecture for the digital twin: A manufacturing case study implementation. J. Intell. Manuf. 2020, 31, 1383–1402. [Google Scholar] [CrossRef]
- Alam, K.M.; El Saddik, A. C2PS: A digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access 2017, 5, 2050–2062. [Google Scholar] [CrossRef]
- Kaul, R.; Ossai, C.; Forkan, A.R.M.; Jayaraman, P.P.; Zelcer, J.; Vaughan, S.; Wickramasinghe, N. The role of AI for developing digital twins in healthcare: The case of cancer care. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2023, 13, e1480. [Google Scholar] [CrossRef]
- Borowski, P.F. Digitization, digital twins, blockchain, and industry 4.0 as elements of management process in enterprises in the energy sector. Energies 2021, 14, 1885. [Google Scholar] [CrossRef]
- Wang, Y.; Chen, X.; Liang, Y.; Zhang, J. Digital twin-driven predictive maintenance for industrial equipment: A review. J. Manuf. Syst. 2020, 2020, 6129995. [Google Scholar]
- Nativi, S.; Mazzetti, P.; Craglia, M. Digital ecosystems for developing digital twins of the earth: The destination earth case. Remote Sens. 2021, 13, 2119. [Google Scholar] [CrossRef]
- Wang, Q.; Jiao, W.; Wang, P.; Zhang, Y. Digital twin for human-robot interactive welding and welder behavior analysis. IEEE/CAA J. Autom. Sin. 2020, 8, 334–343. [Google Scholar] [CrossRef]
- Wang, M.; Wang, C.; Hnydiuk-Stefan, A.; Feng, S.; Atilla, I.; Li, Z. Recent progress on reliability analysis of offshore wind turbine support structures considering digital twin solutions. Ocean. Eng. 2021, 232, 109168. [Google Scholar] [CrossRef]
- Alshammari, K.; Beach, T.; Rezgui, Y. Cybersecurity for digital twins in the built environment: Current research and future directions. J. Inf. Technol. Constr. 2021, 26, 159–173. [Google Scholar] [CrossRef]
- Son, S.; Kwon, D.; Lee, J.; Yu, S.; Jho, N.S.; Park, Y. On the design of a privacy-preserving communication scheme for cloud-based digital twin environments using blockchain. IEEE Access 2022, 10, 75365–75375. [Google Scholar] [CrossRef]
- Burns, T.; Cosgrove, J.; Doyle, F. A Review of Interoperability Standards for Industry 4.0. Procedia Manuf. 2019, 38, 646–653. [Google Scholar] [CrossRef]
- Palensky, P.; Cvetkovic, M.; Gusain, D.; Joseph, A. Digital twins and their use in future power systems. Digit. Twin 2022, 1, 4. [Google Scholar] [CrossRef]
- de Kerckhove, D. The personal digital twin, ethical considerations. Philos. Trans. R. Soc. A 2021, 379, 20200367. [Google Scholar] [CrossRef] [PubMed]
- Suhail, S.; Hussain, R.; Jurdak, R.; Hong, C.S. Trustworthy digital twins in the industrial internet of things with blockchain. IEEE Internet Comput. 2021, 26, 58–67. [Google Scholar] [CrossRef]
- da Silva Mendonça, R.; de Oliveira Lins, S.; de Bessa, I.V.; de Carvalho Ayres, F.A., Jr.; de Medeiros, R.L.P.; de Lucena, V.F., Jr. Digital twin applications: A survey of recent advances and challenges. Processes 2022, 10, 744. [Google Scholar] [CrossRef]
- Lu, Y.; Liu, C.; Kevin, I.; Wang, K.; Huang, H.; Xu, X. Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robot. Comput.-Integr. Manuf. 2020, 61, 101837. [Google Scholar] [CrossRef]
- Steindl, G.; Stagl, M.; Kasper, L.; Kastner, W.; Hofmann, R. Generic digital twin architecture for industrial energy systems. Appl. Sci. 2020, 10, 8903. [Google Scholar] [CrossRef]
- Chaudhary, G.; Khari, M.; Elhoseny, M. (Eds.) Digital Twin Technology; CRC Press: Boca Raton, FL, USA, 2021. [Google Scholar]
- Gill, P.; Kaur, M.; Singh, S. Drone Forensics: A Comprehensive Review on Digital Evidence Acquisition Techniques from Unmanned Aerial Vehicles (UAVs). Int. J. Adv. Comput. Sci. Appl. 2019, 10, 39–45. [Google Scholar]
- Li, L.; Aslam, S.; Wileman, A.; Perinpanayagam, S. Digital twin in aerospace industry: A gentle introduction. IEEE Access 2021, 10, 9543–9562. [Google Scholar] [CrossRef]
- Biesinger, F.; Weyrich, M. The facets of digital twins in production and the automotive industry. In Proceedings of the 2019 23rd International Conference on Mechatronics Technology (ICMT), Salerno, Italy, 23–26 October 2019; pp. 1–6. [Google Scholar]
- Alazab, M.; Khan, L.U.; Koppu, S.; Ramu, S.P.; Iyapparaja, M.; Boobalan, P.; Baker, T.; Maddikunta, P.K.R.; Gadekallu, T.R.; Aljuhani, A. Digital twins for healthcare 4.0-recent advances, architecture, and open challenges. IEEE Consum. Electron. Mag. 2022, 12, 29–37. [Google Scholar] [CrossRef]
R.P | Subject Matter | Methodologies Used | Results And Future Work | Challenges and Weaknesses |
---|---|---|---|---|
[5] | Drone forensics methods, access to a drone’s digital storage, and the retrieval of important data | Inspired by the general instructions from “NIST Special Publication 800-86” for examining artifacts forensically. | Increases knowledge in the field of drone forensics: access to the file system and retrieval of the Android ID of the controller. In future work, carry out more forensic examinations on the Parrot AR and its controller, in addition to other UAVs. | The ability to image a UAV camera forensically without compromising its integrity. There are more than five different file system types on a single UAV aircraft. The software and hardware for the drone have not yet been standardized. |
[6] | An increasing need for reliable digital forensic investigation frameworks for UAV | Created a framework for UAV DF investigation and applied it to the DJI Phantom III UAV. | A reliable framework for drone forensic investigation assists in conducting the forensic investigation methodically. | A lack of standards and sufficient details on the forensic investigation of drone incidents. |
[8] | The implications of using AI-enabled UAVs for forensic investigations | UAV and AI comparison and implications for forensic investigations. | AI-enabled UAVs can provide more accurate information than traditional methods, reduce the amount of time needed for an investigation by providing real-time data about a crime scene or location, and reduce costs. In the future, further explore these technologies as a means of improving the efficiency and accuracy of forensic investigations. | Preparing UAV investigators to handle UAVs securely throughout operations. The guidelines that drone operators follow differ according to different countries’ aviation rules. |
[9] | Gathering, fixing, and analyzing significant artifacts from flight data; investigating and assess the relationship between a drone, a mobile phone, and an SD card | Drone flight experiments to simulate a drone’s criminal behavior. | According to flight data logs, there is no proof linking a drone, SD card, and smartphone. Identified relational artifacts and temporal analysis rules. Future work will include additional drone-related experiments. | Limited access to certain components due to their small size and lack of standardization among different manufacturers’ drones. |
[10] | The current state of drone forensics, focusing on challenges and insights | The theory that file carving and a forensic examination of the important discrete digital containers of a drone can be combined into one logical process known as “drone forensics”. | The analysis added new perspectives to the knowledge already available on drone forensics and brought forth several characteristics and special difficulties. A futile analysis of the Parrot AR and its controller. | Lack of standardization and understanding about how drones store their data. |
[11] | Developing a new method for the digital forensic examination of drones | Examines six drone brands that are frequently utilized for illicit activity and gathers information that might be used in court. | Drone forensics might help law enforcement gather important data required for criminal investigations. Developed a detailed process model for conducting investigations in future work. | Lack of digital forensic tools that are already designed and cover all types of drones and how to handle the data gathered. |
[12] | Obtaining a digital forensic preparedness system and improve a proactive technique for DRFs | A comprehensive review of the existing drone forensic framework. | Proposed a model to investigate which obtains a digital forensic preparedness system and can be used to improve a proactive technique for DRFs. | DF tools are insufficient for investigations, even whenexist to extract data from drones. DF software, tools, and methodologies must be updated to consider continuous developments in drones. |
[13] | Tracking digital evidence, both live and static, in drones is forensic and makes the evidence identification process easier | The purple team technique was used, technical difficulties relating to digital evidence traceability were investigated, the integrity of recent digital forensic tools when conducting forensic analyses for drones was evaluated. | The suggested UAV Kill Chain can assist in resolving significant issues with UAV security and forensics. Plans call for the use of unencrypted links to expand this research. | Due to drones’ architectures and data flow, anti-drone detection and counter-forensics systems are complicated subjects. To perform additional research on different types of drones, it will be necessary to acquire more tools, equipment, and laboratory space, as well as a flight permit. |
[14] | Building several criminal-like drone scenarios and investigating them | Examined which approaches and standards work best when conducting investigations using DJI drones. | The DJI Mini2 has a maximum capacity greater than its weight, which could be exploited by bad actors in a variety of situations. Future work will focus on chip-off data extraction and analysis, how carrying different weights affects battery life, and OcuSync 2.0 transmission technologies. | Physical damage to the device, encryption, and limited access to internal components. |
[15] | Analyzing the current state of security and privacy issues related to UAVs | Discussed the security risks posed by UAVs and the privacy implications of UAVs. | Proposed various countermeasures against security risks and various measures to preserve privacy. Future work will develop effective countermeasures against potential threats as well as comprehensive legal frameworks. | Current laws are inadequate for addressing the security and privacy risks posed by UAVs. The need to develop more effective countermeasures against several potential threats. |
[16] | The potential for UAVs to provide detailed imagery and data that can be used to reconstruct events or identify suspects | Evaluated forensic tool products’ capabilities in depth, showed how to analyze recovered evidence, and examined the validity and dependability of retrieved digital evidence. | The DJI Phantom 4 and Matrice 210 UAVs are capable of providing high-quality imagery and data for forensic analysis purposes. Future work will examine the accuracy and dependability of additional artifacts extracted from UAVs. | No widely available instrument can perform a thorough forensic investigation of drones. Different drone processes and data structures, as well as a large amount of diverse data. |
[17] | Enhanced digital forensic techniques to identify drones flying unusually | Used a drone’s motor current and controller direction values. | The values of the two motors on the right side significantly rose during an unusual flight when the drone moved to the right owing to an outside force. In the future, these values will be used to figure out why this is happening. | Due to the limited number of DJI Phantom 4 Pro trials that were carried out, this conclusion cannot be generalized. The measurement data could be wrong. |
[18] | Investigating the present state of drone forensics and how it affects law enforcement and other stakeholders | Explored the various types of drones and their associated forensic challenges. | It is important to understand how drones can be used to commit crimes and how they can be tracked and identified to facilitate investigations. | Due to their small size and limited battery life, drones pose technical challenges when collecting evidence, a lack of clarity regarding how laws should be applied, and ethical considerations and privacy concerns. |
[19] | Development of a viable comprehensive framework preparedness for drone forensics | Used a design science research method. | The proposed DRFR framework consists of two levels: a proactive level and a reactive level to deal with drone crime from some pre- and post-incident perspectives. Future work will center on the implementation of the DRFRF in an actual case. | Due to a lack of specific rules or standards that handle incident response, the ISO/IEC 27043 investigative process classes are used as a general framework for incident response. |
[20] | Solving the DRF domain’s heterogeneity, interoperability, and difficulty problems | The design-science methodology was used to research “metamodeling”. | The DRFM model integrates the DRF models, processes, activities, and tasks and comprises three primary levels: the M2-Metamodel Level, the M1-DRF Model Level, and the M0-DRF User Data Model Level. In the future, work will focus on establishing a repository for the DRFM to store all relevant information related to the DRF field. | The lack of standardization in drone forensics models and the diversity of drone infrastructure are challenges. |
[21] | The security and privacy of IoT devices and networks | Reviewed IoT forensic systems using blockchain technology. | Ensuring the security of IOT devices by using distributed ledgers to trace transactions and smart contracts to store evidence. Future work will develop more effective solutions for privacy issues. | Scalability, privacy protection, interoperability between different blockchains, and legal considerations are challenges. |
[22] | A complete model for data gathering and processing in drone forensics | Adapted systematic methods of design science research. | Presented a CCAFM model which consists of 4 components: data acquisition, data extraction, data analysis, and reporting. Future work will examine the CCAFM model in practice. | Current methods are limited in their ability to collect evidence from drones. |
[23] | Examining the timeline of events related to drone operations | Described the architecture of DroneTimeline and the advantages of using it over existing methods, then presented a case study. | Proposed a new tool, “DroneTimeline.” Future work includes potential improvements in accuracy and scalability | Open-source forensic software ignores the timeline that might be gleaned from drone device file metadata. |
[26] | Complete knowledge of the recent developments that have brought about problems with UAVs | Discussed security risks, privacy concerns, and constraints with UAVs. | Provided potential solutions for security and privacy issues. | Constraints associated with security and privacy and safety are challenges. |
Characteristics | Description | Benefits | Examples |
---|---|---|---|
Real-Time Monitoring | This is achieved through the integration of various sources, such as sensors, cameras, and other IoT devices. | Allows for the early detection of potential issues or anomalies in a physical asset, enabling proactive maintenance and reducing downtime. Provides valuable insights into the performance and behavior of the asset, allowing for optimization and improvement. | In healthcare, digital twins can monitor patient vital signs or medical equipment to ensure timely interventions. For example, a DT of a cancer patient can continuously monitor their vital signs and alert healthcare providers if there are any abnormalities that require immediate attention [38]. |
Predictive Analytics | Includes the use of statistical algorithms and machine learning techniques to analyze data from sensors, IoT devices, historical data, and other sources and predict future outcomes. | Identifying potential problems before they occur, enabling proactive measures to prevent them from happening. Optimizing operations by identifying areas where improvements can be made. Reduces downtime by predicting when maintenance or repairs will be required. | In the energy sector, where DTs are used to monitor power plants and predict when maintenance will be required, energy companies can identify potential problems before they arise and take corrective action by examining historical data on equipment performance and environmental factors like temperature and humidity levels [39]. |
Simulation Capabilities | This is achieved through the use of advanced modeling and simulation techniques that allow digital twins to accurately replicate the behavior of physical systems. | Testing and refineing designs before they are implemented in the real world, reducing the risk of costly errors and delays. Optimizing performance by identifying areas for improvement and testing different scenarios to find the best solution. | Engineers can test new designs and improve existing ones using digital twins to simulate aircraft performance. For example, it has been discovered that the implementation of a UAV DT system can greatly enhance the safety performance of a UAV throughout its airspace flight [40]. |
Scalability | The capacity to handle an increasing system’s complexity and data volume; because of this, digital twins can change and adapt to the changing requirements of the system they represent. | Enables organizations to grow without having to invest in additional physical assets. Enables better decision making by providing real-time insights into the system’s performance. Facilitates collaboration between different stakeholders and teams by providing a common platform for data exchange. | DTs can simulate entire cities, including transportation systems, buildings, and energy grids, to improve sustainability and optimize resource usage. For example, the project Digital Twin Earth, which aims to create a virtual replica of the planet Earth to study climate change and its impact on various ecosystems [41]. |
Remote Control | This indicates that users are able to interact with and modify the digital twin even without physically being present at the site. | Facilitating a secure space for experimentation without potential harm, allowing remote control of physical systems globally in real time and enabling collaborative access and modification of the digital twin by multiple users Enables multiple users to access and modify the digital twin, regardless of their physical location. | DTs can be used to enable users to interact with and control a physical entity. For example, a human user and a robot can be connected through a DT system, which enables real-time remote control to monitor and adjust parameters [42,43]. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Almusayli, A.; Zia, T.; Qazi, E.-u.-H. Drone Forensics: An Innovative Approach to the Forensic Investigation of Drone Accidents Based on Digital Twin Technology. Technologies 2024, 12, 11. https://doi.org/10.3390/technologies12010011
Almusayli A, Zia T, Qazi E-u-H. Drone Forensics: An Innovative Approach to the Forensic Investigation of Drone Accidents Based on Digital Twin Technology. Technologies. 2024; 12(1):11. https://doi.org/10.3390/technologies12010011
Chicago/Turabian StyleAlmusayli, Asma, Tanveer Zia, and Emad-ul-Haq Qazi. 2024. "Drone Forensics: An Innovative Approach to the Forensic Investigation of Drone Accidents Based on Digital Twin Technology" Technologies 12, no. 1: 11. https://doi.org/10.3390/technologies12010011