# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 7 Discussion and Directions for Future Works  
Digital forensic professionals face challenges when conducting forensic investigations on Unmanned Aerial Vehicles (UAVs) due to the diverse range of digital components present in a typical UAV. This makes it challenging for forensic investigators to concentrate on a specific forensic tool that can retrieve all the necessary data for the investigation process.  
In certain instances, acquiring an image file of the data from a UAV's airborne camera without compromising its integrity is difficult. In terms of forensic photography, numerous UAVs feature USB connectors that do not facilitate direct access to the internal disk.  
Accessing flight data via onboard flight microcontrollers frequently demands special user authorization through the wireless controller, which is unlikely to be available to security agencies and forensic investigators. In addition, most flight data retrieved from the flight microcontroller is encrypted. As a result, the lack of a microcontroller complicates the forensic investigation process.  
Software, hardware, and firmware for onboard UAVs have not yet been standardized, and they differ from one manufacturer to the other. There are currently no standard protocols for flight controllers; thus, there is no common format for flight data. Users can also boost the efficiency of a UAV by adding extra elements or changing it with Software Developer Kits given by many UAV manufacturers.  
Accessibility to flight data via the internal flight controller chip frequently necessitates specific owner authorization via the wireless controller, which is unlikely to be available to law enforcement agencies and forensic experts. In addition, most flight data retrieved from the flight microchip is encrypted. As a result, the lack of a remote controller complicates forensic analysis.  
UAVs rely significantly on a volatile memory, which means that the flight data recorded there would be lost if the battery is dead. Additionally, some sensor information can be designed to be transferred to a secure server inside a cloud infrastructure or to be shared on file-sharing or social websites.  
It must be emphasized that although offering a way to do forensics, the majority of drone forensics research focuses on commercial drones that use proprietary software. As a result, their methodologies, or at least part of them, are difficult to standardize [160]. Due to developments in drone manufacturing, forensic frameworks and methods require regular updating. For reference, Table 9 lists acronyms used in the paper.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Dataset** & **Remarks** \\ \hline Drone Tracking [157] & Clips of a flying UAV being recorded using many commercial cameras and highly precise 3D UAV trajectory classification algorithm recorded by Fixposition’s exact real-time RTK system. Ground truth time synchronization and ground truth camera positions are also included in several clips. \\ \hline Amateur UAV Detection [158] & Non-drone, UAV-like “negative” entities are included in the dataset. Yolov2-tiny and Yolov3-voc versions were utilized with this dataset. Working with Yolo design and the darknet platform is usually recommended. \\ \hline Phantom III drone imagery [159] & The imagery in this collection was captured using a Phantom III drone. A DJI FC300S visible light camera as well as a Sentera 1.2 MP GS-0002 6.05 mm near-infrared camera placed on the UAV produced two sets of images. It gives you an image log with the GPS location of the collection points. \\ \hline \hline \end{tabular}
\end{table}
Table 8: _Cont._  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Notations** & **Full Name** \\ \hline ADS-B & Automatic Dependent Surveillance-Broadcast \\ BLOS & Beyond Line-Of-Sight \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of drone-related notations.