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With the frequent occurrence of drone-related criminal cases, drone forensics has become a hot spot of concern. During drone-related criminal investigations, the implicated drones are often forcibly brought down, which poses significant challenges in conducting forensic analysis. In order to restore the truth of criminal cases, it is necessary to extract data not only from the external TF card but also from internal chip memory in drone forensics. To address this issue, a drone data parser (DRDP) is proposed to extract internal and external data from criminal-implicated drones. In this paper, we present comprehensive forensics on the DJI Mavic 2 Pro, analyzing the main file structure and encryption model. According to its file structures, three case studies are conducted on various file types (DAT files, TXT files, and default files) to verify the effectiveness and applicability of the designed procedure. The results show that the encrypted data of the implicated drone, such as GPS information, flight time, flight altitude, flight distance, three velocity components (x, y, z) and other information can be extracted and decrypted correctly, which provides evidence for the identification of the case facts.

Although other methods exist to store and manage data in modern information technology, the standard solution is file systems. Therefore, keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone lack several important capabilities for FAIR data management: the two most significant being insufficient visualization of data and inadequate possibilities for searching and obtaining an overview. Research data management systems (RDMSs) can fill this gap, but many do not support the simultaneous use of the file system and RDMS. This simultaneous use can have many benefits, but keeping data in RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow for keeping file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS LinkAhead (previously named “CaosDB”).

The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems

Storing scientific data on the filesystem in a meaningful and transparent way is no trivial task. In particular, when the data have to be accessed after their originator has left the lab, the importance of a standardized filesystem layout cannot be underestimated. It is desirable to have a structure that allows for the unique categorization of all kinds of data from experimental results to publications. They have to be accessible to a broad variety of workflows, e.g., via graphical user interface as well as via command line, in order to find widespread acceptance. Furthermore, the inclusion of already existing data has to be as simple as possible. We propose a three-level layout to organize and store scientific data that incorporates the full chain of scientific data management from data acquisition to analysis to publications. Metadata are saved in a standardized way and connect original data to analyses and publications as well as to their originators. A simple software tool to check a file structure for compliance with the proposed structure is presented.

With the increasing storage capacity of personal computing devices, the problems of information overload and information fragmentation are apparent on users’ desktops. For the Web, semantic technologies solve this problem by adding a machine-interpretable information layer on top of existing resources. It has been shown that the application of these technologies to desktop environments is helpful for end users. However, certain characteristics of the Semantic Web architecture that are commonly accepted in the Web context are not desirable for desktops. To overcome these limitations, the authors propose the sile model, which combines characteristics of the Semantic Web and file systems. This model is a conceptual foundation for the Semantic Desktop and serves as underlying infrastructure on which applications and further services can be built. The authors present one service, a virtual file system based on siles, which allows users to semantically annotate files and directories and keeps full compatibility to traditional hierarchical file systems. The authors also discuss how Semantic Web vocabularies can be applied for meaningful annotation of files and present a prototypical implementation of the model and analyze the performance of typical access operations, both on the file system and metadata level.

This chapter introduces the organization and behavior of ANSYS. It presents multiple ways to interact with the program and summarizes the types of feedback that ANSYS provides. It also answers common questions including “How do I save files?,” “Where is the Undo button?,” and “Where can I find help?.” Most importantly, it explains why ANSYS may be different from other software programs that you have used and how you can benefit from these differences.

The literature on physical database design in general, and on file segmentation in particular, typically ignores any consideration of data security and the cost to enforce it. If records can be physically designed so that all data elements in a given record type have identical security restrictions for a given user, then data element level security enforcement can be transformed into the less costly file level security enforcement for that user and rie. Similarly, if all record types have identical security restrictions, file based security might be sufficient. This paper extends an earlier model for file segmentation to include security considerations. The extended model embeds the security measures into the logical file structure and exploits a four category taxonomy of security restriction types. The model is used to generalize the interaction between element level selective security and physical database design.

Perhaps the most important skill for someone working with computer forensics is to know how computers work. In order to locate digital traces of an e-mail, the examiner must know that such traces may look like. While this book is intended for someone who is fairly skilled in the computer world, there are some theories that are extra important for a forensic examiner and this computer theory is presented in this chapter. This includes an overview of encryption and decryption as well as a presentation of how data is represented in the digital word, in binary, hexadecimal and plain ASCII. Further, this chapter introduces theory that is often overlooked by disciplines other than computer forensics. This includes an overview of the NTFS file system and Windows registry that is one of the most valuable sources of information during an examination of a Windows computer. The chapter also describes what commonly happens when a file is deleted from a computer, namely that it is not deleted at all.