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Applied Sciences
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Intelligent Digital Forensics and Cyber Security
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Intelligent Digital Forensics and Cyber Security

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 12002

Special Issue Editor

Prof. Dr. Hein Venter

E-Mail Website
Guest Editor
Digital Forensic Science (DigiForS) Research Group, Department of Computer Science, University of Pretoria, Pretoria 0002, South Africa
Interests: digital forensics; cybersecurity; information privacy; wireless security systems; network security

Special Issue Information

Dear Colleagues,

Information security and cybersecurity have become a major priority for every organization, as it involves confidential data and private information, one of the most valuable resources. Playing a critical role in cybersecurity, digital forensics has received significant attention from researchers and practitioners. With the increasing sophistication of modern cyberattacks, the complexity of digital (forensic) investigations requires new technologies and solutions.

For this reason, there is a need to embrace the opportunities afforded by applying principles and procedures of artificial intelligence to digital forensics intelligence and to intelligent forensics. Artificial intelligence, particularly machine learning and deep learning, is bringing new developments in cybersecurity and moving beyond the capabilities of digital forensic tools that are in current use. By applying new techniques to digital investigations, there is a need to address the challenges of the larger and more complex domains in cybersecurity nowadays.

In this Special Issue, we are gathering diverse and complementary articles that demonstrate developments in digital forensics and tackle new challenges for cybersecurity. Some specific topics include, but are not limited to:

  • Digital forensics;
  • Computer and network forensics;
  • Artificial intelligence for cybersecurity, privacy, and forensics;
  • Internet of Things security, privacy, and forensics;
  • Distributed trust and security issues in pervasive computing;
  • Machine learning methods for database security.

The above topic list is not meant to be exhaustive; this Special Issue is interested in all aspects of digital forensics and new technologies or solutions for cybersecurity.

Prof. Dr. Hein Venter
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital forensics
  • intelligent forensics
  • artificial intelligence for cybersecurity
  • cybercrime
  • digital forensic investigations
  • digital forensic readiness
  • computer forensics

Published Papers (5 papers)

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Research

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19 pages, 3011 KiB  
Article
A Malware Detection Framework Based on Semantic Information of Behavioral Features
by Yuxin Zhang, Shumian Yang, Lijuan Xu, Xin Li and Dawei Zhao
Appl. Sci. 2023, 13(22), 12528; https://doi.org/10.3390/app132212528 - 20 Nov 2023
Cited by 2 | Viewed by 1001
Abstract
As the amount of malware has grown rapidly in recent years, it has become the most dominant attack method in network security. Learning execution behavior, especially Application Programming Interface (API) call sequences, has been shown to be effective for malware detection. However, it [...] Read more.
As the amount of malware has grown rapidly in recent years, it has become the most dominant attack method in network security. Learning execution behavior, especially Application Programming Interface (API) call sequences, has been shown to be effective for malware detection. However, it is troublesome in practice to adequate mining of API call features. Among the current research methods, most of them only analyze single features or inadequately analyze the features, ignoring the analysis of structural and semantic features, which results in information loss and thus affects the accuracy. In order to deal with the problems mentioned above, we propose a novel method of malware detection based on semantic information of behavioral features. First, we preprocess the sequence of API function calls to reduce redundant information. Then, we obtain a vectorized representation of the API call sequence by word embedding model, and encode the API call name by analyzing it to characterize the API name’s semantic structure information and statistical information. Finally, a malware detector consisting of CNN and bidirectional GRU, which can better understand the local and global features between API calls, is used for detection. We evaluate the proposed model in a publicly available dataset provided by a third party. The experimental results show that the proposed method outperforms the baseline method. With this combined neural network architecture, our proposed model attains detection accuracy of 0.9828 and an F1-Score of 0.9827. Full article
(This article belongs to the Special Issue Intelligent Digital Forensics and Cyber Security)
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23 pages, 22143 KiB  
Article
Anthropological Comparative Analysis of CCTV Footage in a 3D Virtual Environment
by Krzysztof Maksymowicz, Aleksandra Kuzan, Łukasz Szleszkowski and Wojciech Tunikowski
Appl. Sci. 2023, 13(21), 11879; https://doi.org/10.3390/app132111879 - 30 Oct 2023
Viewed by 803
Abstract
The image is a particularly valuable data carrier in medical forensic and forensic analyses. One of the analyses, as mentioned above, is to assess whether a graphically captured object is the same object examined in reality. This is a complicated process due to [...] Read more.
The image is a particularly valuable data carrier in medical forensic and forensic analyses. One of the analyses, as mentioned above, is to assess whether a graphically captured object is the same object examined in reality. This is a complicated process due to perspective foreshortening, making it difficult to determine the scale and proportion of objects in the frame, as well as the subsequent correct reading of their actual measurements. This paper presented a method for the 3D reconstruction of silhouettes of people recorded in a photo or video, with the aim of identifying these people through subsequent comparative studies. The authors presented an algorithm for dealing with graphic evidence, using the example of the analysis of spatial correlation of the silhouette of the perpetrator of the actual event (recorded via CCTV footage) with the silhouette of the suspect (scanned in 3D in custody). In this paper, the authors posed the thesis that the isometric (devoid of perspective foreshortening) display mode that 3D platforms offer, and the animation of the figure to the desired identical poses, provides the possibility of not only obtaining linear measurements of the person but also of orthophotographic visualization of body proportions, allowing their comparison with another silhouette, which is difficult to achieve in perspective view of the studied image. Full article
(This article belongs to the Special Issue Intelligent Digital Forensics and Cyber Security)
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34 pages, 20819 KiB  
Article
Forensic Operations for Recognizing SQLite Content (FORC): An Automated Forensic Tool for Efficient SQLite Evidence Extraction on Android Devices
by Eman Daraghmi, Zaer Qaroush, Monia Hamdi and Omar Cheikhrouhou
Appl. Sci. 2023, 13(19), 10736; https://doi.org/10.3390/app131910736 - 27 Sep 2023
Viewed by 1241
Abstract
Mobile forensics is crucial in reconstructing various everyday activities accomplished through mobile applications during an investigation. Manual analysis can be tedious, time-consuming, and error-prone. This study introduces an automated tool called Forensic Operations for Recognizing SQLite Content (FORC), specifically designed for Android, to [...] Read more.
Mobile forensics is crucial in reconstructing various everyday activities accomplished through mobile applications during an investigation. Manual analysis can be tedious, time-consuming, and error-prone. This study introduces an automated tool called Forensic Operations for Recognizing SQLite Content (FORC), specifically designed for Android, to extract Simple Query Language Table Database Lightweight (SQLite) evidence. SQLite is a library that serves as a container for mobile application data, employing a zero-configuration, serverless, self-contained, and transactional SQL database engine. While some SQLite files possess extensions such as .db, .db3, .sqlite, and .sqlit3, others have none. The lack of file extensions may result in missing evidence that could unveil the truth. The proposed tool utilizes both the file extensions and headers of the SQLite data to recognize and identify SQLite data generated or modified by a mobile application. The FORC tool’s capability was evaluated using the Chrome application as a case study, and a comparison between FORC and other tools was conducted. The results suggest that FORC significantly simplifies mobile forensic analysis. Full article
(This article belongs to the Special Issue Intelligent Digital Forensics and Cyber Security)
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Review

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12 pages, 993 KiB  
Review
Machine-Learning Forensics: State of the Art in the Use of Machine-Learning Techniques for Digital Forensic Investigations within Smart Environments
by Laila Tageldin and Hein Venter
Appl. Sci. 2023, 13(18), 10169; https://doi.org/10.3390/app131810169 - 10 Sep 2023
Viewed by 1986
Abstract
Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic investigation (DFI) in such environments. [...] Read more.
Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic investigation (DFI) in such environments. These challenges include data heterogeneity, data distribution, and massive amounts of data, which exceed digital forensic (DF) investigators’ human capabilities to deal with all of these challenges within a short period of time. Furthermore, they significantly slow down or even incapacitate the conventional DFI process. With the increasing frequency of digital crimes, better and more sophisticated DFI procedures are desperately needed, particularly in such environments. Since machine-learning (ML) techniques might be a viable option in smart environments, this paper presents the integration of ML into DF, through reviewing the most recent papers concerned with the applications of ML in DF, specifically within smart environments. It also explores the potential further use of ML techniques in DF in smart environments to reduce the hard work of human beings, as well what to expect from future ML applications to the conventional DFI process. Full article
(This article belongs to the Special Issue Intelligent Digital Forensics and Cyber Security)
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32 pages, 2763 KiB  
Review
Children’s Safety on YouTube: A Systematic Review
by Saeed Ibrahim Alqahtani, Wael M. S. Yafooz, Abdullah Alsaeedi, Liyakathunisa Syed and Reyadh Alluhaibi
Appl. Sci. 2023, 13(6), 4044; https://doi.org/10.3390/app13064044 - 22 Mar 2023
Cited by 4 | Viewed by 6308
Abstract
Background: With digital transformation and growing social media usage, kids spend considerable time on the web, especially watching videos on YouTube. YouTube is a source of education and entertainment media that has a significant impact on the skill improvement, knowledge, and attitudes [...] Read more.
Background: With digital transformation and growing social media usage, kids spend considerable time on the web, especially watching videos on YouTube. YouTube is a source of education and entertainment media that has a significant impact on the skill improvement, knowledge, and attitudes of children. Simultaneously, harmful and inappropriate video content has a negative impact. Recently, researchers have given much attention to these issues, which are considered important for individuals and society. The proposed methods and approaches are to limit or prevent such threats that may negatively influence kids. These can be categorized into five main directions. They are video rating, parental control applications, analysis meta-data of videos, video or audio content, and analysis of user accounts. Objective: The purpose of this study is to conduct a systematic review of the existing methods, techniques, tools, and approaches that are used to protect kids and prevent them from accessing inappropriate content on YouTube videos. Methods: This study conducts a systematic review of research papers that were published between January 2016 and December 2022 in international journals and international conferences, especially in IEEE Xplore Digital Library, ACM Digital Library, Web of Science, Google Scholar, Springer database, and ScienceDirect database. Results: The total number of collected articles was 435. The selection and filtration process reduced this to 72 research articles that were appropriate and related to the objective. In addition, the outcome answers three main identified research questions. Significance: This can be beneficial to data mining, cybersecurity researchers, and peoples’ concerns about children’s cybersecurity and safety. Full article
(This article belongs to the Special Issue Intelligent Digital Forensics and Cyber Security)
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