Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
2.9
Journals
Electronics
Special Issues
Deep Learning Cognitive Computing Techniques and Their Application in Cybersecurity...
share announcement

Deep Learning Cognitive Computing Techniques and Their Application in Cybersecurity and Forensics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 6262

Special Issue Editors

Dr. Nickson M. Karie

E-Mail Website
Guest Editor
Cyber Security CRC, ECU Security Research Institute, Edith Cowan University, Joondalup 6027, Australia
Interests: digital forensics; critical infrastructure security; intrusion detection and prevention; information and computer security architecture; network security, IoT security
Dr. Wencheng Yang

E-Mail Website
Guest Editor
Cyber Security CRC Researcher, ECU Security Research Institute, Edith Cowan University, Joondalup 6027, Australia
Interests: biometric security; biometric pattern recognition; machine learning; cyber security
Dr. Oluwasola Mary Adedayo

E-Mail Website
Guest Editor
Department of Applied Computer Science, The University of Winnipeg, Winnipeg, MB R3B 2E9, Canada
Interests: digital forensics; database forensics; cybersecurity

Special Issue Information

Dear Colleagues,

Recent developments in artificial intelligence, specifically in deep leaning and cognitive computing techniques, have caused new developments in cybersecurity and forensics. As cyber-attacks and ransomware become more prevalent in all areas of our lives, the struggle to combat these cybercrimes is becoming a daily struggle. Cybercriminals make use of complicated cyber-attack techniques to evade detection systems, making it difficult for security professionals and investigators to capture any potential digital forensic evidence.

For this reason, we believe that deep learning (DL), which is a subset of artificial intelligence (AI), has very distinct use-cases in the domain of cybersecurity and forensics. Although many people might argue that it is not an unrivalled solution, it can help enhance the fight against cybercrime. DL uses some machine learning techniques to solve problems using neural networks that simulate human decision-making. Based on these grounds, DL holds the potential to dramatically change the domain of cybersecurity and forensics in a variety of ways as well as provide solutions to security professionals and forensic investigators. Such solutions can range from managing and responding to cyber-attacks, reducing bias in forensic investigations, as well as challenging what evidence is considered admissible in a court of law or any civil hearing.

While this Special Issue invites a broad range of topics across emerging deep learning cognitive computing techniques applications in cybersecurity and forensics, some specific topics include, but are not limited to: 

  • Application of deep learning cognitive computing techniques in cybersecurity or forensics;
  • Deep learning for cyber forensics;
  • Deep learning enabled cybersecurity or forensics engine;
  • Novel machine learning approaches to cybersecurity or forensics;
  • Deep learning methods for cybersecurity or forensics;
  • Deep learning methods for database security or forensics.

Dr. Nickson M. Karie
Dr. Wencheng Yang
Dr. Oluwasola Mary Adedayo
Guest Editors

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. Electronics 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

  • artificial intelligence
  • deep learning cognitive computing techniques
  • cybersecurity
  • digital forensics
  • cyber attacks
  • cyber crimes
  • neural networks
  • machine learning
  • deep learning for biometrics security
  • deep learning for IoT security and forensics
  • deep learning for cloud security and forensics
  • deep learning for database security and forensics

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

31 pages, 9035 KiB  
Article
Enhancing Ransomware Attack Detection Using Transfer Learning and Deep Learning Ensemble Models on Cloud-Encrypted Data
by Amardeep Singh, Zohaib Mushtaq, Hamad Ali Abosaq, Salim Nasar Faraj Mursal, Muhammad Irfan and Grzegorz Nowakowski
Electronics 2023, 12(18), 3899; https://doi.org/10.3390/electronics12183899 - 15 Sep 2023
Cited by 4 | Viewed by 2240
Abstract
Ransomware attacks on cloud-encrypted data pose a significant risk to the security and privacy of cloud-based businesses and their consumers. We present RANSOMNET+, a state-of-the-art hybrid model that combines Convolutional Neural Networks (CNNs) with pre-trained transformers, to efficiently take on the challenging issue [...] Read more.
Ransomware attacks on cloud-encrypted data pose a significant risk to the security and privacy of cloud-based businesses and their consumers. We present RANSOMNET+, a state-of-the-art hybrid model that combines Convolutional Neural Networks (CNNs) with pre-trained transformers, to efficiently take on the challenging issue of ransomware attack classification. RANSOMNET+ excels over other models because it combines the greatest features of both architectures, allowing it to capture hierarchical features and local patterns. Our findings demonstrate the exceptional capabilities of RANSOMNET+. The model had a fantastic precision of 99.5%, recall of 98.5%, and F1 score of 97.64%, and attained a training accuracy of 99.6% and a testing accuracy of 99.1%. The loss values for RANSOMNET+ were impressively low, ranging from 0.0003 to 0.0035 throughout training and testing. We tested our model against the industry standard, ResNet 50, as well as the state-of-the-art, VGG 16. RANSOMNET+ excelled over the other two models in terms of F1 score, accuracy, precision, and recall. The algorithm’s decision-making process was also illuminated by RANSOMNET+’s interpretability analysis and graphical representations. The model’s openness and usefulness were improved by the incorporation of feature distributions, outlier detection, and feature importance analysis. Finally, RANSOMNET+ is a huge improvement in cloud safety and ransomware research. As a result of its unrivaled accuracy and resilience, it provides a formidable line of defense against ransomware attacks on cloud-encrypted data, keeping sensitive information secure and ensuring the reliability of cloud-stored data. Cybersecurity professionals and cloud service providers now have a reliable tool to combat ransomware threats thanks to this research. Full article
(This article belongs to the Special Issue Deep Learning Cognitive Computing Techniques and Their Application in Cybersecurity and Forensics)
►▼ Show Figures

Graphical abstract

17 pages, 2920 KiB  
Article
Entity-Based Integration Framework on Social Unrest Event Detection in Social Media
by Ao Shen and Kam Pui Chow
Electronics 2022, 11(20), 3416; https://doi.org/10.3390/electronics11203416 - 21 Oct 2022
Viewed by 1342
Abstract
Social unrest events have been an issue of concern to people in various countries. In the past few years, mass unrest events appeared in many countries. Meanwhile, social media has become a distinctive method of spreading event information. It is necessary to construct [...] Read more.
Social unrest events have been an issue of concern to people in various countries. In the past few years, mass unrest events appeared in many countries. Meanwhile, social media has become a distinctive method of spreading event information. It is necessary to construct an effective method to analyze the unrest events through social media platforms. Existing methods mainly target well-labeled data and take relatively little account of the event development. This paper proposes an entity-based integration event detection framework for event extraction and analysis in social media. The framework integrates two modules. The first module utilizes named entity recognition technology based on the bidirectional encoder representation from transformers (BERT) algorithm to extract the event-related entities and topics of social unrest events during social media communication. The second module suggests the K-means clustering method and dynamic topic model (DTM) for dynamic analysis of these entities and topics. As an experimental scenario, the effectiveness of the framework is demonstrated using the Lihkg discussion forum and Twitter from 1 August 2019 to 31 August 2020. In addition, the comparative experiment is performed to reveal the differences between Chinese users on Lihkg and Twitter for comparative social media studies. The experiment results somehow indicate the characteristic of social unrest events that can be found in social media. Full article
(This article belongs to the Special Issue Deep Learning Cognitive Computing Techniques and Their Application in Cybersecurity and Forensics)
►▼ Show Figures

Figure 1

22 pages, 4819 KiB  
Article
Error Level Analysis Technique for Identifying JPEG Block Unique Signature for Digital Forensic Analysis
by Nor Amira Nor Azhan, Richard Adeyemi Ikuesan, Shukor Abd Razak and Victor R. Kebande
Electronics 2022, 11(9), 1468; https://doi.org/10.3390/electronics11091468 - 3 May 2022
Cited by 2 | Viewed by 1907
Abstract
The popularity of unique image compression features of image files opens an interesting research analysis process, given that several digital forensics cases are related to diverse file types. Of interest has been fragmented file carving and recovery which forms a major aspect of [...] Read more.
The popularity of unique image compression features of image files opens an interesting research analysis process, given that several digital forensics cases are related to diverse file types. Of interest has been fragmented file carving and recovery which forms a major aspect of digital forensics research on JPEG files. Whilst there exist several challenges, this paper focuses on the challenge of determining the co-existence of JPEG fragments within various file fragment types. Existing works have exhibited a high false-positive rate, therefore rendering the need for manual validation. This study develops a technique that can identify the unique signature of JPEG 8 × 8 blocks using the Error Level Analysis technique, implemented in MATLAB. The experimental result that was conducted with 21 images of JFIF format with 1008 blocks shows the efficacy of the proposed technique. Specifically, the initial results from the experiment show that JPEG 8 × 8 blocks have unique characteristics which can be leveraged for digital forensics. An investigator could, therefore, search for the unique characteristics to identify a JPEG fragment during a digital investigation process. Full article
(This article belongs to the Special Issue Deep Learning Cognitive Computing Techniques and Their Application in Cybersecurity and Forensics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop