Special Issue "Cyber Situational Awareness Techniques and Human Factors"

A special issue of Journal of Cybersecurity and Privacy (ISSN 2624-800X).

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. Xavier Bellekens
E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, Scotland, G1 1XW, UK
Interests: cyber-security; deception; maritime security; critical infrastructure security; intrusion detection systems; cyber situational awareness; cyber security training
Special Issues, Collections and Topics in MDPI journals
Dr. Mohamed Amine Ben Farah
E-Mail Website
Guest Editor
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, Scotland, G1 1XW, UK
Interests: cryptography; chaos theory; secure communications; privacy; cyber-security; blockchain
Special Issues, Collections and Topics in MDPI journals
Dr. Elochukwu Ukwandu
E-Mail Website
Guest Editor
Department of Computer Science, Cardiff School of Technologies, Cardiff Metropolitan University, Llandaff Campus, Western Avenue, Cardiff, UK
Interests: cryptography; cyber security; secret sharing; resilient, smart and anonymized cloud-based data storage methods
Dr. Hanan Hindy
E-Mail Website
Guest Editor
Abertay University, School of Design and Informatics, Bell Street, Dundee, DD1 1HG, Scotland, UK
Interests: Intrusion Detection Systems; Artificial Intelligence; Machine Learning; Cyber-security; Mobile Security; IoT Security

Special Issue Information

Dear Colleagues,

Over the past decade, the rise of new technologies, such as the Internet of Things and associated interfaces, has dramatically increased our reliance on the cyberspace and the need to understand our environment accurately, to predict, respond, and solve potential cybersecurity problems that may occur.

Cyber situational awareness focuses on the correlation of disparate data, playing an integral role in information assurance. In order to achieve cyber situational awareness, understand new threats, and better our defenses, we must obtain relevant information across organizational structures and turn it into usable intelligence allowing security analysts and operators to:

  • Make informed decisions;
  • Visualize their environment;
  • Understand the security posture of the infrastructure;
  • Understand the destructive actions of adversaries;
  • Identify key indicators of malicious activities;
  • Determine the best defense to hinder or stop said malicious activities.

While these applications of CSA have been proven beneficial for the cybersecurity industry, they have also highlighted a number of shortcomings, such as the lack of interconnection with human factors, the difficulty to create fusion centers, the lack of a collaborative defense approach (from a user or network perspective), and the need for CSA frameworks, to name a few.

This Special Issue on “Cyber Situational Awareness Techniques and Human Factors” is aimed at industrial and academic researchers applying non-traditional methods to solve cybersecurity problems. The key areas of this Special Issue include but are not limited to:

  • situational awareness assessments
  • information security metrics and measurements
  • OSING
  • cyber behavioral analytics and profiling
  • PsyOPS
  • web analytics and incident response
  • social network intelligence
  • game theory
  • cyberattack scenarios
  • situation-aware application
  • context-aware application
  • situation-aware network
  • context-aware network
  • attack graphs
  • security and incident analysis
  • sensor fusion
  • data correlation
  • cyber psychology
  • human decision control
  • proactive defense strategies

Dr. Xavier Bellekens
Dr. Mohamed Amine Ben Farah
Dr. Elochukwu Ukwandu
Dr. Hanan Hindy
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 papers will be 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. Journal of Cybersecurity and Privacy is an international peer-reviewed open access quarterly 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 1000 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.

Published Papers (2 papers)

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Research

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Article
Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector
J. Cybersecur. Priv. 2021, 1(1), 199-218; https://doi.org/10.3390/jcp1010011 - 21 Mar 2021
Cited by 1 | Viewed by 1080
Abstract
Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain [...] Read more.
Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain many attributes. Such high dimensionality of datasets poses a significant challenge to information extraction in terms of time and space complexity. Moreover, having so many attributes may be a hindrance towards creation of a decision boundary due to noise in the dataset. Large scale data with redundant or insignificant features increases the computational time and often decreases goodness of fit which is a critical issue in cybersecurity. In this research, we have proposed and implemented an efficient feature selection algorithm to filter insignificant variables. Our proposed Dynamic Feature Selector (DFS) uses statistical analysis and feature importance tests to reduce model complexity and improve prediction accuracy. To evaluate DFS, we conducted experiments on two datasets used for cybersecurity research namely Network Security Laboratory (NSL-KDD) and University of New South Wales (UNSW-NB15). In the meta-learning stage, four algorithms were compared namely Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units, Random Forest and a proposed Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) for accuracy estimation. For NSL-KDD, experiments revealed an increment in accuracy from 99.54% to 99.64% while reducing feature size of one-hot encoded features from 123 to 50. In UNSW-NB15 we observed an increase in accuracy from 90.98% to 92.46% while reducing feature size from 196 to 47. The proposed approach is thus able to achieve higher accuracy while significantly lowering number of features required for processing. Full article
(This article belongs to the Special Issue Cyber Situational Awareness Techniques and Human Factors)
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Review

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Review
Augmented Reality and the Digital Twin: State-of-the-Art and Perspectives for Cybersecurity
J. Cybersecur. Priv. 2021, 1(3), 519-538; https://doi.org/10.3390/jcp1030026 - 09 Sep 2021
Viewed by 614
Abstract
The rapid advancements of technology related to the Internet of Things and Cyber-Physical Systems mark an ongoing industrial revolution. Digital Twins and Augmented Reality play a significant role in this technological advancement. They are highly complementary concepts enabling the representation of physical assets [...] Read more.
The rapid advancements of technology related to the Internet of Things and Cyber-Physical Systems mark an ongoing industrial revolution. Digital Twins and Augmented Reality play a significant role in this technological advancement. They are highly complementary concepts enabling the representation of physical assets in the digital space (Digital Twin) and the augmentation of physical space with digital information (Augmented Reality). Throughout the last few years, research has picked up on this and explored the possibilities of combining DT and AR. However, cybersecurity scholars have not yet paid much attention to this combined-arms approach, despite its potential. Especially, concerning contemporary security challenges, such as developing cyber situational awareness and including human factors into cybersecurity, AR and DT, offer tremendous potential for improvement. In this work, we systematize existing knowledge on AR-powered DTs and shed light on why and how cybersecurity could benefit from this combination. Full article
(This article belongs to the Special Issue Cyber Situational Awareness Techniques and Human Factors)
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