Special Issue "Intelligent Security and Privacy Approaches against Cyber Threats"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 November 2020.

Special Issue Editor

Dr. Nour Moustafa
Website1 Website2 Website3 SciProfiles
Guest Editor
School of Engineering & Information Technology, The University of New South Wales, Canberra, Australia
Interests: intrusion detection; threat intelligence; privacy preservation; digital forensics; machine/deep learning; network systems; IoT; cloud
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues, 

As many organizations have moved to work from home, cyber attackers have expanded their advanced persistent threats (APT), such as phishing, spear-phishing and zero-day attacks, to exploit vulnerabilities of home networks. It is urgent to develop well-designed privacy security approaches, algorithms, protocols, standards and policies for safeguarding home and organization networks against new cyber threats.

The reason for this challenge is that the policy of Bring your Own device (BYOD) allows individuals to use various Internet of Things (IoT) devices, operating systems and tools, which are different in the settings of security and privacy. The technical and humanized practices of ‘Security-Based Organization’ and ‘Security-Based Home’ will enrich individuals' knowledge for protecting their home networks and securing their organizations’ assets. ‘Security-Based Organization’ denotes that organizations often provide security services and tools and training to employees with less effort from the employees and high visibility of security services, while ‘Security-Based Home’ denotes that individuals need new cyber practices which adapt security to home networks. The transition to work from home needs new security and privacy models that would employ Artificial Intelligence (AI), blockchain, human factor models, cognitive models. and secure big data analytics to secure home networks and safeguard organization assets.

Topics of interest include but are not limited to:

- Intelligent security practices and model-based AI against COVID-19 threats;

- Privacy-enabled human factor models against COVID-19 cyberattacks;

- AI-based Intrusion Detection Systems for discovering COVID-19 cyberattacks;

- AI-based cognitive models against COVID-19 cyberattacks;

- Privacy-driven human analytical behaviours in home networks;

- Privacy-preserving algorithms and approaches for protecting data of home networks;

- Secure Big Data analytics to analyze heterogeneous IoT and home elements;

- Secure and distributed semantic techniques for modeling home networks;

- Blockchain technologies for trusting home and organization systems and networks;

- Threat intelligence for pivoting COVID-19 cyber-attacks.

Dr. Nour Moustafa
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 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. Electronics is an international peer-reviewed open access monthly 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 1500 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

  • security
  • privacy
  • artificial intelligence
  • intrusion detection
  • human factors
  • privacy preservation

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Review

Open AccessReview
The k-means Algorithm: A Comprehensive Survey and Performance Evaluation
Electronics 2020, 9(8), 1295; https://doi.org/10.3390/electronics9081295 - 12 Aug 2020
Abstract
The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. [...] Read more.
The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions. Full article
(This article belongs to the Special Issue Intelligent Security and Privacy Approaches against Cyber Threats)
Show Figures

Figure 1

Open AccessReview
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions
Electronics 2020, 9(7), 1177; https://doi.org/10.3390/electronics9071177 - 20 Jul 2020
Abstract
The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The [...] Read more.
The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks. Full article
(This article belongs to the Special Issue Intelligent Security and Privacy Approaches against Cyber Threats)
Show Figures

Figure 1

Back to TopTop