Special Issue "Cybersecurity and Data Science"

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

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editor

Prof. Dr. Krzysztof Szczypiorski
Website
Guest Editor
Institute of Telecommunications, Warsaw University of Technology, 00-665 Warszawa, Poland
Interests: cybersecurity; digital forensics; steganography; anomaly detection
Special Issues and Collections in MDPI journals

Special Issue Information

This Special Issue is devoted to promoting the latest research in cybersecurity and data science. Digital transformation turns data into the new oil. The increasing availability of big data, structured and unstructured datasets, raises new challenges in cybersecurity, efficient data processing and knowledge extraction. The field of cybersecurity and data science fuels the data-driven economy. Innovations in this field require strong foundations in mathematics, statistics, machine learning and information security. 

The unprecedented increase in the availability of data in many fields of science and technology (e.g., genomic data, data from industrial environments, sensory data of smart cities, and social network data) ask for new methods and solutions for data processing, information extraction and decision support. This stimulates the development of new methods of data analysis, including those adapted to the analysis of new data structures and the growing volume of data.

Prof. Dr. Krzysztof Szczypiorski
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 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 1800 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

  • Cybersecurity:
    • automated safety management systems
    • non-repudiation systems, including blockchain-based
    • data protection using machine learning
    • detection of unknown attacks on ICT systems using big data and fast data algorithms
    • post-quantum cryptography
  • BioMed Data Science:
    • bioinformatics
    • biostatistics
    • computational medicine
  • Big and Stream Data Science:
    • big data
    • distributed storage
    • batch and stream analytics (smart city, genomics)
  • Advanced Machine Learning:
    • statistical learning methods
    • interpretable and explainable predictive models
    • clustering
    • classification and data fusion
  • Mathematical Foundations for Data Science:
    • mathematical foundations of data modeling and analysis
    • statistics and probability
    • graphs and networks
    • soft computing

Published Papers (1 paper)

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Research

Open AccessArticle
Multilayer Detection of Network Steganography
Electronics 2020, 9(12), 2128; https://doi.org/10.3390/electronics9122128 - 12 Dec 2020
Abstract
This paper presents a new method for steganography detection in network protocols. The method is based on a multilayer approach for the selective analysis of derived and aggregated metrics utilizing machine learning algorithms. The main objective is to provide steganalysis capability for networks [...] Read more.
This paper presents a new method for steganography detection in network protocols. The method is based on a multilayer approach for the selective analysis of derived and aggregated metrics utilizing machine learning algorithms. The main objective is to provide steganalysis capability for networks with large numbers of devices and connections. We discuss considerations for performance analysis and present results. We also describe a means of applying our method for multilayer detection of a popular RSTEG (Retransmission Steganography) technique. Full article
(This article belongs to the Special Issue Cybersecurity and Data Science)
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