Symmetry or Asymmetry in Big Data Datasets for Cybersecurity

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 189

Special Issue Editor


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Guest Editor
Department of Electronics, Computing and Mathematics, University of Derby, Derby DE22 1GB, UK
Interests: IoT; wireless sensor networks; machine learning; artificial intelligence; optimization algorithms; cyber security
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Special Issue Information

Dear Colleagues,

The rapid expansion of digital infrastructures and the ubiquitous deployment of smart technologies have led to an exponential growth in data generation. Big data has revolutionized the field of cybersecurity, offering a wealth of information that can be harnessed for threat detection, anomaly analysis, and the prevention of cyberattacks. However, the inherent properties of big data often present unique challenges, particularly concerning the notions of symmetry and asymmetry in data structures, distributions, and patterns.

This Special Issue explores the dual roles of symmetry and asymmetry in big data as they pertain to cybersecurity. Symmetrical data patterns, often leveraged for predictive analytics, provide consistent insights, enhancing the detection of familiar threats. Conversely, asymmetric data distributions, characteristic of unpredictable and emerging cyber threats, require advanced models for effective analysis and mitigation.

We invite submissions that address theoretical advancements, methodologies, and practical applications focusing on how symmetry and asymmetry can influence security frameworks, data modeling, and threat analysis in the era of big data. Topics of interest include but are not limited to the following:

  • Symmetrical and asymmetrical data modeling for threat detection.
  • The impact of data symmetry/asymmetry on machine learning performance in cybersecurity.
  • Techniques for managing imbalanced and asymmetrical big data in cyber defense mechanisms.
  • Novel algorithms that leverage symmetrical data patterns for efficient anomaly detection.
  • Case studies demonstrating the real-world implications of data asymmetry on cybersecurity measures.
  • The role of symmetrical features in risk assessment and predictive analysis.

This Special Issue aims to bridge the gap between data science and cybersecurity, highlighting the interplay between symmetry and asymmetry in developing robust, data-driven security strategies.

Research areas may include (but are not limited to) the following:

Themes and Article Types for Submission:

  1. Symmetrical and asymmetrical patterns in cybersecurity data;
  2. Machine learning and data imbalance challenges;
  3. Predictive analytics using symmetry;
  4. Data distribution and feature engineering;
  5. Anomaly detection techniques in asymmetrical data;
  6. Risk assessment models leveraging symmetrical features;
  7. Performance evaluation of algorithms on symmetrical vs. asymmetrical datasets;
  8. Practical applications of symmetry/asymmetry in threat intelligence;
  9. Data preprocessing strategies for asymmetric big data;
  10. Theoretical frameworks on symmetry and asymmetry in cybersecurity.

Dr. Haider Ali
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. Symmetry 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 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

  • machine learning
  • cybersecurity
  • deep learning
  • threat detection
  • intrusion detection
  • anomaly detection
  • privacy
  • malware
  • artificial intelligence
  • digital security

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Published Papers

This special issue is now open for submission.
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