Network Information Theory and Its Applications in Security and Privacy

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 1952

Special Issue Editors


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Guest Editor
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore
Interests: information theory and its applications

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Guest Editor
Chair of Communications Engineering, Technical University of Munich, Munich, Germany
Interests: information theory; coding theory; combinatorics

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Guest Editor
Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
Interests: computer security; cyber security; privacy; information security; cryptography; intrusion detection; malware; trust; anonymity
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Special Issue Information

Dear Colleagues,

This Special Issue aims to explore cutting-edge advancements in Network Information Theory and its applications in the domains of security and privacy. Network information theory deals with the fundamental principles of information flow and communication within networks. In the context of security and privacy, it explores how information theory concepts can be employed to enhance the confidentiality, integrity, and availability of data transmission and storage.

This Special Issue seeks to give an overview of the theoretical foundations, practical techniques, and novel applications of network information theory in the fields of security and privacy. It aims to address the challenges and opportunities that arise in various applications, such as wireless networks, Internet of Things (IoT), social networks, and cloud computing and distributed data storage.

This Special Issue will publish high-quality, original research papers dealing with overlapping topics such as:

  • Security and privacy in data transmission and storage;
  • Privacy-preserving protocols in large-scale networks;
  • Secure and reliable cloud computing;
  • Network information theory applications in wireless communications, Internet of Things (IoT), and cyber-physical systems;
  • Post-quantum cryptography and its impact on secure communication networks;
  • Machine learning and artificial intelligence techniques for enhancing security and privacy in networks.

Researchers working in the fields of network information theory, security, and privacy are encouraged to submit their original research papers. The aim is to foster the dissemination of knowledge, exchange of ideas, and advancement of solutions that harness the power of network information theory for improving security and privacy.

Dr. Stanislav Kruglik
Dr. Ilya Vorobyev
Dr. Luis Javier García Villalba
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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

  • network information theory
  • security and privacy
  • wireless communications
  • cyber-physical systems
  • Internet of Things
  • distributed storage systems
  • machine learning and artificial intelligence
  • cloud computing

Published Papers (2 papers)

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Research

16 pages, 628 KiB  
Article
Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
by Rucha Mannikar and Fabio Di Troia
Appl. Sci. 2024, 14(10), 4019; https://doi.org/10.3390/app14104019 - 9 May 2024
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Abstract
A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often [...] Read more.
A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often camouflage their activity by utilizing common internet protocols, such as HTTP and IRC, making their detection challenging. This paper addresses this threat by proposing a method to identify botnets based on distinctive communication patterns between command and control servers and bots. Recognizable traits in botnet behavior, such as coordinated attacks, heartbeat signals, and periodic command distribution, are analyzed. Probabilistic models, specifically Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), are employed to learn and identify these activity patterns in network traffic data. This work utilizes publicly available datasets containing a combination of botnet, normal, and background traffic to train and test these models. The comparative analysis reveals that both HMMs and PHMMs are effective in detecting botnets, with PHMMs exhibiting superior accuracy in botnet detection compared to HMMs. Full article
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13 pages, 424 KiB  
Article
Sequential Polar Decoding with Cost Metric Threshold
by Ilya Timokhin and Fedor Ivanov
Appl. Sci. 2024, 14(5), 1847; https://doi.org/10.3390/app14051847 - 23 Feb 2024
Viewed by 477
Abstract
Polar codes have established themselves as a cornerstone in modern error correction coding due to their capacity-achieving properties and practical implementation advantages. However, decoding polar codes remains a computationally intensive task. In this paper, we introduce a novel approach to improve the decoding [...] Read more.
Polar codes have established themselves as a cornerstone in modern error correction coding due to their capacity-achieving properties and practical implementation advantages. However, decoding polar codes remains a computationally intensive task. In this paper, we introduce a novel approach to improve the decoding efficiency of polar codes by integrating the threshold-based SC-Creeper decoding algorithm, originally designed for convolutional codes. Our proposed decoder with an additional cost function seamlessly merges two established decoding paradigms, namely the stack and Fano approaches. The core idea is to leverage the strengths of both decoding techniques to strike a balance between computational efficiency and performance, with an additional method of controlling movement along a code tree. Simulations demonstrate the superiority of the proposed improved SC-Creeper decoder with tuned parameters. The improved SC-Creeper decoder achieves the performance of the CA-SCL-8 decoder in terms of high code rates and overcomes it in terms of the N=1024 code length, while simultaneously surpassing the efficiency of the traditional Fano decoding algorithm. Full article
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