Special Issue "Mathematical Models in Security, Defense, Cyber Security and Cyber Defense II"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 1438

Special Issue Editors

Dr. Angel Martín-del-Rey
E-Mail Website
Guest Editor
Institute of Fundamental Physics and Mathematics, Department of Applied Mathematics, University of Salamanca, 37008 Salamanca, Spain
Interests: cryptography; mathematical modeling; wireless sensor network security
Special Issues, Collections and Topics in MDPI journals
Dr. Roberto Casado Vara
E-Mail Website
Guest Editor
BISITE research group, University of Salamanca, Edificio Multiusos I+D+i, C/ Espejo s/n, 37007 Salamanca, Spain
Interests: artificial intelligence; nonlinear control; stochastic systems; optimization; blockchain
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lixiang Li
E-Mail Website
Guest Editor
Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: information security; compressed sensing; swarm intelligence; complex network; neural network
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The role of mathematical modeling in theoretical and applied scientific development is crucial. This discipline covers almost all areas of knowledge, providing models that simulate different phenomena and behaviors of all types. Nowadays, one of the most important problems that our society is facing is to guarantee the security of citizens and infrastructures against intentional or non-intentional threats. This refers not only to physical assets but also to those of a logical nature.

The main goal of this Special Issue is to gather high-quality research papers and reviews focused on the design, analysis, and development of mathematical models with applications in the field of security, defense, cyber security, and cyber defense. Specifically, this Special Issue will cover different perspectives on these and related potential topics:

  • Mathematical modeling in security and defense;
  • Mathematical modeling in cyber security and cyber defense;
  • Complex networks analysis in security and defense;
  • Complex network analysis applied to cyber security;
  • Mathematical modeling of biological agents and malware propagation;
  • Mathematical models for critical infrastructure protection;
  • Theoretical and applied cryptography.

Dr. Angel Martín-del-Rey
Dr. Roberto Casado Vara
Prof. Dr. Lixiang Li
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 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. Mathematics 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 2100 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

  • mathematical modeling
  • complex networks
  • security and cyber security
  • defense and cyber defense
  • cryptography

Published Papers (2 papers)

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Research

Article
Retaliation against Ransomware in Cloud-Enabled PureOS System
Mathematics 2023, 11(1), 249; https://doi.org/10.3390/math11010249 - 03 Jan 2023
Viewed by 464
Abstract
Ransomware is malicious software that encrypts data before demanding payment to unlock them. The majority of ransomware variants use nearly identical command and control (C&C) servers but with minor upgrades. There are numerous variations of ransomware, each of which can encrypt either the [...] Read more.
Ransomware is malicious software that encrypts data before demanding payment to unlock them. The majority of ransomware variants use nearly identical command and control (C&C) servers but with minor upgrades. There are numerous variations of ransomware, each of which can encrypt either the entire computer system or specific files. Malicious software needs to infiltrate a system before it can do any real damage. Manually inspecting all potentially malicious file types is a time-consuming and resource-intensive requirement of conventional security software. Using established metrics, this research delves into the complex issues of identifying and preventing ransomware. On the basis of real-world malware samples, we created a parameterized categorization strategy for functional classes and suggestive features. We also furnished a set of criteria that highlights the most commonly featured criteria and investigated both behavior and insights. We used a distinct operating system and specific cloud platform to facilitate remote access and collaboration on files throughout the entire operational experimental infrastructure. With the help of our proposed ransomware detection mechanism, we were able to effectively recognize and prevent both state-of-art and modified ransomware anomalies. Aggregated log revealed a consistent but satisfactory detection rate at 89%. To the best of our knowledge, no research exists that has investigated the ransomware detection and impact of ransomware for PureOS, which offers a unique platform for PC, mobile phones, and resource intensive IoT (Internet of Things) devices. Full article
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Article
Increasing the Effectiveness of Network Intrusion Detection Systems (NIDSs) by Using Multiplex Networks and Visibility Graphs
Mathematics 2023, 11(1), 107; https://doi.org/10.3390/math11010107 - 26 Dec 2022
Viewed by 460
Abstract
In this paper, we present a new approach to NIDS deployment based on machine learning. This new approach is based on detecting attackers by analyzing the relationship between computers over time. The basic idea that we rely on is that the behaviors of [...] Read more.
In this paper, we present a new approach to NIDS deployment based on machine learning. This new approach is based on detecting attackers by analyzing the relationship between computers over time. The basic idea that we rely on is that the behaviors of attackers’ computers are different from those of other computers, because the timings and durations of their connections are different and therefore easy to detect. This approach does not analyze each network packet statistically. It analyzes, over a period of time, all traffic to obtain temporal behaviors and to determine if the IP is an attacker instead of that packet. IP behavior analysis reduces drastically the number of alerts generated. Our approach collects all interactions between computers, transforms them into time series, classifies them, and assembles them into a complex temporal behavioral network. This process results in the complex characteristics of each computer that allow us to detect which are the attackers’ addresses. To reduce the computational efforts of previous approaches, we propose to use visibility graphs instead of other time series classification methods, based on signal processing techniques. This new approach, in contrast to previous approaches, uses visibility graphs and reduces the computational time for time series classification. However, the accuracy of the model is maintained. Full article
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