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Applied Sciences
Special Issues
Emerging Technologies in Network Security and Cryptography
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Emerging Technologies in Network Security and Cryptography

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

Deadline for manuscript submissions: 30 May 2024 | Viewed by 2168

Special Issue Editors

Dr. Masoud Kaveh

E-Mail Website
Guest Editor
Department of Information and Communication Engineering, Aalto University, Espoo‎, ‎Finland
Interests: hardware security; machine learning; cryptographic protocols PUFs; IoT security; blockchain security; FPGA design; VHDL programming; applied cryptography; physical layer security
Dr. Diego Martín

E-Mail Website
Guest Editor
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain
Interests: Internet of Things; blockchain technologies; cyber physical systems; knowledge management; information retrieval
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's rapidly evolving digital landscape, ensuring the security and privacy of network communications has become paramount. The emergence of new technologies brings both opportunities and challenges in the field of network security and cryptography. This Special Issue aims to explore the latest advancements and innovations in these areas, focusing on the development and application of emerging technologies for enhanced network security and cryptography.

Scope and Topics of Interest:

We invite researchers and practitioners to contribute their original research, case studies, and reviews on various topics related to the emerging technologies in network security and cryptography. This Special Issue covers a broad range of subjects, including but not limited to:

  • Blockchain technology for secure and decentralized networks;
  • Machine learning and artificial intelligence for network threat detection and prevention;
  • Quantum-resistant cryptography and post-quantum security algorithms;
  • Privacy-preserving techniques for data protection and anonymity in networks;
  • Secure protocols for Internet of Things (IoT) and cyber-physical systems (CPS);
  • Secure communication and authentication mechanisms in wireless networks;
  • Cloud and edge computing security in distributed systems;
  • Privacy and security challenges in social networks and online platforms;
  • Threat intelligence and analysis for proactive network defense;
  • Secure software-defined networking (SDN) and network function virtualization (NFV);
  • Cryptographic protocols for secure data transmission and storage;
  • Emerging technologies for secure data sharing and collaboration;
  • Secure multi-party computation and homomorphic encryption techniques;
  • Hardware and physical layer security for network infrastructure;
  • Integration of emerging technologies with traditional security mechanisms.

Keynote Lectures and Tutorials:

In addition to research contributions, this Special Issue will include keynote lectures and tutorials from renowned experts in the field of network security and cryptography. These sessions will provide valuable insights into the latest trends, challenges, and future directions in emerging technologies.

Dr. Masoud Kaveh
Dr. Diego Martín
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. 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.

Published Papers (2 papers)

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Research

16 pages, 6863 KiB  
Article
HAE: A Hybrid Cryptographic Algorithm for Blockchain Medical Scenario Applications
by Ziang Chen, Jiantao Gu and Hongcan Yan
Appl. Sci. 2023, 13(22), 12163; https://doi.org/10.3390/app132212163 - 09 Nov 2023
Cited by 1 | Viewed by 1247
Abstract
The integration of cryptographic algorithms like Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) is pivotal in bolstering the core attributes of blockchain technology, especially in achieving decentralization, tamper resistance, and anonymization within the realm of medical applications. Despite their widespread utilization, [...] Read more.
The integration of cryptographic algorithms like Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) is pivotal in bolstering the core attributes of blockchain technology, especially in achieving decentralization, tamper resistance, and anonymization within the realm of medical applications. Despite their widespread utilization, the conventional AES and ECC face significant hurdles in security and efficiency when dealing with expansive medical data, posing a challenge to the effective preservation of patient privacy. In light of these challenges, this study introduces HAE (hybrid AES and ECC), an innovative hybrid cryptographic algorithm that ingeniously amalgamates the robustness of AES with the agility of ECC. HAE is designed to symmetrically encrypt original data with AES while employing ECC for the asymmetric encryption of the initial AES key. This strategy not only alleviates the complexities associated with AES key management but also enhances the algorithm’s security without compromising its efficiency. We provide an in-depth exposition of HAE’s deployment within a framework tailored for medical scenarios, offering empirical insights into its enhanced performance metrics. Our experimental outcomes underscore HAE’s exemplary security, time efficiency, and optimized resource consumption, affirming its potential as a breakthrough advancement for augmenting blockchain applications in the medical sector, heralding a new era of enhanced data security and privacy within this critical domain. Full article
(This article belongs to the Special Issue Emerging Technologies in Network Security and Cryptography)
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23 pages, 5222 KiB  
Article
A Decision-Fusion-Based Ensemble Approach for Malicious Websites Detection
by Abed Alanazi and Abdu Gumaei
Appl. Sci. 2023, 13(18), 10260; https://doi.org/10.3390/app131810260 - 13 Sep 2023
Viewed by 603
Abstract
Malicious websites detection is one of the cyber-security tasks that protects sensitive information such as credit card details and login credentials from attackers. Machine learning (ML)-based methods have been commonly used in several applications of cyber-security research. Although there are some methods and [...] Read more.
Malicious websites detection is one of the cyber-security tasks that protects sensitive information such as credit card details and login credentials from attackers. Machine learning (ML)-based methods have been commonly used in several applications of cyber-security research. Although there are some methods and approaches proposed in the state-of-the-art studies, the advancement of the most effective solution is still of research interest and needs to be improved. Recently, decision fusion methods play an important role in improving the accuracy of ML methods. They are broadly classified based on the type of fusion into a voting decision fusion technique and a divide and conquer decision fusion technique. In this paper, a decision fusion ensemble learning (DFEL) model is proposed based on voting technique for detecting malicious websites. It combines the predictions of three effective ensemble classifiers, namely, gradient boosting (GB) classifier, extreme gradient boosting (XGB) classifier, and random forest (RF) classifier. We use these classifiers because their advantages to perform well for class imbalanced and data with statistical noises such as in the case of malicious websites detection. A weighted majority-voting rule is utilized for generating the final decisions of used classifiers. The experimental results are conducted on a publicly available large dataset of malicious and benign websites. The comparative study exposed that the DFEL model achieves high accuracies, which are 97.25% on average of 10-fold cross-validation test and 98.50% on a holdout of 30% test set. This confirms the ability of proposed approach to improve the detection rate of malicious websites. Full article
(This article belongs to the Special Issue Emerging Technologies in Network Security and Cryptography)
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