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Applied Sciences
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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: 20 June 2025 | Viewed by 13181

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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

Jump to: Review

25 pages, 4366 KiB  
Article
Hybrid AI-Powered Real-Time Distributed Denial of Service Detection and Traffic Monitoring for Software-Defined-Based Vehicular Ad Hoc Networks: A New Paradigm for Securing Intelligent Transportation Networks
by Onur Polat, Saadin Oyucu, Muammer Türkoğlu, Hüseyin Polat, Ahmet Aksoz and Fahri Yardımcı
Appl. Sci. 2024, 14(22), 10501; https://doi.org/10.3390/app142210501 - 14 Nov 2024
Viewed by 1444
Abstract
Vehicular Ad Hoc Networks (VANETs) are wireless networks that improve traffic efficiency, safety, and comfort for smart vehicle users. However, with the rise of smart and electric vehicles, traditional VANETs struggle with issues like scalability, management, energy efficiency, and dynamic pricing. Software Defined [...] Read more.
Vehicular Ad Hoc Networks (VANETs) are wireless networks that improve traffic efficiency, safety, and comfort for smart vehicle users. However, with the rise of smart and electric vehicles, traditional VANETs struggle with issues like scalability, management, energy efficiency, and dynamic pricing. Software Defined Networking (SDN) can help address these challenges by centralizing network control. The integration of SDN with VANETs, forming Software Defined-based VANETs (SD-VANETs), shows promise for intelligent transportation, particularly with autonomous vehicles. Nevertheless, SD-VANETs are susceptible to cyberattacks, especially Distributed Denial of Service (DDoS) attacks, making cybersecurity a crucial consideration for their future development. This study proposes a security system that incorporates a hybrid artificial intelligence model to detect DDoS attacks targeting the SDN controller in SD-VANET architecture. The proposed system is designed to operate as a module within the SDN controller, enabling the detection of DDoS attacks. The proposed attack detection methodology involves the collection of network traffic data, data processing, and the classification of these data. This methodology is based on a hybrid artificial intelligence model that combines a one-dimensional Convolutional Neural Network (1D-CNN) and Decision Tree models. According to experimental results, the proposed attack detection system identified that approximately 90% of the traffic in the SD-VANET network under DDoS attack consisted of malicious DDoS traffic flows. These results demonstrate that the proposed security system provides a promising solution for detecting DDoS attacks targeting the SD-VANET architecture. Full article
(This article belongs to the Special Issue Emerging Technologies in Network Security and Cryptography)
►▼ Show Figures

Figure 1

Figure 1
<p>General architecture of vehicular networks.</p> Full article ">Figure 2
<p>Software Defined Network Architecture.</p> Full article ">Figure 3
<p>Software Defined Vehicular Ad Hoc Network Architecture.</p> Full article ">Figure 4
<p>SD-VANET_Guard intrusion detection system integration.</p> Full article ">Figure 5
<p>The overall architecture of the 1DCNN-DT hybrid artificial intelligence model.</p> Full article ">Figure 6
<p>The convolution process.</p> Full article ">Figure 7
<p>The number of packets received and transmitted through the controller’s interface in the experimental SD-VANET topology.</p> Full article ">Figure 8
<p>CPU utilization of the controller in the experimental SD-VANET topology.</p> Full article ">Figure 9
<p>Percentage of DDoS traffic in the existing network flow within the experimental SD-VANET topology.</p> Full article ">
22 pages, 6228 KiB  
Article
Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
by Gabriel Jekateryńczuk, Damian Jankowski, René Veyland and Zbigniew Piotrowski
Appl. Sci. 2024, 14(9), 3934; https://doi.org/10.3390/app14093934 - 5 May 2024
Viewed by 1434
Abstract
This study investigates the application of steganography for enhancing network security by detecting and promptly eliminating malicious packets to prevent flooding and consequent denial of service attacks while also identifying malicious equipment. The paper discusses foundational concepts such as the prisoner’s dilemma, covert [...] Read more.
This study investigates the application of steganography for enhancing network security by detecting and promptly eliminating malicious packets to prevent flooding and consequent denial of service attacks while also identifying malicious equipment. The paper discusses foundational concepts such as the prisoner’s dilemma, covert channels, qualitative metrics, and existing steganography techniques in computer communications. An architecture was developed to assess the effectiveness of this solution, and experiments were conducted, with their results presented. This contribution leverages established steganographic principles and seamlessly integrates with widely adopted IPsec protocols, offering a solution to improve covert communication within computer networks. Full article
(This article belongs to the Special Issue Emerging Technologies in Network Security and Cryptography)
►▼ Show Figures

Figure 1

Figure 1
<p>Relationship between cryptography, steganography, and watermarking [<a href="#B4-applsci-14-03934" class="html-bibr">4</a>].</p> Full article ">Figure 2
<p>Prisoner’s dilemma.</p> Full article ">Figure 3
<p>Covert channels in ICT networks.</p> Full article ">Figure 4
<p>Relationships between qualitative measures.</p> Full article ">Figure 5
<p>Structure of packets for AH and ESP transport and tunnel modes.</p> Full article ">Figure 6
<p>ESP tunnel mode packet structure.</p> Full article ">Figure 7
<p>Normal traffic packets sequence.</p> Full article ">Figure 8
<p>Modified malicious traffic packet sequence.</p> Full article ">Figure 9
<p>The standard architecture of site-to-site IPsec connection over several networks.</p> Full article ">Figure 10
<p>Use case with two probes for controlling an IPsec flow with a hacker.</p> Full article ">Figure 11
<p>Use case with probes per inter-router link.</p> Full article ">Figure 12
<p>Message encoding/decoding per proximity to the router.</p> Full article ">Figure 13
<p>Use case with a mix of sender, listener, and relay probes.</p> Full article ">Figure 14
<p>Dashboards in Grafana.</p> Full article ">Figure 15
<p>Creating threshold alert in Grafana.</p> Full article ">Figure 16
<p>Notification for fired alert.</p> Full article ">Figure 17
<p>For example, MAC address interception output.</p> Full article ">Figure 18
<p>Network overhead test dashboard.</p> Full article ">
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 - 9 Nov 2023
Cited by 2 | Viewed by 2867
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)
►▼ Show Figures

Figure 1

Figure 1
<p>AES algorithm encryption process.</p> Full article ">Figure 2
<p>Hierarchy of blockchain.</p> Full article ">Figure 3
<p>Hybrid cipher algorithm HAE encryption process.</p> Full article ">Figure 4
<p>Hybrid cipher algorithm HAE decryption process.</p> Full article ">Figure 5
<p>HAE application framework for blockchain healthcare scenarios.</p> Full article ">Figure 6
<p>Result of running the hybrid cipher algorithm.</p> Full article ">Figure 7
<p>Shared address hash.</p> Full article ">Figure 8
<p>Initial medical data.</p> Full article ">Figure 9
<p>Comparison of encryption and decryption time of three cryptographic algorithms.</p> Full article ">Figure 10
<p>Comparison of CPU utilization of three cryptographic algorithms.</p> Full article ">
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
Cited by 1 | Viewed by 1188
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Number of dataset instances with the distribution of classes.</p> Full article ">Figure 2
<p>Flowchart of proposed approach.</p> Full article ">Figure 3
<p>Flowchart of the data classification step.</p> Full article ">Figure 4
<p>Unique and frequency values of features in the dataset.</p> Full article ">Figure 5
<p>Correlation heat map of the dataset features.</p> Full article ">Figure 6
<p>Number of malicious and benign instances in the training and test sets.</p> Full article ">Figure 7
<p>Confusion matrix of classification results using a DFEL model tested on 30% of the dataset without the highly correlated features,.</p> Full article ">Figure 8
<p>Confusion matrix of classification results using a DFEL model tested on 30% of the dataset with the highly correlated features.</p> Full article ">Figure 9
<p>Classification time of all test sets in seconds.</p> Full article ">Figure 10
<p>ROC curves of the DFEL model and its other base models: (<b>a</b>) ROC curve of GB classifier, (<b>b</b>) ROC curve of XGB classifier, (<b>c</b>) ROC curve of RF classifier, and (<b>d</b>) ROC curve of DFEL model.</p> Full article ">Figure 10 Cont.
<p>ROC curves of the DFEL model and its other base models: (<b>a</b>) ROC curve of GB classifier, (<b>b</b>) ROC curve of XGB classifier, (<b>c</b>) ROC curve of RF classifier, and (<b>d</b>) ROC curve of DFEL model.</p> Full article ">Figure 11
<p>Learning curves of the DFEL model and its base models: (<b>a</b>) learning curve of GB classifier, (<b>b</b>) learning curve of XGB classifier, (<b>c</b>) learning curve of RF classifier, and (<b>d</b>) learning curve of DFEL model.</p> Full article ">Figure 11 Cont.
<p>Learning curves of the DFEL model and its base models: (<b>a</b>) learning curve of GB classifier, (<b>b</b>) learning curve of XGB classifier, (<b>c</b>) learning curve of RF classifier, and (<b>d</b>) learning curve of DFEL model.</p> Full article ">Figure 12
<p>Averaged accuracy and F1 score results of the proposed DFEL model trained on a 10-fold cross-validation with and without highly correlated features.</p> Full article ">

Review

Jump to: Research

19 pages, 10215 KiB  
Review
Encryption of 3D or Higher-Dimensional Images: A Review
by Renatta Nigro, Gabriel Ferreira, Maria Alice Andrade Calazans, Geovane Miguel, Felipe Alberto B. S. Ferreira, Verusca Severo and Francisco Madeiro
Appl. Sci. 2025, 15(1), 108; https://doi.org/10.3390/app15010108 - 27 Dec 2024
Viewed by 837
Abstract
This article aims to review encryption techniques for 3D or higher-dimensional images. Precisely, the following classes of images are considered: light field images, point clouds, and 3D medical images. The security and performance aspects of the encryption schemes for images are analyzed. It [...] Read more.
This article aims to review encryption techniques for 3D or higher-dimensional images. Precisely, the following classes of images are considered: light field images, point clouds, and 3D medical images. The security and performance aspects of the encryption schemes for images are analyzed. It is observed that the number of papers with encryption in the transform domain exceeds the number of papers with encryption in the spatial domain. Additionally, it is observed that the application of encryption does not predominate in all the dimensions of the images, that is, the encryption does not occur completely in the image but rather through slices or sections. This review article summarizes several issues related to encryption techniques, technical perspectives for the future, and gaps in the literature. Full article
(This article belongs to the Special Issue Emerging Technologies in Network Security and Cryptography)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) An illustration of a camera array used to capture light field images and (<b>b</b>) an image captured by such a system.</p> Full article ">Figure 2
<p>Schematic of light information captured in a lenslet camera.</p> Full article ">Figure 3
<p>Raw image obtained from a lenslet camera and an expanded portion of the same image.</p> Full article ">Figure 4
<p>Visualization of all views of a light field image captured by a lenslet device, where each view is referenced by a coordinate system <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>s</mi> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math>, along with an expanded central view, in which each pixel is referenced by a coordinate system <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>u</mi> <mo>,</mo> <mi>v</mi> <mo>)</mo> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Examples of 3D point clouds: (<b>a</b>) cloud with 8,128,921 points and (<b>b</b>) cloud with 150,388 points.</p> Full article ">Figure 6
<p>Database research methodology fluxogram.</p> Full article ">Figure 7
<p>Number of publications and citations per year.</p> Full article ">Figure 8
<p>Distribution of the selected papers per database.</p> Full article ">Figure 9
<p>Word cloud.</p> Full article ">Figure 10
<p>Co-citation network.</p> Full article ">Figure 11
<p>Co-occurrence network of five clusters from author keywords.</p> Full article ">Figure 12
<p>Distribution of transforms used in selected articles.</p> Full article ">Figure 13
<p>Representation when encryption occurs completely or partially in the image.</p> Full article ">
16 pages, 257 KiB  
Review
Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization
by Maria Daniela Tache (Ungureanu), Ovidiu Păscuțoiu and Eugen Borcoci
Appl. Sci. 2024, 14(14), 5967; https://doi.org/10.3390/app14145967 - 9 Jul 2024
Cited by 8 | Viewed by 4139
Abstract
Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the [...] Read more.
Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/IP control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control IP networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing. Full article
(This article belongs to the Special Issue Emerging Technologies in Network Security and Cryptography)
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