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Electronics
Special Issues
Next Generation Networks and Systems Security
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Next Generation Networks and Systems Security

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 10401

Special Issue Editors

Dr. Konstantinos Limniotis

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
Interests: cryptography; privacy; network security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicholas Kolokotronis

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of the Peloponnese, 221 31 Tripoli, Greece
Interests: cyber-security; game-theoretic security; autonomous security; privacy; risk management; cryptography; blockchain; post-quantum cryptography; coding theory; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Stavros Shiaeles

E-Mail Website
Guest Editor
Cyber Security Research Group, School of Computing University of Portsmouth, Portsmouth PO1 2UP, UK
Interests: AI cybersecurity; intrusion detection and response; botnets; malware analysis; forensics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is our pleasure to announce this Special Issue on “Next Generation Networks and Systems Security”.

This Special Issue aims to present new research, as well as to highlight the latest results and current challenges, with respect to next-generation networks and systems security and privacy. In a highly evolving environment, where different technologies such as IoT networks, blockchain technologies, 5G Networks, Wi-Fi networks, cloud computing, etc. are being interconnected and expanded, new threats for security and privacy are constantly appearing. To address them, advanced technologies such as artificial intelligence, machine learning and deep learning, big data analysis, post-quantum cryptography, and privacy-enhancing technologies seem to have a crucial role.

This Special Issue seeks original unpublished papers in this area, focusing on novel system architecture designs, new detection techniques, new results on cryptography (including post-quantum cryptography as well as privacy-enhancing cryptography), either theoretical or practical, as well as on experimental studies\technical reports. Hardware attacks and defenses are also within the scope of the issue. Review/survey papers, as well as papers focusing on addressing/analyzing relevant legal requirements on cybersecurity and privacy, from an engineering perspective, are also highly encouraged.

You may choose our Joint Special Issue in Network.

Dr. Konstantinos Limniotis
Dr. Nicholas Kolokotronis
Dr. Stavros Shiaeles
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. Electronics 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

  • artificial intelligence
  • machine learning
  • deep learning
  • big data analysis
  • blockchain
  • cloud computing
  • cryptography
  • Internet of Things
  • intrusion detection
  • privacy—personal data protection
  • smart networks
  • 5G networks
  • Wi-Fi
  • IEEE 802.11 family of standards

Published Papers (5 papers)

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Research

25 pages, 453 KiB  
Article
Exploring Personal Data Processing in Video Conferencing Apps
by Georgios Achilleos, Konstantinos Limniotis and Nicholas Kolokotronis
Electronics 2023, 12(5), 1247; https://doi.org/10.3390/electronics12051247 - 05 Mar 2023
Cited by 1 | Viewed by 1668
Abstract
The use of video conferencing applications has increased tremendously in recent years, particularly due to the COVID-19 pandemic and the associated restrictions on movements. As a result, the corresponding smart apps have also seen increased usage, leading to a surge in downloads of [...] Read more.
The use of video conferencing applications has increased tremendously in recent years, particularly due to the COVID-19 pandemic and the associated restrictions on movements. As a result, the corresponding smart apps have also seen increased usage, leading to a surge in downloads of video conferencing apps. However, this trend has generated several data protection and privacy challenges inherent in the smart mobile ecosystem. This paper aims to study data protection issues in video conferencing apps by statistically and dynamically analyzing the most common such issues in real-time operation on Android platforms. The goal is to determine what these applications do in real time and verify whether they provide users with sufficient information regarding the underlying personal data processes. Our results illustrate that there is still room for improvement in several aspects, mainly because the relevant privacy policies do not always provide users with sufficient information about the underlying personal data processes (especially with respect to data leaks to third parties), which, in turn, raises concerns about compliance with data protection by design and default principles. Specifically, users are often not informed about which personal data are being processed, for what purposes, and whether these processes are necessary (and, if yes, why) or based on their consent. Furthermore, the permissions required by the apps during runtime are not always justified. Full article
(This article belongs to the Special Issue Next Generation Networks and Systems Security)
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17 pages, 1127 KiB  
Article
Federated Auto-Meta-Ensemble Learning Framework for AI-Enabled Military Operations
by Konstantinos Demertzis, Panayotis Kikiras, Charalabos Skianis, Konstantinos Rantos, Lazaros Iliadis and George Stamoulis
Electronics 2023, 12(2), 430; https://doi.org/10.3390/electronics12020430 - 13 Jan 2023
Cited by 2 | Viewed by 1986
Abstract
One of the promises of AI in the military domain that seems to guarantee its adoption is its broad applicability. In a military context, the potential for AI is present in all operational domains (i.e., land, sea, air, space, and cyber-space) and all [...] Read more.
One of the promises of AI in the military domain that seems to guarantee its adoption is its broad applicability. In a military context, the potential for AI is present in all operational domains (i.e., land, sea, air, space, and cyber-space) and all levels of warfare (i.e., political, strategic, operational, and tactical). However, despite the potential, the convergence between needs and AI technological advances is still not optimal, especially in supervised machine learning for military applications. Training supervised machine learning models requires a large amount of up-to-date data, often unavailable or difficult to produce by one organization. An excellent way to tackle this challenge is federated learning by designing a data pipeline collaboratively. This mechanism is based on implementing a single universal model for all users, trained using decentralized data. Furthermore, this federated model ensures the privacy and protection of sensitive information managed by each entity. However, this process raises severe objections to the effectiveness and generalizability of the universal federated model. Usually, each machine learning algorithm shows sensitivity in managing the available data and revealing the complex relationships that characterize them, so the forecast has some severe biases. This paper proposes a holistic federated learning approach to address the above problem. It is a Federated Auto-Meta-Ensemble Learning (FAMEL) framework. FAMEL, for each user of the federation, automatically creates the most appropriate algorithm with the optimal hyperparameters that apply to the available data in its possession. The optimal model of each federal user is used to create an ensemble learning model. Hence, each user has an up-to-date, highly accurate model without exposing personal data in the federation. As it turns out experimentally, this ensemble model offers better predictability and stability. Its overall behavior smoothens noise while reducing the risk of a wrong choice resulting from under-sampling. Full article
(This article belongs to the Special Issue Next Generation Networks and Systems Security)
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17 pages, 953 KiB  
Article
Dynamic Feedback versus Varna-Based Techniques for SDN Controller Placement Problems
by Wael Hosny Fouad Aly, Hassan Kanj, Samer Alabed, Nour Mostafa and Khaled Safi
Electronics 2022, 11(14), 2273; https://doi.org/10.3390/electronics11142273 - 21 Jul 2022
Cited by 2 | Viewed by 1465
Abstract
During the past few years, software-defined networking (SDN) has become a successful architecture that decouples the control plane from the data plane. SDN has the capability to monitor and control the network in a central fashion through a softwarization process. The central element [...] Read more.
During the past few years, software-defined networking (SDN) has become a successful architecture that decouples the control plane from the data plane. SDN has the capability to monitor and control the network in a central fashion through a softwarization process. The central element is the controller. For the current SDN architectures, there is an essential need for multiple controllers. The process of placing the controllers efficiently in an SDN environment is called the controller placement problem (CPP). Earlier CPP solutions focused on improving the propagation delays through the capacity of the controllers and the dynamic load on the switches. In this paper, we develop a novel algorithm called dynamic feedback algorithm for controller placement for SDN (DFBCPSDN). DFBCPSDN is compared with the varna-based optimization (VBO) towards solving the CPP. We used the VBO as the reference model to this work since it is relatively a new algorithm. Moreover, the VBO extensively outperformed many other existing models. To the best of our knowledge, this is one of the first attempts to minimize the total average latency of SDN using feedback control theoretic techniques. Experimental results indicate that the DFBCPSDN outperforms the VBO algorithm implemented in two well-known topologies, namely Internet2 OS3E topology and EU-GÉANT topology. We observe that for uncapacitated CPP, the DFBCPSDN outperforms the VBO for Internet2 OS3E and EU-GÉANT topologies by 11% and 9%, respectively, in terms of total average latency. On the other hand, for capacitated CPP, the DFBCPSDN algorithm outperforms the VBO reference model by 10% and 8%, respectively. Full article
(This article belongs to the Special Issue Next Generation Networks and Systems Security)
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14 pages, 596 KiB  
Article
Feedback ARMA Models versus Bayesian Models towards Securing OpenFlow Controllers for SDNs
by Wael Hosny Fouad Aly, Hassan Kanj, Nour Mostafa and Samer Alabed
Electronics 2022, 11(9), 1513; https://doi.org/10.3390/electronics11091513 - 09 May 2022
Cited by 1 | Viewed by 1321
Abstract
In software-defined networking (SDN), the control layers are moved away from the forwarding switching layers. SDN gives more programmability and flexibility to the controllers. OpenFlow is a protocol that gives access to the forwarding plane of a network switch or router over the [...] Read more.
In software-defined networking (SDN), the control layers are moved away from the forwarding switching layers. SDN gives more programmability and flexibility to the controllers. OpenFlow is a protocol that gives access to the forwarding plane of a network switch or router over the SDN network. OpenFlow uses a centralized control of network switches and routers in and SDN environment. Security is of major importance for SDN deployment. Transport layer security (TLS) is used to implement security for OpenFlow. This paper proposed a new technique to improve the security of the OpenFlow controller through modifying the TLS implementation. The proposed model is referred to as the secured feedback model using autoregressive moving average (ARMA) for SDN networks (SFBARMASDN). SFBARMASDN depended on computing the feedback for incoming packets based on ARMA models. Filtering techniques based on ARMA techniques were used to filter the packets and detect malicious packets that needed to be dropped. SFBARMASDN was compared to two reference models. One reference model was Bayesian-based and the other reference model was the standard OpenFlow. Full article
(This article belongs to the Special Issue Next Generation Networks and Systems Security)
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26 pages, 959 KiB  
Article
SAGMAD—A Signature Agnostic Malware Detection System Based on Binary Visualisation and Fuzzy Sets
by Betty Saridou, Joseph Ryan Rose, Stavros Shiaeles and Basil Papadopoulos
Electronics 2022, 11(7), 1044; https://doi.org/10.3390/electronics11071044 - 26 Mar 2022
Cited by 10 | Viewed by 2968
Abstract
Image conversion of byte-level data, or binary visualisation, is a relevant approach to security applications interested in malicious activity detection. However, in practice, binary visualisation has always been seen to have great limitations when dealing with large volumes of data, and would be [...] Read more.
Image conversion of byte-level data, or binary visualisation, is a relevant approach to security applications interested in malicious activity detection. However, in practice, binary visualisation has always been seen to have great limitations when dealing with large volumes of data, and would be a reluctant candidate as the core building block of an intrusion detection system (IDS). This is due to the requirements of computational time when processing the flow of byte data into image format. Machine intelligence solutions based on colour tone variations that are intended for pattern recognition would overtax the process. In this paper, we aim to solve this issue by proposing a fast binary visualisation method that uses Fuzzy Set theory and the H-indexing space filling curve. Our model can assign different colour tones on a byte, allowing it to be influenced by neighbouring byte values while preserving optimal locality indexing. With this work, we wish to establish the first steps in pursuit of a signature-free IDS. For our experiment, we used 5000 malicious and benign files of different sizes. Our methodology was tested on various platforms, including GRNET’s High-Performance Computing services. Further improvements in computation time allowed larger files to convert in roughly 0.5 s on a desktop environment. Its performance was also compared with existing machine learning-based detection applications that used traditional binary visualisation. Despite lack of optimal tuning, SAGMAD was able to achieve 91.94% accuracy, 90.63% precision, 92.7% recall, and an F-score of 91.61% on average when tested within previous binary visualisation applications and following their parameterisation scheme. The results exceeded malware file-based experiments and were similar to network intrusion applications. Overall, the results demonstrated here prove our method to be a promising mechanism for a fast AI-based signature-agnostic IDS. Full article
(This article belongs to the Special Issue Next Generation Networks and Systems Security)
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