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Computers
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Using New Technologies in Cyber Security Solutions (2nd Edition)
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Using New Technologies in Cyber Security Solutions (2nd Edition)

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "ICT Infrastructures for Cybersecurity".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 11399

Special Issue Editors

Prof. Dr. Leandros Maglaras

E-Mail Website
Guest Editor
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
Interests: critical infrastructure protection; authentication methods; IDS
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Helge Janicke

E-Mail Website
Guest Editor
Centre for Securing Digital Futures, Edith Cowan University, Perth, WA 6027, Australia
Interests: cyber security; security of industrial control systems/SCADA; digital forensics; cyber physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this Special Issue of Computers, original research articles and reviews are welcome. The main topic of research is cyber security. Novel technologies and methods that can be used in cyber security fields are acceptable. Example research areas may include (but are not limited to) the following:

  • Using blockchain technologies on cyber security solutions;
  • Using LLMs, deep learning and active learning on cyber security solutions;
  • IoT, virtualization and cloud computing security;
  • Security and privacy issues in metaverse;
  • Using pot-quantum cryptographic solutions;
  • Secure smart contracts;
  • Cyber-attacks on blockchain technology;
  • Novel cyber-crimes on social media using deepfake technology.

Recently, most daily life activities have moved into the digital world. These days, it is easier to get benefits from online transactions because many people can be easily manipulated by cyber attackers. According to recent scientific reports, in 2025, cybercrimes will cost the world economy about USD 10 trillion, and cybercrime will be one of the most profitable sectors worldwide. It can be clearly understood that the number of cyber-related crimes are increasing in high volumes, and there is no feasible technique or method which can effectively stop the attackers. When cyber defenders find new solutions to the known attacks, cybercriminals find new attacks which have not been seen before. In other words, most of the time, the cybercriminals are one step ahead. Using new technologies, such as blockchain, smart contracts, virtualization, LLMs, deep learning, active learning, and post-quantum crypto, can be one of the most promising solutions in the cyber security area. Thus, in this Special Issue, we expect original research articles as well as review papers that may apply various new technologies and methods in the cyber security field. This Special Issue will present new ideas on cyber security, expand scholars domain knowledge, and provide possible efficient solutions against cybercriminals.

Prof. Dr. Leandros Maglaras
Prof. Dr. Helge Janicke
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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • LLM cybersecurity solutions
  • post-quantum crypto
  • deepfake attacks
  • metaverse cybersecurity

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

Published Papers (7 papers)

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Research

25 pages, 5901 KiB  
Article
Use of Explainable Artificial Intelligence for Analyzing and Explaining Intrusion Detection Systems
by Pamela Hermosilla, Mauricio Díaz, Sebastián Berríos and Héctor Allende-Cid
Computers 2025, 14(5), 160; https://doi.org/10.3390/computers14050160 - 25 Apr 2025
Viewed by 235
Abstract
The increase in malicious cyber activities has generated the need to produce effective tools for the field of digital forensics and incident response. Artificial intelligence (AI) and its fields, specifically machine learning (ML) and deep learning (DL), have shown great potential to aid [...] Read more.
The increase in malicious cyber activities has generated the need to produce effective tools for the field of digital forensics and incident response. Artificial intelligence (AI) and its fields, specifically machine learning (ML) and deep learning (DL), have shown great potential to aid the task of processing and analyzing large amounts of information. However, models generated by DL are often considered “black boxes”, a name derived due to the difficulties faced by users when trying to understand the decision-making process for obtaining results. This research seeks to address the challenges of transparency, explainability, and reliability posed by black-box models in digital forensics. To accomplish this, explainable artificial intelligence (XAI) is explored as a solution. This approach seeks to make DL models more interpretable and understandable by humans. The SHAP (SHapley Additive eXplanations) and LIME (Local Interpretable Model-agnostic Explanations) methods will be implemented and evaluated as a model-agnostic technique to explain predictions of the generated models for forensic analysis. By applying these methods to the XGBoost and TabNet models trained on the UNSW-NB15 dataset, the results indicated distinct global feature importance rankings between the model types and revealed greater consistency of local explanations for the tree-based XGBoost model compared to the deep learning-based TabNet. This study aims to make the decision-making process in these models transparent and to assess the confidence and consistency of XAI-generated explanations in a forensic context. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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23 pages, 2539 KiB  
Article
Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques
by Ghalia Nassreddine, Mohamad Nassereddine and Obada Al-Khatib
Computers 2025, 14(3), 82; https://doi.org/10.3390/computers14030082 - 25 Feb 2025
Cited by 1 | Viewed by 1084
Abstract
Recent advancements across various sectors have resulted in a significant increase in the utilization of smart gadgets. This augmentation has resulted in an expansion of the network and the devices linked to it. Nevertheless, the development of the network has concurrently resulted in [...] Read more.
Recent advancements across various sectors have resulted in a significant increase in the utilization of smart gadgets. This augmentation has resulted in an expansion of the network and the devices linked to it. Nevertheless, the development of the network has concurrently resulted in a rise in policy infractions impacting information security. Finding intruders immediately is a critical component of maintaining network security. The intrusion detection system is useful for network security because it can quickly identify threats and give alarms. In this paper, a new approach for network intrusion detection was proposed. Combining the results of machine learning models like the random forest, decision tree, k-nearest neighbors, and XGBoost with logistic regression as a meta-model is what this method is based on. For the feature selection technique, the proposed approach creates an advanced method that combines the correlation-based feature selection with an embedded technique based on XGBoost. For handling the challenge of an imbalanced dataset, a SMOTE-TOMEK technique is used. The suggested algorithm is tested on the NSL-KDD and CIC-IDS datasets. It shows a high performance with an accuracy of 99.99% for both datasets. These results prove the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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42 pages, 1173 KiB  
Article
Advancing Cyber Incident Timeline Analysis Through Retrieval-Augmented Generation and Large Language Models
by Fatma Yasmine Loumachi, Mohamed Chahine Ghanem and Mohamed Amine Ferrag
Computers 2025, 14(2), 67; https://doi.org/10.3390/computers14020067 - 13 Feb 2025
Viewed by 1424
Abstract
Cyber timeline analysis or forensic timeline analysis is critical in digital forensics and incident response (DFIR) investigations. It involves examining artefacts and events—particularly their timestamps and associated metadata—to detect anomalies, establish correlations, and reconstruct a detailed sequence of the incident. Traditional approaches rely [...] Read more.
Cyber timeline analysis or forensic timeline analysis is critical in digital forensics and incident response (DFIR) investigations. It involves examining artefacts and events—particularly their timestamps and associated metadata—to detect anomalies, establish correlations, and reconstruct a detailed sequence of the incident. Traditional approaches rely on processing structured artefacts, such as logs and filesystem metadata, using multiple specialised tools for evidence identification, feature extraction, and timeline reconstruction. This paper introduces an innovative framework, GenDFIR, a context-specific approach powered via large language model (LLM) capabilities. Specifically, it proposes the use of Llama 3.1 8B in zero-shot, selected for its ability to understand cyber threat nuances, integrated with a retrieval-augmented generation (RAG) agent. Our approach comprises two main stages: (1) Data preprocessing and structuring: incident events, represented as textual data, are transformed into a well-structured document, forming a comprehensive knowledge base of the incident. (2) Context retrieval and semantic enrichment: a RAG agent retrieves relevant incident events from the knowledge base based on user prompts. The LLM processes the pertinent retrieved context, enabling a detailed interpretation and semantic enhancement. The proposed framework was tested on synthetic cyber incident events in a controlled environment, with results assessed using DFIR-tailored, context-specific metrics designed to evaluate the framework’s performance, reliability, and robustness, supported by human evaluation to validate the accuracy and reliability of the outcomes. Our findings demonstrate the practical power of LLMs in advancing the automation of cyber-incident timeline analysis, a subfield within DFIR. This research also highlights the potential of generative AI, particularly LLMs, and opens new possibilities for advanced threat detection and incident reconstruction. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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18 pages, 974 KiB  
Article
Generative AI-Enhanced Cybersecurity Framework for Enterprise Data Privacy Management
by Geeta Sandeep Nadella, Santosh Reddy Addula, Akhila Reddy Yadulla, Guna Sekhar Sajja, Mohan Meesala, Mohan Harish Maturi, Karthik Meduri and Hari Gonaygunta
Computers 2025, 14(2), 55; https://doi.org/10.3390/computers14020055 - 8 Feb 2025
Viewed by 1719
Abstract
This study presents a Generative AI-Enhanced Cybersecurity Framework designed to strengthen enterprise data privacy management while improving threat detection accuracy and scalability. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic datasets that mimic [...] Read more.
This study presents a Generative AI-Enhanced Cybersecurity Framework designed to strengthen enterprise data privacy management while improving threat detection accuracy and scalability. By leveraging Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and traditional anomaly detection methods, the framework generates synthetic datasets that mimic real-world data, ensuring privacy and regulatory compliance. At its core, the anomaly detection engine integrates machine learning models, such as Random Forest and Support Vector Machines (SVMs), alongside deep learning techniques like Long Short-Term Memory (LSTM) networks, delivering robust performance across diverse domains. Experimental results demonstrate the framework’s adaptability and high performance in the financial sector (accuracy: 94%, recall: 95%), healthcare (accuracy: 96%, precision: 93%), and smart city infrastructures (accuracy: 91%, F1 score: 90%). The framework achieves a balanced trade-off between accuracy (0.96) and computational efficiency (processing time: 1.5 s per transaction), making it ideal for real-time enterprise deployments. Unlike analog systems that achieve > 0.99 accuracy at the cost of higher resource consumption and limited scalability, this framework emphasizes practical applications in diverse sectors. Additionally, it employs differential privacy, encryption, and data masking to ensure data security while addressing modern cybersecurity challenges. Future work aims to enhance real-time scalability further and explore reinforcement learning to advance proactive threat mitigation measures. This research provides a scalable, adaptive, and practical solution for enterprise-level cybersecurity and data privacy management. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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18 pages, 1484 KiB  
Article
Noise-Based Active Defense Strategy for Mitigating Eavesdropping Threats in Internet of Things Environments
by Abdallah Farraj and Eman Hammad
Computers 2025, 14(1), 6; https://doi.org/10.3390/computers14010006 - 27 Dec 2024
Viewed by 891
Abstract
Establishing robust cybersecurity for Internet of Things (IoT) ecosystems poses significant challenges for system operators due to IoT resource constraints, trade-offs between security and performance, diversity of applications, and their security requirements, usability, and scalability. This article introduces a physical-layer security (PLS) approach [...] Read more.
Establishing robust cybersecurity for Internet of Things (IoT) ecosystems poses significant challenges for system operators due to IoT resource constraints, trade-offs between security and performance, diversity of applications, and their security requirements, usability, and scalability. This article introduces a physical-layer security (PLS) approach that enables IoT devices to maintain specified levels of information confidentiality against wireless channel eavesdropping threats. This work proposes applying PLS active defense mechanisms utilizing spectrum-sharing schemes combined with fair scheduling and power management algorithms to mitigate the risk of eavesdropping attacks on resource-constrained IoT environments. Specifically, an IoT device communicating over an insecure wireless channel will utilize intentional noise signals transmitted alongside the actual IoT information signal. The intentional noise signal will appear to an eavesdropper (EVE) as additional noise, reducing the EVE’s signal-to-interference-plus-noise ratio (SINR) and increasing the EVE’s outage probability, thereby restricting their capacity to decode the transmitted IoT information, resulting in better protection for the confidentiality of the IoT device’s transmission. The proposed communication strategy serves as a complementary solution to existing security methods. Analytical and numerical analyses presented in this article validate the effectiveness of the proposed strategy, demonstrating that IoT devices can achieve the desired levels of confidentiality. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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30 pages, 5129 KiB  
Article
Open-Source Artificial Intelligence Privacy and Security: A Review
by Younis Al-Kharusi, Ajmal Khan, Muhammad Rizwan and Mohammed M. Bait-Suwailam
Computers 2024, 13(12), 311; https://doi.org/10.3390/computers13120311 - 26 Nov 2024
Viewed by 3897
Abstract
This paper reviews the privacy and security challenges posed by open-source artificial intelligence (AI) models. The increased use of open-source machine learning models, while beneficial for resource efficiency and collaboration, has introduced significant privacy risks and security vulnerabilities. Key threats include model inversion, [...] Read more.
This paper reviews the privacy and security challenges posed by open-source artificial intelligence (AI) models. The increased use of open-source machine learning models, while beneficial for resource efficiency and collaboration, has introduced significant privacy risks and security vulnerabilities. Key threats include model inversion, membership inference, data leakage, and backdoor attacks, which could expose sensitive data or compromise system integrity. Our review highlights that many open-source models are vulnerable to these attacks due to their transparency and accessibility. We also identify that adversarial training, differential privacy (DP), and model sanitization techniques can effectively mitigate some of these risks, though achieving a balance between transparency and security remains a challenge. The findings highlight the need for continuous research and innovation to ensure that open-source AI models remain both secure and privacy-compliant in increasingly critical applications across various industries. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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30 pages, 566 KiB  
Article
Area–Time-Efficient High-Radix Modular Inversion Algorithm and Hardware Implementation for ECC over Prime Fields
by Yamin Li
Computers 2024, 13(10), 265; https://doi.org/10.3390/computers13100265 - 12 Oct 2024
Viewed by 1380
Abstract
Elliptic curve cryptography (ECC) is widely used for secure communications, because it can provide the same level of security as RSA with a much smaller key size. In constrained environments, it is important to consider efficiency, in terms of execution time and hardware [...] Read more.
Elliptic curve cryptography (ECC) is widely used for secure communications, because it can provide the same level of security as RSA with a much smaller key size. In constrained environments, it is important to consider efficiency, in terms of execution time and hardware costs. Modular inversion is a key time-consuming calculation used in ECC. Its hardware implementation requires extensive hardware resources, such as lookup tables and registers. We investigate the state-of-the-art modular inversion algorithms, and evaluate the performance and cost of the algorithms and their hardware implementations. We then propose a high-radix modular inversion algorithm aimed at reducing the execution time and hardware costs. We present a detailed radix-8 hardware implementation based on 256-bit primes in Verilog HDL and compare its cost performance to other implementations. Our implementation on the Altera Cyclone V FPGA chip used 1227 ALMs (adaptive logic modules) and 1037 registers. The modular inversion calculation took 3.67 ms. The AT (area–time) factor was 8.30, outperforming the other implementations. We also present an implementation of ECC using the proposed radix-8 modular inversion algorithm. The implementation results also showed that our modular inversion algorithm was more efficient in area–time than the other algorithms. Full article
(This article belongs to the Special Issue Using New Technologies in Cyber Security Solutions (2nd Edition))
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