Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
10.0
4.6
Journals
Future Internet
Special Issues
Cybersecurity in the Age of AI, IoT, and Edge Computing
share announcement

Cybersecurity in the Age of AI, IoT, and Edge Computing

A Special Issue of Future Internet (ISSN 1999-5903) belonging to the section "Cybersecurity".

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 24757

Editors

Dr. Yuji Sekiya

E-Mail Website
Guest Editor
Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
Interests: network protocol; cloud computing; distributed system; cybersecurity
Dr. Masashi Eto

E-Mail Website
Guest Editor
National Cyber Observation Center, National Institute of Information and Communications Technology, Tokyo 184-8795, Japan
Interests: darknet monitoring; honeypot; intrusion detection; malware analysis and auto-configuration of the Internetworking

Special Issue Information

Dear Colleagues,

Cybersecurity is an essential foundational technology for the stable operation of the IT systems that support modern society. As AI technology becomes increasingly integrated into social systems, attacks targeting AI pose serious risks that could directly impact societal safety. Furthermore, fields such as IoT, edge computing, mobile communication networks, and robotics are core components of future social infrastructure. Protecting these technologies from cyber threats is a prerequisite for building a safe and secure society.

Ensuring that these advanced foundational technologies are cybersecure has become an urgent research challenge and a key strategic issue, not only in academia but also across industry. To safeguard the IT infrastructure that society depends on, advancements in cybersecurity research and its practical application are required.

Dr. Yuji Sekiya
Dr. Masashi Eto
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet 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

  • cybersecurity for user environments
  • cybersecurity for reliability and fault-tolerance systems
  • information security and privacy protection in AI
  • security, privacy, and trust issues in IT systems
  • AI and machine learning technologies for cybersecurity
  • cybersecurity for AI and AI-systems
  • zero-trust security for information protection and system design
  • countermeasure against cyber threats using AI technologies
  • secure architecture for large-scale IoT systems and edge computing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (9 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

26 pages, 1466 KB  
Article
Securing the Internet of Things, Lightweight Mutual Authentication Based on Quantum Key Distribution
by Muhammad Nawaz Khan, Inam Ullah, Sokjoon Lee and Mohsin Shah
Future Internet 2026, 18(5), 230; https://doi.org/10.3390/fi18050230 - 24 Apr 2026
Viewed by 854
Abstract
The Internet of Things (IoT) and quantum computing revolutionized the era of conventional and classical computing into a new paradigm of Quantum-IoT where qubits and entanglement make IoT more interactive, powerful, and secure. They facilitate numerous tasks by increasing productivity and efficiency, paving [...] Read more.
The Internet of Things (IoT) and quantum computing revolutionized the era of conventional and classical computing into a new paradigm of Quantum-IoT where qubits and entanglement make IoT more interactive, powerful, and secure. They facilitate numerous tasks by increasing productivity and efficiency, paving the path for a smarter and more connected future. In this article, we propose a novel authentication scheme, “Securing the Internet of Things, Lightweight Mutual Authentication Based on Quantum Key Distribution (LMA-QIoT)”. LMA-QIoT enables mutual authentication using various parameters including quantum key distribution, symmetric keys and timestamps, as well as additional quantum random numbers. All these parameters play a crucial role in thwarting man-in-the-middle, backtracking and nonce reuse attacks. The evaluation of LMA-QIoT demonstrates that quantum key distribution and quantum numbers enhance system performance by reducing CPU usage by 25% and memory requirements 30% compared to an IoT edge-based system and without a server, respectively. In the reconfiguration ratio, the efficiency metric grows exponentially and remains constant on the initial line in edge-server-based systems. In comparison, LMA-QIoT confirms a much reduced overall computational complexity by 16.64%, with the lowest computational cost of O(n2). At 1024 Bytes, the original data length and increased data length (normalized) sizes stay constant with 2logn(klogn). Comparing the total overhead, LMA-QIoT demonstrates a reduction of 33 ms, which corresponds to approximately 16.63% less than the baseline mechanisms. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)
►▼ Show Figures

Graphical abstract

27 pages, 3391 KB  
Article
A Hybrid Federated–Incremental Learning Framework for Continuous Authentication in Zero-Trust Networks
by Jie Ji, Shi Qiu, Shengpeng Ye and Xin Liu
Future Internet 2026, 18(3), 154; https://doi.org/10.3390/fi18030154 - 16 Mar 2026
Cited by 1 | Viewed by 930
Abstract
Zero-trust architecture (ZTA) requires continuous and adaptive identity authentication to maintain security in dynamic environments. However, current federated learning (FL)-based authentication models often struggle to incorporate evolving attack patterns without experiencing catastrophic forgetting. Moreover, non-independent and identically distributed (non-IID) client data and concept [...] Read more.
Zero-trust architecture (ZTA) requires continuous and adaptive identity authentication to maintain security in dynamic environments. However, current federated learning (FL)-based authentication models often struggle to incorporate evolving attack patterns without experiencing catastrophic forgetting. Moreover, non-independent and identically distributed (non-IID) client data and concept drift frequently lead to degraded model robustness and personalization. To address these issues, this paper presents a hybrid learning framework that integrates federated learning with incremental learning (IL) for sustainable authentication. A Dynamic Weighted Federated Aggregation (DWFA) algorithm is developed to mitigate concept drift by adjusting aggregation weights in real time, ensuring that the global model adapts to changing data distributions. This approach enables continuous learning from distributed threat data while maintaining privacy and eliminating the need for historical data retention. Experimental results on real-world traffic datasets indicate that the proposed framework outperforms conventional FL baselines, reducing the overall error rate by approximately 56% and improving the detection rate for novel attack types by over 17.8%. Furthermore, the framework remains stable against performance decay while maintaining efficient communication overhead. This study provides an adaptive, privacy-preserving solution for identity authentication in zero-trust systems. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)
►▼ Show Figures

Graphical abstract

46 pages, 3751 KB  
Article
Wangiri Fraud Detection: A Comprehensive Approach to Unlabeled Telecom Data
by Amirreza Balouchi, Meisam Abdollahi, Ali Eskandarian, Kianoush Karimi Pour Kerman, Elham Majd, Neda Azouji and Amirali Baniasadi
Future Internet 2026, 18(1), 15; https://doi.org/10.3390/fi18010015 - 27 Dec 2025
Cited by 2 | Viewed by 2882
Abstract
Wangiri fraud is a pervasive telecommunications scam that exploits missed calls to lure victims into dialing premium-rate numbers, resulting in significant financial losses for operators and consumers. This paper presents a comprehensive machine learning framework for detecting Wangiri fraud in highly imbalanced and [...] Read more.
Wangiri fraud is a pervasive telecommunications scam that exploits missed calls to lure victims into dialing premium-rate numbers, resulting in significant financial losses for operators and consumers. This paper presents a comprehensive machine learning framework for detecting Wangiri fraud in highly imbalanced and unlabeled Call Detail Record (CDR) datasets. We introduce a novel unsupervised labeling approach using domain-driven heuristics, coupled with advanced feature engineering to capture temporal, geographic, and behavioral patterns indicative of fraud. To address severe class imbalance, we evaluate multiple sampling strategies like the Synthetic Minority Over-sampling Technique (SMOTE) and undersampling, and also compare the performance of Logistic Regression, Decision Trees, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). Our results demonstrate that ensemble methods, particularly Random Forest and XGBoost, achieve near-perfect accuracy (e.g., Receiver Operating Characteristic Area Under the Curve (ROC-AUC) >0.99) on balanced data while maintaining interpretability. The proposed pipeline offers a scalable and practical solution for real-time fraud detection, providing telecom operators with an effective tool to mitigate Wangiri fraud risks. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)
►▼ Show Figures

Graphical abstract

25 pages, 9168 KB  
Article
A Resilient Deep Learning Framework for Mobile Malware Detection: From Architecture to Deployment
by Aysha Alfaw, Mohsen Rouached and Aymen Akremi
Future Internet 2025, 17(12), 532; https://doi.org/10.3390/fi17120532 - 21 Nov 2025
Cited by 2 | Viewed by 1683
Abstract
Mobile devices are frequent targets of malware due to the large volume of sensitive personal, financial, and corporate data they process. Traditional static, dynamic, and hybrid analysis methods are increasingly insufficient against evolving threats. This paper proposes a resilient deep learning framework for [...] Read more.
Mobile devices are frequent targets of malware due to the large volume of sensitive personal, financial, and corporate data they process. Traditional static, dynamic, and hybrid analysis methods are increasingly insufficient against evolving threats. This paper proposes a resilient deep learning framework for Android malware detection, integrating multiple models and a CPU-aware selection algorithm to balance accuracy and efficiency on mobile devices. Two benchmark datasets (i.e., the Android Malware Dataset for Machine Learning and CIC-InvesAndMal2019) were used to evaluate five deep learning models: DNN, CNN, RNN, LSTM, and CNN-LSTM. The results show that CNN-LSTM achieves the highest detection accuracy of 97.4% on CIC-InvesAndMal2019, while CNN delivers strong accuracy of 98.07%, with the lowest CPU usage (5.2%) on the Android Dataset, making it the most practical for on-device deployment. The framework is implemented as an Android application using TensorFlow Lite, providing near-real-time malware detection with an inference time of under 150 ms and memory usage below 50 MB. These findings confirm the effectiveness of deep learning for mobile malware detection and demonstrate the feasibility of deploying resilient detection systems on resource-constrained devices. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)
►▼ Show Figures

Figure 1

16 pages, 793 KB  
Article
Zero-Copy Messaging: Low-Latency Inter-Task Communication in CHERI-Enabled RTOS
by Mina Soltani Siapoush and Jim Alves-Foss
Future Internet 2025, 17(11), 506; https://doi.org/10.3390/fi17110506 - 4 Nov 2025
Cited by 1 | Viewed by 1957
Abstract
Efficient and secure inter-task communication (ITC) is critical in real-time embedded systems, particularly in security-sensitive architectures. Traditional ITC mechanisms in Real-Time Operating Systems (RTOSs) often incur high latency from kernel trapping, context-switch overhead, and multiple data copies during message passing. This paper introduces [...] Read more.
Efficient and secure inter-task communication (ITC) is critical in real-time embedded systems, particularly in security-sensitive architectures. Traditional ITC mechanisms in Real-Time Operating Systems (RTOSs) often incur high latency from kernel trapping, context-switch overhead, and multiple data copies during message passing. This paper introduces a zero-copy, capability-protected ITC framework for CHERI-enabled RTOS environments that achieves both high performance and strong compartmental isolation. The approach integrates mutexes and semaphores encapsulated as sealed capabilities, a shared memory ring buffer for messaging, and compartment-local stubs to eliminate redundant data copies and reduce cross-compartment transitions. Temporal safety is ensured through hardware-backed capability expiration, mitigating use-after-free vulnerabilities. Implemented as a reference application on the CHERIoT RTOS, the framework delivers up to 3× lower mutex lock latency and over 70% faster message transfers compared to baseline FreeRTOS, while preserving deterministic real-time behavior. Security evaluation confirms resilience against unauthorized access, capability leakage, and TOCTTO vulnerabilities. These results demonstrate that capability-based zero-copy ITC can be a practical and performance-optimal solution for constrained embedded systems that demand high throughput, low latency, and verifiable isolation guarantees. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)
►▼ Show Figures

Figure 1

26 pages, 1474 KB  
Article
Eavesdropper Detection in Six-State Protocol Against Partial Intercept–Resend Attack
by Francesco Fiorini, Rosario Giuseppe Garroppo, Michele Pagano and Rostyslav Schiavini Yadzhak
Future Internet 2025, 17(11), 500; https://doi.org/10.3390/fi17110500 - 31 Oct 2025
Cited by 3 | Viewed by 1969
Abstract
This work presents and evaluates two threshold-based detection methods for the Six-State quantum key distribution protocol, considering a realistic scenario involving partial intercept–resend attack and channel noise. The statistical properties of the shared quantum bit error rate (QBER) are analyzed and used to [...] Read more.
This work presents and evaluates two threshold-based detection methods for the Six-State quantum key distribution protocol, considering a realistic scenario involving partial intercept–resend attack and channel noise. The statistical properties of the shared quantum bit error rate (QBER) are analyzed and used to estimate the attacker interception density from observed data. Building on this foundation, the work derives two optimal QBER detection thresholds designed to minimize both false positive and false negative rates, following, respectively, upper theoretical bounds and limit probability density function approach. A developed Qiskit simulation environment enables the evaluation and comparison of the two detection methods on simulated and real-inspired quantum systems with differing noise characteristics. This framework moves beyond theoretical analysis, allowing practical investigation of system noise effects on detection accuracy. Simulation results confirm that both methods are robust and effective, achieving high detection accuracy across all the tested configurations, thereby validating their applicability to real-world quantum communication systems. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

35 pages, 13595 KB  
Review
A Comprehensive Survey on 5G RedCap: Technologies, Security Vulnerabilities, and Attack Vectors
by Pavan Raja I, Kurunandan Jain, Hari N. N, Sethu Subramanian N and Prabhakar Krishnan
Future Internet 2026, 18(3), 118; https://doi.org/10.3390/fi18030118 - 27 Feb 2026
Cited by 3 | Viewed by 3534
Abstract
While 5G addresses extreme performance tiers, 3GPP Releases 17 and 18 RedCap fill critical mid-tier performance gaps for diverse applications like industrial sensors and consumer wearables. The existing academic literature remains fragmented, focusing on isolated metrics rather than a holistic synthesis. There is [...] Read more.
While 5G addresses extreme performance tiers, 3GPP Releases 17 and 18 RedCap fill critical mid-tier performance gaps for diverse applications like industrial sensors and consumer wearables. The existing academic literature remains fragmented, focusing on isolated metrics rather than a holistic synthesis. There is a significant need to integrate technical specifications with empirical industry data. This survey systematically reviews Release 17/18 specifications, integrating literature from 2021 to 2025. We consolidate academic simulations and industry empirical reports to facilitate a rigorous comparative analysis across critical performance indicators. Findings evaluate complexity reduction via bandwidth limitation, antenna reduction, and HD-FDD. We provide a comprehensive security threat matrix, mapping vulnerabilities like RACH spoofing and paging suppression to countermeasures. RedCap cannot match eMBB throughput or NB-IoT’s battery life. Consequently, legacy LPWA remains more suitable for simple, decade-long sensing tasks. This work contributes a novel use-case taxonomy and a security analysis. This study provides practitioners with actionable insights into complexity trade-offs and network security risks. Future research should prioritize AI-driven management and “zero-maintenance” IoT through advanced power-saving innovations. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research, Review

27 pages, 1227 KB  
Systematic Review
Enhancing Network Intrusion Detection with Quantum Machine Learning: A Comprehensive Survey of Methods, Metrics, and Applications
by Antanios Kaissar, Ali Bou Nassif and Ahmed Bouridane
Future Internet 2026, 18(5), 234; https://doi.org/10.3390/fi18050234 - 27 Apr 2026
Cited by 4 | Viewed by 2137
Abstract
Quantum computing introduces new computational capabilities that can support advanced cybersecurity solutions when combined with machine learning. In recent years, quantum machine learning (QML) has emerged as a promising approach for enhancing network intrusion detection systems (IDS), particularly for analyzing complex and high-dimensional [...] Read more.
Quantum computing introduces new computational capabilities that can support advanced cybersecurity solutions when combined with machine learning. In recent years, quantum machine learning (QML) has emerged as a promising approach for enhancing network intrusion detection systems (IDS), particularly for analyzing complex and high-dimensional network traffic. This paper presents a systematic survey of QML techniques applied to network intrusion detection. The survey reviews peer-reviewed studies published up to January 2026 that employ quantum, hybrid quantum–classical, and quantum-inspired learning models for IDS. The selected studies are analyzed with respect to the algorithms used, intrusion detection datasets, and evaluation metrics reported. The analysis shows that most current approaches rely on simulated quantum environments and legacy datasets, while evaluation practices remain inconsistent across studies. These findings highlight the early developmental stage of QML-based IDS and the need for standardized evaluation protocols and more realistic experimental settings. Finally, open challenges and future research directions are identified to support the development of reliable, scalable, and practically deployable QML-based intrusion detection systems. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)
►▼ Show Figures

Graphical abstract

26 pages, 614 KB  
Systematic Review
Cybersecurity in Higher Education Institutions: A Systematic Review of Emerging Trends, Challenges and Solutions
by Oladele Afolalu and Mohohlo Samuel Tsoeu
Future Internet 2025, 17(12), 575; https://doi.org/10.3390/fi17120575 - 15 Dec 2025
Cited by 12 | Viewed by 7630
Abstract
Higher education institutions (HEIs) are increasingly becoming vulnerable to cyberattacks as they adopt digital technologies to support their administrative, research and academic activities. These institutions, which typically operate in open and decentralized environments, face serious challenges as a result of the growing complexity [...] Read more.
Higher education institutions (HEIs) are increasingly becoming vulnerable to cyberattacks as they adopt digital technologies to support their administrative, research and academic activities. These institutions, which typically operate in open and decentralized environments, face serious challenges as a result of the growing complexity of cyberattacks such as phishing, ransomware and data breaches. This systematic review synthesizes existing literature on cybersecurity in HEIs, identifying key challenges, emerging solutions and current trends. The review analyses the adoption of advanced technologies such as zero trust architectures (ZTAs), artificial intelligence (AI)-driven security and cloud-based systems. Furthermore, it investigates the underlying causes of cybersecurity vulnerabilities, including fragmented security procedures, lack of proper awareness about cybersecurity among users and associated technology gaps. The review also examines how governance frameworks, institutional policies and the incorporation of state-of-the-art security technologies can significantly mitigate these threats. Findings reveal that considerable progress has been made by some institutions in implementing security measures. However, comprehensive cybersecurity plans that integrate technological solutions with a robust institutional culture of cybersecurity awareness are still critically needed. The review concludes by highlighting the need for HEIs to collaborate and foster institution-wide partnership to strengthen cybersecurity measures. Finally, an in-depth study into the strategies and best practices for handling emerging cyberthreats in the HEIs is recommended. Full article
(This article belongs to the Special Issue Cybersecurity in the Age of AI, IoT, and Edge Computing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-9
Back to TopTop