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

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 2455

Special Issue 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-blind 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 1600 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

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Published Papers (4 papers)

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Research

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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
Viewed by 607
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)
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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
Viewed by 600
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)
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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
Viewed by 588
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)
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Other

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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
Viewed by 200
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)
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