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Future Internet
Special Issues
Intrusion Detection and Resiliency in Cyber-Physical Systems and Networks
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Intrusion Detection and Resiliency in Cyber-Physical Systems and Networks

A special issue of Future Internet (ISSN 1999-5903).

Deadline for manuscript submissions: 20 August 2025 | Viewed by 2338

Special Issue Editors

Dr. Olusola Tolulope Odeyomi

E-Mail Website
Guest Editor
Department of Computer Science, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA
Interests: machine learning; social networks; deep learning; natural-language processing; intrusion detection
Special Issues, Collections and Topics in MDPI journals
Dr. Temitayo Olowu

E-Mail Website
Guest Editor
Idaho National Laboratory, 1955 Fremont Ave, Idaho Falls, ID 83415, USA
Interests: renewable energy systems integration; power systems' control and optimization; power electronics control; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In an increasingly connected world, cyberattacks represent stealthy and often devastating intrusions into networks, systems, and infrastructure. These attacks have evolved in sophistication and frequency, leading to severe consequences such as security breaches, financial losses, and compromised critical systems. Addressing these challenges requires innovative and robust intrusion detection methods that enhance the security and resilience of cyber–physical systems and networks. This Special Issue invites researchers to contribute original and impactful research focusing on intrusion detection techniques and novel approaches in cybersecurity. Emphasis is placed on leveraging cutting-edge technologies, such as artificial intelligence (AI), machine learning, blockchain, and quantum security, to address contemporary threats effectively. Additionally, methods that ensure resiliency and adaptability in cyber–physical systems are of particular interest, especially those operating in dynamic and adversarial environments.

We will provide a platform for interdisciplinary research that bridges gaps between traditional cybersecurity practices and emerging AI and system resiliency techniques. Contributions addressing practical applications in industries such as smart cities, healthcare, transportation, and energy systems are especially welcome.

We invite high-quality research articles, reviews, and case studies. Contributions should present theoretical innovations, practical applications, or both, with clear implications for advancing security and resiliency in cyber–physical systems and networks. We encourage submissions that address, but are not limited to, the following topics:

  • Machine Learning and Federated Learning: Application of centralized and decentralized machine learning methods to enhance intrusion detection across distributed systems;
  • Resiliency and Robustness: Techniques to ensure systems remain operational despite ongoing attacks or failures;
  • Quantum Security: Novel cryptographic techniques to safeguard systems against quantum-computing-based threats;
  • Adversarial Learning: Strategies to mitigate the impacts of adversarial attacks on AI models;
  • Deep Fake Detection: Identifying and mitigating threats posed by deep fake technologies in communications and operations;
  • Intrusion Detection: Development of scalable, efficient, and accurate methods for detecting unauthorized access;
  • Data Breaches and Privacy Preservation: Techniques to prevent data breaches while maintaining user privacy;
  • Malware Analysis and Detection: Advanced approaches for identifying and neutralizing malware threats;
  • Sybil Attacks and Byzantine Faults: Detection and prevention of attacks targeting distributed systems and blockchains;
  • Ransomware Mitigation: Strategies to detect and respond to ransomware attacks;
  • Blockchain Technology: Leveraging blockchain for secure data sharing and intrusion detection;
  • Honeypots: Deployment and analysis of honeypots for luring and studying attackers;
  • Trustworthy AI: Ensuring AI systems are secure, interpretable, and resilient to attacks;
  • Differential Privacy and Anonymization: Techniques to maintain user anonymity while analyzing sensitive data;
  • Large-Language Models (LLMs): Utilizing and safeguarding advanced AI models in intrusion detection;
  • Phishing Attack Countermeasures: Strategies to identify and neutralize phishing threats effectively.

Dr. Olusola Tolulope Odeyomi
Dr. Temitayo Olowu
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. 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

  • intrusion detection
  • cyber–physical systems
  • machine learning
  • federated learning
  • adversarial learning
  • intrusion detection
  • malware detection
  • blockchain technology
  • trustworthy AI
  • large-language models

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Published Papers (3 papers)

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Research

14 pages, 274 KiB  
Article
Multi-Class Intrusion Detection in Internet of Vehicles: Optimizing Machine Learning Models on Imbalanced Data
by Ágata Palma, Mário Antunes, Jorge Bernardino and Ana Alves
Future Internet 2025, 17(4), 162; https://doi.org/10.3390/fi17040162 - 7 Apr 2025
Viewed by 303
Abstract
The Internet of Vehicles (IoV) presents complex cybersecurity challenges, particularly against Denial-of-Service (DoS) and spoofing attacks targeting the Controller Area Network (CAN) bus. This study leverages the CICIoV2024 dataset, comprising six distinct classes of benign traffic and various types of attacks, to evaluate [...] Read more.
The Internet of Vehicles (IoV) presents complex cybersecurity challenges, particularly against Denial-of-Service (DoS) and spoofing attacks targeting the Controller Area Network (CAN) bus. This study leverages the CICIoV2024 dataset, comprising six distinct classes of benign traffic and various types of attacks, to evaluate advanced machine learning techniques for instrusion detection systems (IDS). The models XGBoost, Random Forest, AdaBoost, Extra Trees, Logistic Regression, and Deep Neural Network were tested under realistic, imbalanced data conditions, ensuring that the evaluation reflects real-world scenarios where benign traffic dominates. Using hyperparameter optimization with Optuna, we achieved significant improvements in detection accuracy and robustness. Ensemble methods such as XGBoost and Random Forest consistently demonstrated superior performance, achieving perfect accuracy and macro-average F1-scores, even when detecting minority attack classes, in contrast to previous results for the CICIoV2024 dataset. The integration of optimized hyperparameter tuning and a broader methodological scope culminated in an IDS framework capable of addressing diverse attack scenarios with exceptional precision. Full article
(This article belongs to the Special Issue Intrusion Detection and Resiliency in Cyber-Physical Systems and Networks)
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20 pages, 2207 KiB  
Article
A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
by Amanda Thomson, Leandros Maglaras and Naghmeh Moradpoor
Future Internet 2025, 17(3), 120; https://doi.org/10.3390/fi17030120 - 7 Mar 2025
Viewed by 696
Abstract
Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and [...] Read more.
Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and complex phishing sites that are designed to deceive and expose. Tracking, blocking and detecting such domains is complex, and very often it involves complex allowlist or denylist management or SIEM integration with open-source TLS fingerprinting techniques. Many fingerprinting techniques, such as JARM and JA3, are used by threat hunters to determine domain classification, but with the increase in TLS similarity, particularly in CDNs, they are becoming less useful. The aim of this paper was to adapt and evolve open-source TLS fingerprinting techniques with increased features to enhance granularity and to produce a similarity-mapping system that would enable the tracking and detection of previously unknown malicious domains. This was achieved by enriching TLS fingerprints with HTTP header data and producing a fine-grain similarity visualisation that represented high-dimensional data using MinHash and Locality-Sensitive Hashing. Influence was taken from the chemistry domain, where the problem of high-dimensional similarity in chemical fingerprints is often encountered. An enriched fingerprint was produced, which was then visualised across three separate datasets. The results were analysed and evaluated, with 67 previously unknown malicious domains being detected based on their similarity to known malicious domains and nothing else. The similarity-mapping technique produced demonstrates definite promise in the arena of early detection of malware and phishing domains. Full article
(This article belongs to the Special Issue Intrusion Detection and Resiliency in Cyber-Physical Systems and Networks)
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29 pages, 3905 KiB  
Article
Federated Deep Learning for Scalable and Privacy-Preserving Distributed Denial-of-Service Attack Detection in Internet of Things Networks
by Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei and Riad Alharbey
Future Internet 2025, 17(2), 88; https://doi.org/10.3390/fi17020088 - 13 Feb 2025
Viewed by 762
Abstract
Industry-wide IoT networks have altered operations and increased vulnerabilities, notably DDoS attacks. IoT systems are decentralised. Therefore, these attacks flood networks with malicious traffic, creating interruptions, financial losses, and availability issues. We need scalable, privacy-preserving, and resource-efficient IoT intrusion detection algorithms to solve [...] Read more.
Industry-wide IoT networks have altered operations and increased vulnerabilities, notably DDoS attacks. IoT systems are decentralised. Therefore, these attacks flood networks with malicious traffic, creating interruptions, financial losses, and availability issues. We need scalable, privacy-preserving, and resource-efficient IoT intrusion detection algorithms to solve this essential problem. This paper presents a Federated-Learning (FL) framework using ResVGG-SwinNet, a hybrid deep-learning architecture, for multi-label DDoS attack detection. ResNet improves feature extraction, VGGNet optimises feature refining, and Swin-Transformer captures contextual dependencies, making the model sensitive to complicated attack patterns across varied network circumstances. Using the FL framework, decentralised training protects data privacy and scales and adapts across diverse IoT contexts. New preprocessing methods like Dynamic Proportional Class Adjustment (DPCA) and Dual Adaptive Selector (DAS) for feature optimisation improve system efficiency and accuracy. The model performed well on CIC-DDoS2019, UNSW-NB15, and IoT23 datasets, with 99.0% accuracy, 2.5% false alert rate, and 99.3% AUC. With a 93.0% optimisation efficiency score, the system balances computational needs with robust detection. With advanced deep-learning models, FL provides a scalable, safe, and effective DDoS detection solution that overcomes significant shortcomings in current systems. The framework protects IoT networks from growing cyber threats and provides a complete approach for current IoT-driven ecosystems. Full article
(This article belongs to the Special Issue Intrusion Detection and Resiliency in Cyber-Physical Systems and Networks)
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