Anomaly and Intrusion Detection in Networks
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Cybersecurity".
Deadline for manuscript submissions: 10 September 2026 | Viewed by 58
Special Issue Editors
Interests: AI-based solutions in cybersecurity; LoRaWAN networks; anomaly detection; intrusion detection; 5G networks
Interests: 5G networks; attack graphs; honeypots; anomaly detection; intrusion detection
Special Issue Information
Dear Colleagues,
The expansion of networks from legacy IT infrastructures to the Internet of Things (IoT) and, increasingly, the Industrial Internet of Things (IIoT), has created highly interconnected environments where devices, sensors, and controllers operate alongside cloud platforms, data analytics, and artificial intelligence, resulting in heterogeneous and layered landscapes. This diversity significantly complicates anomaly and intrusion detection, as each domain exhibits distinct characteristics, communication patterns, and security requirements. Addressing these challenges is essential to safeguarding critical sectors such as smart manufacturing, healthcare, energy, and transportation.
This Special Issue seeks high-quality contributions presenting novel approaches, algorithms, and case studies in anomaly and intrusion detection. We welcome research that leverages machine learning, deep learning, hybrid methods, and other innovative techniques, as well as studies addressing the unique challenges posed by heterogeneous IoT, IIoT, and legacy IT networks. Both theoretical and practical works are encouraged, particularly those demonstrating real-world applicability and effectiveness in dynamic environments.
We invite original research papers, reviews, and case studies. Topics of interest include, but are not limited to, the following:
- AI and Machine Learning for Intrusion Detection: Supervised, unsupervised, and reinforcement learning techniques for detecting anomalies in heterogeneous IoT and IIoT networks;
- IoT and IIoT Security: Intrusion detection tailored to IoT ecosystems, industrial IoT deployments, and cyber–physical systems;
- Hybrid and Knowledge-Driven Methods: Combining statistical, rule-based, and AI-powered approaches for more accurate anomaly detection;
- Trustworthy and Explainable AI (XAI): Ensuring intrusion detection systems are interpretable, transparent, and secure;
- Honeypots and Digital Twins: Deception- and simulation-based techniques to early detect and analyze advanced threats;
- Innovative Approaches: Emerging and unconventional methods that advance anomaly and intrusion detection beyond existing paradigms;
- Critical Infrastructure Applications: Case studies on anomaly and intrusion detection in smart grids, healthcare, transportation, and other mission-critical domains.
Dr. Eszter Kail
Dr. Anna Bánáti
Prof. Dr. Miklós Kozlovszky
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
- anomaly detection
- intrusion detection
- machine learning
- legacy networks
- XAI
- IoT
- IIoT
- honeypots
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