Advanced Technologies in Intrusion Detection System
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 August 2026 | Viewed by 883
Editors
Interests: computing; network and cybersecurity; intrusion detection
Special Issue Information
Dear Colleagues,
In the contemporary digital landscape, Intrusion Detection Systems (IDSs) constitute a critical component of modern cybersecurity architectures, whose core function lies in monitoring network traffic and system activities to identify potential breaches and malicious behaviors.
Though there has been significant advancement in this area, traditional IDS approaches are often insufficient to cope with sophisticated attack techniques. The primary reasons that have been identified in the literature are (a) high false positive rates; (b) poor detection efficacy for unknown or dynamically changing threats; (c) difficulties in maintaining scalability and real-time performance; (d) inability to identify traffic patterns in encrypted communications; (e) limited interpretability of ML/AI-driven IDS; (f) insufficient robustness against evasion techniques.
The objective of this Special Issue is to explore advanced technologies for advancing IDS, with a focus on improving detection accuracy, reducing false-positive rates, and expanding the capacity to identify a broad spectrum of cyber threats. Emerging advanced technologies such as artificial intelligence (AI), deep learning, distributed architectures, blockchain technology are reshaping the conceptualization and practical implementation of information security. These approaches offer not only enhanced accuracy and robustness but also scalability, adaptability, and resilience across diverse, resource-limited environments.
We invite high-quality, original research papers and review articles addressing topics including, but not limited to, the following:
- ML and AI-enhanced intrusion detection;
- Distributed and collaborative intrusion detection;
- Privacy-preserving intrusion detection;
- Deep learning architectures for anomaly detection;
- Creation and use of benchmark datasets for intrusion detection;
- Real-time and scalable intrusion detection;
- Blockchain for intrusion detection;
- Intrusion detection in industrial control systems, IoT and cloud environments;
- Intrusion detection in resource-constrained environments.
Prof. Dr. Fazhi Qi
Dr. Jiarong Wang
Guest Editors
Manuscript Submission Information
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Keywords
- intrusion detection systems
- cybersecurity
- data security
- machine learning
- artificial intelligence
- anomaly detection
- industrial control systems
- IoT
- cloud environments
- blockchain
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