Recent Advances in Intrusion Detection Systems Using Machine Learning
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".
Deadline for manuscript submissions: 15 October 2024 | Viewed by 805
Special Issue Editors
Interests: cybersecurity; machine learning; social cybersecurity; social computing; natural language processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Cyber-attacks are not only increasing but are also evolving rapidly to become highly sophisticated, thereby leading to increasing challenges in precisely detecting threats and intrusions, making the development of advanced intrusion detection systems (IDSs) more crucial than ever. Accordingly, numerous IDSs are designed and used to protect valuable assets in financial services, healthcare, manufacturing, data centers, critical infrastructures, etc. Many research ideas targeting IDSs using artificial intelligence (AI) and machine learning (ML) techniques have been proposed. Particularly, in recent years, IDSs leveraging deep learning have demonstrated remarkable capabilities in learning representations of complex data, ranging from high-dimensional to temporal and spatial data, pushing the frontiers of these systems. While successful in many domains, IDSs still suffer from many issues. Some of them are as follows: (1) False-positive rates are high. (2) Security experts need to conduct elaborate feature extraction. (3) Insufficient data can be used to train effective models in some sensitive applications. (4) The detection performance decreases over time due to concept drift. This Special Issue aims to address the challenges and present innovative techniques in the field. Both original research papers and reviews are welcome. Research may focus on (but is not limited to) the following topics:
- Deep learning for IDSs;
- Federated learning for intrusion detection ;
- Anomaly detection for IDSs;
- Concept drift in IDSs;
- Adaptive learning for IDSs;
- Network-based IDSs using AI/ML;
- Host-based IDSs using AI/ML;
- IDSs using AI/ML for cyber-physical systems;
- IDSs using AI/ML for IoT/IIoT;
- Privacy and trust in IDSs;
- Privacy preservation techniques in IDSs;
- Large-scale distributed intrusion detection.
Dr. Sicong Shao
Dr. Jielun Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- intrusion detection
- system and software security
- network security
- machine learning
- artificial intelligence
- deep learning
