Risk and Protection for Machine Learning-Based Network Intrusion
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 June 2024 | Viewed by 1288
Special Issue Editors
Interests: network intrusion prevention; deep learning quality; blockchain smart contracts; IoT routing
Interests: blockchain network security; Internet of Things application engineering and security; applied cryptography
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As the scale of network intrusion grows, machine learning models become a popular approach for intrusion detection based on their significant computation capability, especially deep learning models. Although machine learning-based intrusion detection models can detect a large range of network intrusions, it is difficult to explain the detection results because of the model’s computation complexity. Adversarial attacks can pollute the detection training model to mislead the detection results, and they are difficult to be observed. Thus, non-explainable results and adversarial attacks lead to new risks of machine learning-based intrusion detection models.
This Special Issue invites research or review papers on new advanced protections for machine learning-based intrusion detection models that explore with their new risks, such as adversarial attacks. For federal learning, if malicious clients provide the training results polluted by the adversarial attacks, the server training model is also polluted. Generative adversarial networks can generate both beneficial training samples and adversarial samples. Contrastive learning models have illustrated their self-learning capability for images, and they can be good candidates to protect the intrusion detections via self-learning. Blockchain is also a popular approach to protect against intrusion detections. These new emerging techniques can establish hybrid protection solutions for intrusion detection to prevent their new risks.
Dr. Chinyang Henry Tseng
Prof. Dr. Hsing-Chung Chen
Guest Editors
Manuscript Submission Information
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Keywords
- intrusion detection
- machine learning
- deep learning
- adversarial attack
- federal learning
- generative adversarial network
- contrastive learning
- blockchain
