Special Issue "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: 30 January 2024 | Viewed by 163

Special Issue Editors

Dr. Chinyang Henry Tseng
E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 237303, Taiwan
Interests: network intrusion prevention; deep learning quality; blockchain smart contracts; IoT routing

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

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2300 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
  • machine learning
  • deep learning
  • adversarial attack
  • federal learning
  • generative adversarial network
  • contrastive learning
  • blockchain

Published Papers

This special issue is now open for submission.
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