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Mathematical and Computing Sciences for Artificial Intelligence
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Mathematical and Computing Sciences for Artificial Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1407

Special Issue Editor

Prof. Dr. Chong-zhi Gao

E-Mail Website
Guest Editor
School of Computer Science, Guangzhou University, Guangzhou, China
Interests: security and privacy in machine learning and artificial intelligence

Special Issue Information

Dear Colleagues,

​The field of artificial intelligence relies on a deep understanding of mathematics, statistics and computer science to create algorithms that can learn from data and make intelligent decisions. However, due to the lack of data and bias in data, as well as the complexity of real-world systems, there are still many challenges in this field, including mathematical foundations and modelling in artificial intelligence, better optimization algorithms, interpretability of artificial intelligence, and building AI algorithms to solve specific application problems. Since mathematics is the foundation of artificial intelligence, and the integration of mathematical and computing sciences plays a crucial role in advancing AI research, this Special Issue aims to explore the latest developments, methodologies, and applications that highlight the synergy between mathematics, computing, and AI. We welcome original research papers addressing various aspects of mathematical and computing sciences for artificial intelligence. Topics of interest include, but are not limited to:

  • Optimization algorithms and machine learning;
  • Probabilistic modeling and Bayesian inference in AI;
  • Reinforcement learning and control theory;
  • Adversarial attacks and defense in AI;
  • Game theory and AI decision;
  • Mathematical approaches to explainable AI;
  • Graph theory and network analysis for AI systems;
  • Natural language processing and computational linguistics;
  • Mathematical modeling for computer vision;
  • The applications of AI in the field of medical sciences.

Prof. Dr. Chong-zhi Gao
Guest Editor

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. Mathematics 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 2600 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.

Published Papers (1 paper)

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Research

17 pages, 1081 KiB  
Article
Malicious Traffic Classification via Edge Intelligence in IIoT
by Maoli Wang, Bowen Zhang, Xiaodong Zang, Kang Wang and Xu Ma
Mathematics 2023, 11(18), 3951; https://doi.org/10.3390/math11183951 - 17 Sep 2023
Viewed by 943
Abstract
The proliferation of smart devices in the 5G era of industrial IoT (IIoT) produces significant traffic data, some of which is encrypted malicious traffic, creating a significant problem for malicious traffic detection. Malicious traffic classification is one of the most efficient techniques for [...] Read more.
The proliferation of smart devices in the 5G era of industrial IoT (IIoT) produces significant traffic data, some of which is encrypted malicious traffic, creating a significant problem for malicious traffic detection. Malicious traffic classification is one of the most efficient techniques for detecting malicious traffic. Although it is a labor-intensive and time-consuming process to gather large labeled datasets, the majority of prior studies on the classification of malicious traffic use supervised learning approaches and provide decent classification results when a substantial quantity of labeled data is available. This paper proposes a semi-supervised learning approach for classifying malicious IIoT traffic. The approach utilizes the encoder–decoder model framework to classify the traffic, even with a limited amount of labeled data available. We sample and normalize the data during the data-processing stage. In the semi-supervised model-building stage, we first pre-train a model on a large unlabeled dataset. Subsequently, we transfer the learned weights to a new model, which is then retrained using a small labeled dataset. We also offer an edge intelligence model that considers aspects such as computation latency, transmission latency, and privacy protection to improve the model’s performance. To achieve the lowest total latency and to reduce the risk of privacy leakage, we first create latency and privacy-protection models for each local, edge, and cloud. Then, we optimize the total latency and overall privacy level. In the study of IIoT malicious traffic classification, experimental results demonstrate that our method reduces the model training and classification time with 97.55% accuracy; moreover, our approach boosts the privacy-protection factor. Full article
(This article belongs to the Special Issue Mathematical and Computing Sciences for Artificial Intelligence)
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