Artificial Intelligence Security and Machine Learning

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

Deadline for manuscript submissions: 1 May 2025 | Viewed by 1031

Special Issue Editor


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Guest Editor
School of Physics and Electronics, Central South University, Changsha 410083, China
Interests: artificial intelligence; sensor electronics; chaos encryption algorithms

Special Issue Information

This Special Issue of Mathematics seeks to explore the rapidly evolving field of artificial intelligence (AI), with a focus on security aspects and machine learning techniques. AI and machine learning are integral for modern computational mathematics and intersect significantly with various domains, such as dynamic systems, engineering mathematics, and mathematical physics.

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) are transforming every aspect of society, from healthcare and finance to engineering and the sciences. The integration of AI with traditional mathematical and engineering disciplines has paved the way for advanced analytical solutions and optimized decision-making processes. However, with great power comes great responsibility. The rapid deployment of AI systems necessitates robust measures to secure these systems against increasingly sophisticated threats. This Special Issue seeks to highlight research that not only advances the mathematical foundations of AI but also enhances its security and reliability.

The aim of this Special Issue is to curate a collection of high-quality research articles that address critical issues at the intersection of AI security and machine learning. We aim to showcase studies that have developed new mathematical models, computational algorithms, and practical applications that ensure the security and efficacy of AI systems. This Special Issue aligns with the journal’s focus on computational and applied mathematics, dynamic systems, and mathematical physics, contributing towards a deeper understanding of AI's capabilities and limitations.

Contributions are welcome in various formats, including original research articles, comprehensive reviews, and case studies. Suggested themes include, but are not limited to, the following:

  • Theoretical foundations of AI and machine learning;
  • Security challenges in machine learning algorithms;
  • Cryptography and secure communication protocols in AI;
  • AI in network security and intrusion detection;
  • Robust machine learning algorithms against adversarial attacks;
  • Application of fuzzy systems to secure AI frameworks;
  • Decision-making and risk analysis in AI-driven systems;
  • Bio-inspired security algorithms for machine learning;
  • Integration of AI with sensor and electronic systems for enhanced security;
  • Mathematical models for chaos and complexity in AI systems;
  • Case studies on AI and ML applications in security-sensitive sectors (e.g., finance, healthcare, and telecommunications).

I am looking forward to receiving your contributions.

Prof. Dr. Xuemei Xu
Guest Editor

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Keywords

  • artificial intelligence
  • machine learning
  • AI security
  • cryptography
  • adversarial machine learning
  • network security
  • fuzzy logic
  • risk analysis
  • bio-inspired algorithms
  • chaos theory

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Published Papers (1 paper)

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Research

13 pages, 335 KiB  
Article
Binary Encoding-Based Federated Learning for Traffic Sign Recognition in Autonomous Driving
by Yian Wen, Yun Zhou and Kai Gao
Mathematics 2024, 12(14), 2229; https://doi.org/10.3390/math12142229 - 17 Jul 2024
Viewed by 761
Abstract
Autonomous driving involves collaborative data sensing and traffic sign recognition. Emerging artificial intelligence technology has brought tremendous advances to vehicular networks. However, it is challenging to guarantee privacy and security when using traditional centralized machine learning methods for traffic sign recognition. It is [...] Read more.
Autonomous driving involves collaborative data sensing and traffic sign recognition. Emerging artificial intelligence technology has brought tremendous advances to vehicular networks. However, it is challenging to guarantee privacy and security when using traditional centralized machine learning methods for traffic sign recognition. It is urgent to introduce a distributed machine learning approach to protect private data of connected vehicles. In this paper, we propose a local differential privacy-based binary encoding federated learning approach. The binary encoding techniques and random perturbation methods are used in distributed learning scenarios to enhance the efficiency and security of data transmission. For the vehicle layer in this approach, the model is trained locally, and the model parameters are uploaded to the central server through encoding and perturbing. The central server designs the corresponding decoding, correction scheme, and regression statistical method for the received binary string. Then, the model parameters are aggregated and updated in the server and transmitted to the vehicle until the learning model is trained. The performance of the proposed approach is verified using the German Traffic Sign Recognition Benchmark data set. The simulation results show that the convergence of the approach is better with the increase in the learning cycle. Compared with baseline methods, such as the convolutional neural network, random forest, and backpropagation, the proposed approach achieves higher accuracy in the process of traffic sign recognition, with an increase of 6%. Full article
(This article belongs to the Special Issue Artificial Intelligence Security and Machine Learning)
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