Generative Artificial Intelligence: Systems, Technologies and Applications

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 822

Special Issue Editors


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Guest Editor
Department of Education Sciences in Early Childhood, Democritus University of Thrace, 68100 Alexandroupolis, Greece
Interests: artificial intelligence; knowledge representation; artificial intelligence in education; e-learning; generative artificial intelligence

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Guest Editor
Prof. Emeritus. Dr., Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece
Interests: artificial intelligence; knowledge representation; intelligent systems; intelligent e-learning; sentiment analysis
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Special Issue Information

Dear Colleagues,

Generative artificial intelligence has become popular during recent years, providing enhanced experiences to users and advancing many fields. Generative AI systems may produce various forms of data such as text, images, audio, and videos, among others. The availability of Web-based systems encompassing generative artificial intelligence provides enhanced experiences to Internet users, enabling them to utilize advanced AI methods. Due to the increasing popularity of generative AI systems, there are many directions for relevant research work in the context of the Internet.

This Special Issue aims to address recent advances in generative AI and how these affect the evolution of the Internet. It welcomes original, unpublished research and review papers concerning all relevant aspects.

Topics of interest include, but are not limited to, the following:

  • New application fields of generative AI;
  • New viewpoints in existing application fields of generative AI;
  • Improving the effectiveness of methods used in generative AI;
  • New AI methods in the context of generative AI systems;
  • Time efficiency and generative AI systems;
  • Improving user interaction with generative AI systems;
  • Security and generative AI systems;
  • Combination of generative AI systems with other AI systems;
  • Explainable AI methods and generative AI;
  • Neural networks and generative AI;
  • Data mining and generative AI;
  • Natural language processing and generative AI.

Dr. Jim Prentzas
Prof. Dr. Ioannis Hatzilygeroudis
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 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

  • generative artificial intelligence
  • machine learning
  • data mining
  • neural networks
  • natural language processing
  • explainable artificial intelligence
  • combinations of AI methods

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Published Papers (1 paper)

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Research

42 pages, 1300 KiB  
Article
A Hybrid Human-AI Model for Enhanced Automated Vulnerability Scoring in Modern Vehicle Sensor Systems
by Mohamed Sayed Farghaly, Heba Kamal Aslan and Islam Tharwat Abdel Halim
Future Internet 2025, 17(8), 339; https://doi.org/10.3390/fi17080339 - 28 Jul 2025
Viewed by 422
Abstract
Modern vehicles are rapidly transforming into interconnected cyber–physical systems that rely on advanced sensor technologies and pervasive connectivity to support autonomous functionality. Yet, despite this evolution, standardized methods for quantifying cybersecurity vulnerabilities across critical automotive components remain scarce. This paper introduces a novel [...] Read more.
Modern vehicles are rapidly transforming into interconnected cyber–physical systems that rely on advanced sensor technologies and pervasive connectivity to support autonomous functionality. Yet, despite this evolution, standardized methods for quantifying cybersecurity vulnerabilities across critical automotive components remain scarce. This paper introduces a novel hybrid model that integrates expert-driven insights with generative AI tools to adapt and extend the Common Vulnerability Scoring System (CVSS) specifically for autonomous vehicle sensor systems. Following a three-phase methodology, the study conducted a systematic review of 16 peer-reviewed sources (2018–2024), applied CVSS version 4.0 scoring to 15 representative attack types, and evaluated four free source generative AI models—ChatGPT, DeepSeek, Gemini, and Copilot—on a dataset of 117 annotated automotive-related vulnerabilities. Expert validation from 10 domain professionals reveals that Light Detection and Ranging (LiDAR) sensors are the most vulnerable (9 distinct attack types), followed by Radio Detection And Ranging (radar) (8) and ultrasonic (6). Network-based attacks dominate (104 of 117 cases), with 92.3% of the dataset exhibiting low attack complexity and 82.9% requiring no user interaction. The most severe attack vectors, as scored by experts using CVSS, include eavesdropping (7.19), Sybil attacks (6.76), and replay attacks (6.35). Evaluation of large language models (LLMs) showed that DeepSeek achieved an F1 score of 99.07% on network-based attacks, while all models struggled with minority classes such as high complexity (e.g., ChatGPT F1 = 0%, Gemini F1 = 15.38%). The findings highlight the potential of integrating expert insight with AI efficiency to deliver more scalable and accurate vulnerability assessments for modern vehicular systems.This study offers actionable insights for vehicle manufacturers and cybersecurity practitioners, aiming to inform strategic efforts to fortify sensor integrity, optimize network resilience, and ultimately enhance the cybersecurity posture of next-generation autonomous vehicles. Full article
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