Recent Advances in Artificial Intelligence for Security and Security for Artificial Intelligence

Topic Information

Dear Colleagues,

The rapid evolution of artificial intelligence (AI) has profoundly impacted various sectors, including healthcare, remote sensing, smart cities, and more. For example, AI technologies are being increasingly integrated to analyze and process remote images derived from multi-spectral, hyperspectral, and LiDAR systems. However, as the effectiveness of AI systems heavily depends on the availability and utilization of large datasets, which often contain sensitive personal information, the risks of data breaches, unauthorized access, and the misuse of personal data have become more pressing. In addition, AI models themselves are vulnerable to evolving cyber threats, such as adversarial attacks, model inversion, and data poisoning, which further complicate the landscape of data security and privacy protection. Regulatory frameworks like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) emphasize the need for robust data protection strategies, urging AI systems to adopt advanced security and privacy-preserving technologies. This Topic, "Recent Advances in Artificial Intelligence for Security and Security for Artificial Intelligence", seeks to explore innovative solutions at the intersection of AI and security. We invite submissions that examine advanced approaches, methodologies, and applications of AI to improve security, as well as techniques for securing AI systems themselves.

The topics of interest include but are not limited to the following:

• Secure and efficient encryption algorithms powered by AI; 
• AI-enhanced remote sensing image processing and analysis; 
• Adversarial attacks and defenses in remote sensing image classification; 
• Privacy-preserving AI techniques for remote sensing data (e.g., federated learning and differential privacy); 
• AI-based solutions for securing network protocols; 
• Intelligent authentication systems using machine learning; 
• AI-based vulnerability detection in communication networks; 
• Secure data aggregation and sharing protocols in AI; 
• Privacy risks and mitigation strategies in AI-powered applications; 
• Protecting personal data in AI training and inference; 
• AI models’ resilience to adversarial perturbations; 
• AI for building secure cloud platforms and infrastructures.

Dr. Tao Zhang
Dr. Xiangyun Tang
Dr. Jiacheng Wang
Dr. Chuan Zhang
Prof. Dr. Jiqiang Liu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Informatics informatics	2.8	8.4	2014	34.9 Days	CHF 1800	Submit
Journal of Cybersecurity and Privacy jcp	-	9.1	2021	24.4 Days	CHF 1200	Submit
Future Internet futureinternet	3.6	8.3	2009	17 Days	CHF 1600	Submit
Mathematics mathematics	2.2	4.6	2013	18.4 Days	CHF 2600	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.9 Days	CHF 2700	Submit

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Published Papers (1 paper)

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All Applied Sciences Electronics Informatics JCP Future Internet Mathematics Sensors Remote Sensing

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24 pages, 2288 KiB  

Open AccessSystematic Review

A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning

by Jallal-Eddine Moussaoui, Mehdi Kmiti, Khalid El Gholami and Yassine Maleh

J. Cybersecur. Priv. 2025, 5(3), 41; https://doi.org/10.3390/jcp5030041 - 2 Jul 2025

Viewed by 504

Abstract

Cyber threats are growing in scale and complexity, outpacing the capabilities of traditional security systems. Machine learning (ML) models offer enhanced detection accuracy but often rely on centralized data, raising privacy concerns. Federated learning (FL), by contrast, enables decentralized model training but suffers [...] Read more.

Cyber threats are growing in scale and complexity, outpacing the capabilities of traditional security systems. Machine learning (ML) models offer enhanced detection accuracy but often rely on centralized data, raising privacy concerns. Federated learning (FL), by contrast, enables decentralized model training but suffers from scalability and latency issues. Hybrid AI models, which integrate ML and FL techniques, have emerged as a promising solution to balance performance, privacy, and scalability in cybersecurity. This systematic review investigates the current landscape of hybrid AI models, evaluating their strengths and limitations across five key dimensions: accuracy, privacy preservation, scalability, explainability, and robustness. Findings indicate that hybrid models consistently outperform standalone approaches, yet challenges remain in real-time deployment and interpretability. Future research should focus on improving explainability, optimizing communication protocols, and integrating secure technologies such as blockchain to enhance real-world applicability. Full article

(This article belongs to the Topic Recent Advances in Artificial Intelligence for Security and Security for Artificial Intelligence)

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