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 (3 papers)

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

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26 pages, 3463 KB  

Open AccessArticle

Federated Learning Spam Detection Based on FedProx and Multi-Level Multi-Feature Fusion

by Yunpeng Xiong, Junkuo Cao and Guolian Chen

Informatics 2025, 12(3), 93; https://doi.org/10.3390/informatics12030093 - 12 Sep 2025

Abstract

Traditional spam detection methodologies often neglect user privacy preservation, potentially incurring data leakage risks. Furthermore, current federated learning models for spam detection face several critical challenges: (1) data heterogeneity and instability during server-side parameter aggregation, (2) training instability in single neural network architectures [...] Read more.

Traditional spam detection methodologies often neglect user privacy preservation, potentially incurring data leakage risks. Furthermore, current federated learning models for spam detection face several critical challenges: (1) data heterogeneity and instability during server-side parameter aggregation, (2) training instability in single neural network architectures leading to mode collapse, and (3) constrained expressive capability in multi-module frameworks due to excessive complexity. These issues represent fundamental research pain points in federated learning-based spam detection systems. To address this technical challenge, this study innovatively integrates federated learning frameworks with multi-feature fusion techniques to propose a novel spam detection model, FPW-BC. The FPW-BC model addresses data distribution imbalance through the FedProx aggregation algorithm and enhances stability during server-side parameter aggregation via a horse-racing selection strategy. The model effectively mitigates limitations inherent in both single and multi-module architectures through hierarchical multi-feature fusion. To validate FPW-BC’s performance, comprehensive experiments were conducted on six benchmark datasets with distinct distribution characteristics: CEAS, Enron, Ling, Phishing_email, Spam_email, and Fake_phishing, with comparative analysis against multiple baseline methods. Experimental results demonstrate that FPW-BC achieves exceptional generalization capability for various spam patterns while maintaining user privacy preservation. The model attained 99.40% accuracy on CEAS and 99.78% on Fake_phishing, representing significant dual improvements in both privacy protection and detection efficiency. Full article

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

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19 pages, 1716 KB  

Open AccessArticle

Image-Based Adaptive Visual Control of Quadrotor UAV with Dynamics Uncertainties

by Jianlan Guo, Bingsen Huang, Yuqiang Chen, Guangzai Ye and Guanyu Lai

Electronics 2025, 14(15), 3114; https://doi.org/10.3390/electronics14153114 - 5 Aug 2025

Viewed by 426

Abstract

In this paper, an image-based visual control scheme is proposed for a quadrotor aerial vehicle with unknown mass and moment of inertia. In order to reduce the impacts of underactuation in quadrotor dynamics, a virtual image plane is introduced and appropriate image moment [...] Read more.

In this paper, an image-based visual control scheme is proposed for a quadrotor aerial vehicle with unknown mass and moment of inertia. In order to reduce the impacts of underactuation in quadrotor dynamics, a virtual image plane is introduced and appropriate image moment features are defined to decouple the image features from the movement of the vehicle. Subsequently, based on the quadrotor dynamics, a backstepping method is used to construct the torque controller, ensuring that the control system has superior dynamic performance. Furthermore, an adaptive control scheme is then designed to enable online estimation of dynamic parameters. Finally, stability is formally verified through constructive Lyapunov methods, and performance test results validate the efficacy and robustness of the proposed control scheme. It can be verified through performance tests that the quadrotor successfully positions itself at the desired position under uncertain dynamic parameters, and the attitude angles converge to the expected values. Full article

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

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24 pages, 2288 KB  

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 2229

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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