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	16 Days	CHF 2400
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400
Informatics informatics	2.8	8.4	2014	32.1 Days	CHF 1800
Journal of Cybersecurity and Privacy jcp	-	9.1	2021	21.5 Days	CHF 1200
Future Internet futureinternet	3.6	8.3	2009	16.1 Days	CHF 1800
Mathematics mathematics	2.2	4.6	2013	17.3 Days	CHF 2600
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700

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Published Papers (8 papers)

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

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29 pages, 770 KB  

Open AccessArticle

Revisiting SMS Spam Detection: The Impact of Feature Representation on Classical Machine Learning Models

by Meryem Soysaldı Şahin, Durmuş Özkan Şahin and Areej Fateh Salah

Electronics 2026, 15(4), 894; https://doi.org/10.3390/electronics15040894 - 21 Feb 2026

Viewed by 222

Abstract

The proliferation of unsolicited short messages (SMS spam) poses persistent challenges to mobile communication security and user privacy. This study presents a systematic benchmarking and analytical investigation of classical machine learning approaches for SMS spam detection, focusing on the impact of text feature [...] Read more.

The proliferation of unsolicited short messages (SMS spam) poses persistent challenges to mobile communication security and user privacy. This study presents a systematic benchmarking and analytical investigation of classical machine learning approaches for SMS spam detection, focusing on the impact of text feature representation under imbalanced short-text conditions.In practical SMS filtering systems, minimizing false positives (i.e., incorrectly blocking legitimate messages) is a critical operational constraint. Therefore, beyond overall accuracy, precision and specificity are emphasized to ensure reliable preservation of legitimate communication. Using the SMSSpamCollection dataset (5574 messages: 747 spam and 4827 ham), seven feature representation techniques were evaluated in combination with six widely adopted classifiers, resulting in 42 configurations assessed under 10-fold cross-validation. The results demonstrate that feature representation plays a more critical role than classifier complexity. Character-level 3-grams combined with Logistic Regression achieved the best overall performance, reaching 98.55% accuracy, with 98.55% precision and 90.50% recall for the spam class (F1-score = 94.32%), and 0.9893 AUC. Linear SVM produced comparable results, highlighting the effectiveness of linear models when paired with expressive representations. Beyond reporting performance metrics, this study analyzes feature–classifier interaction patterns and clarifies practical trade-offs between precision, recall, and computational efficiency. The findings provide reproducible baselines and structured guidance for designing efficient SMS spam filtering systems. Full article

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

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28 pages, 4978 KB  

Open AccessArticle

Oilseed Flax Yield Prediction in Arid Gansu, China Using a CNN–Informer Model and Multi-Source Spatio-Temporal Data

by Xingyu Li, Yue Li, Bin Yan, Yuhong Gao, Shunchang Su, Hui Zhou, Lianghe Kang, Huan Liu and Yongbiao Li

Remote Sens. 2026, 18(1), 181; https://doi.org/10.3390/rs18010181 - 5 Jan 2026

Cited by 1 | Viewed by 481

Abstract

Oilseed flax (Linum usitatissimum, L.) is an important specialty oilseed crop cultivated in arid and semi-arid regions, where timely, accurate yield prediction is crucial for regional oilseed security and agricultural decision-making. To address the lack of robust county-level yield prediction models [...] Read more.

Oilseed flax (Linum usitatissimum, L.) is an important specialty oilseed crop cultivated in arid and semi-arid regions, where timely, accurate yield prediction is crucial for regional oilseed security and agricultural decision-making. To address the lack of robust county-level yield prediction models for oilseed flax, this study proposes a CNN–Informer hybrid framework that integrates convolutional neural networks (CNNs) with the Informer architecture to model multi-source spatio-temporal data. Unlike conventional Transformer-based approaches, the proposed framework combines CNN-based local temporal feature extraction with the ProbSparse attention mechanism of Informer, enabling the efficient modeling of long-range temporal dependencies across multiple years while reducing the computational burden of attention-based time-series modeling. The model incorporates multi-source inputs, including remote sensing indices (NDVI, EVI, SAVI, KNDVI), TerraClimate meteorological variables, soil properties, and historical yield records. Comprehensive experiments conducted at the county level in Gansu Province, China, demonstrate that the CNN–Informer model consistently outperforms representative machine learning and deep learning baselines (Transformer, Informer, LSTM, and XGBoost), achieving an average performance of R² = 0.82, RMSE = 0.31 t/ha, MAE = 0.21 t/ha, and MAPE = 10.33%. Results from feature ablation and historical yield window analyses reveal that a three-year historical yield window yields optimal performance, with remote sensing features contributing most strongly to predictive accuracy, while meteorological and soil variables enhance spatial adaptability under heterogeneous environmental conditions. Model robustness was further verified through fivefold county-based spatial cross-validation, indicating stable performance and strong generalization capability in unseen regions. Overall, the proposed CNN–Informer framework provides a reliable and interpretable solution for county-level oilseed flax yield prediction and offers practical insights for precision management of specialty crops in arid and semi-arid regions. Full article

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

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35 pages, 6609 KB  

Open AccessArticle

Fairness-Aware Face Presentation Attack Detection Using Local Binary Patterns: Bridging Skin Tone Bias in Biometric Systems

by Jema David Ndibwile, Ntung Ngela Landon and Floride Tuyisenge

J. Cybersecur. Priv. 2026, 6(1), 12; https://doi.org/10.3390/jcp6010012 - 4 Jan 2026

Viewed by 463

Abstract

While face recognition systems are increasingly deployed in critical domains, they remain vulnerable to presentation attacks and exhibit significant demographic bias, particularly affecting African populations. This paper presents a fairness-aware Presentation Attack Detection (PAD) system using Local Binary Patterns (LBPs) with novel ethnicity-aware [...] Read more.

While face recognition systems are increasingly deployed in critical domains, they remain vulnerable to presentation attacks and exhibit significant demographic bias, particularly affecting African populations. This paper presents a fairness-aware Presentation Attack Detection (PAD) system using Local Binary Patterns (LBPs) with novel ethnicity-aware processing techniques specifically designed for African contexts. Our approach introduces three key technical innovations: (1) adaptive preprocessing with differentiated Contrast-Limited Adaptive Histogram Equalization (CLAHE) parameters and gamma correction optimized for different skin tones, (2) group-specific decision threshold optimization using Equal Error Rate (EER) minimization for each ethnic group, and (3) three novel statistical methods for PAD fairness evaluation such as Coefficient of Variation analysis, McNemar’s significance testing, and bootstrap confidence intervals representing the first application of these techniques in Presentation Attack Detection. Comprehensive evaluation on the Chinese Academy of Sciences Institute of Automation-SURF Cross-ethnicity Face Anti-spoofing dataset (CASIA-SURF CeFA) dataset demonstrates significant bias reduction achievements: a 75.6% reduction in the accuracy gap between African and East Asian subjects (from 3.07% to 0.75%), elimination of statistically significant bias across all ethnic group comparisons, and strong overall performance, with 95.12% accuracy and 98.55% AUC. Our work establishes a comprehensive methodology for measuring and mitigating demographic bias in PAD systems while maintaining security effectiveness, contributing both technical innovations and statistical frameworks for inclusive biometric security research. Full article

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

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29 pages, 1277 KB  

Open AccessReview

A Survey on Acoustic Side-Channel Attacks: An Artificial Intelligence Perspective

by Benjamin Quattrone and Youakim Badr

J. Cybersecur. Priv. 2026, 6(1), 6; https://doi.org/10.3390/jcp6010006 - 29 Dec 2025

Viewed by 1182

Abstract

Acoustic Side-Channel Attacks (ASCAs) exploit the sound produced by keyboards and other devices to infer sensitive information without breaching software or network defenses. Recent advances in deep learning, large language models, and signal processing have greatly expanded the feasibility and accuracy of these [...] Read more.

Acoustic Side-Channel Attacks (ASCAs) exploit the sound produced by keyboards and other devices to infer sensitive information without breaching software or network defenses. Recent advances in deep learning, large language models, and signal processing have greatly expanded the feasibility and accuracy of these attacks. To clarify the evolving threat landscape, this survey systematically reviews ASCA research published between January 2020 and February 2025. We categorize modern ASCA methods into three levels of text reconstruction—individual keystrokes, short text (words/phrases), and long-text regeneration— and analyze the signal processing, machine learning, and language-model decoding techniques that enable them. We also evaluate how environmental factors such as microphone placement, ambient noise, and keyboard design influence attack performance, and we examine the challenges of generalizing laboratory-trained models to real-world settings. This survey makes three primary contributions: (1) it provides the first structured taxonomy of ASCAs based on text generation granularity and decoding methodology; (2) it synthesizes cross-study evidence on environmental and hardware factors that fundamentally shape ASCA performance; and (3) it consolidates emerging countermeasures, including Generative Adversarial Network-based noise masking, cryptographic defenses, and environmental mitigation, while identifying open research gaps and future threats posed by voice-enabled IoT and prospective quantum side-channels. Together, these insights underscore the need for interdisciplinary, multi-layered defenses against rapidly advancing ASCA techniques. Full article

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

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15 pages, 5351 KB  

Open AccessArticle

A Steganalysis Method Based on Relationship Mining

by Ruiyao Yang, Yu Yang, Linna Zhou and Xiangli Meng

Electronics 2025, 14(21), 4347; https://doi.org/10.3390/electronics14214347 - 6 Nov 2025

Cited by 1 | Viewed by 696

Abstract

Steganalysis is a critical research direction in the field of information security. Traditional approaches typically employ convolution operations for feature extraction, followed by classification on noise residuals. However, since steganographic signals are inherently weak, convolution alone cannot fully capture their characteristics. To address [...] Read more.

Steganalysis is a critical research direction in the field of information security. Traditional approaches typically employ convolution operations for feature extraction, followed by classification on noise residuals. However, since steganographic signals are inherently weak, convolution alone cannot fully capture their characteristics. To address this limitation, we propose a steganalysis method based on relationship mining, termed RMNet, which leverages positional relationships of steganographic signals for detection. Specifically, features are modeled as graph nodes, where both locally focused and globally adaptive dynamic adjacency matrices guide the propagation paths of these nodes. Meanwhile, the results are further constrained in the feature space, encouraging intra-class compactness and inter-class separability, thereby increasing inter-class separability of positional features and yielding a more discriminative decision boundary. Additionally, to counter signal attenuation during network propagation, we introduce a multi-scale perception module with cross-attention fusion. Experimental results demonstrate that RMNet achieves performance comparable to state-of-the-art models on the BOSSbase and BOWS2 datasets, while offering superior generalization capability. Full article

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

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26 pages, 3423 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

Viewed by 2390

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 1196

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

Cited by 3 | Viewed by 5680

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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