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

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	16 Days	CHF 2400	Submit
Electronics electronics	2.6	6.1	2012	16.4 Days	CHF 2400	Submit
Future Internet futureinternet	3.6	8.3	2009	16.1 Days	CHF 1800	Submit
Informatics informatics	2.8	8.4	2014	32.1 Days	CHF 1800	Submit
Journal of Cybersecurity and Privacy jcp	-	9.1	2021	21.5 Days	CHF 1200	Submit
Mathematics mathematics	2.2	4.6	2013	17.3 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.3 Days	CHF 2700	Submit
Sensors sensors	3.5	8.2	2001	17.8 Days	CHF 2600	Submit

15 pages, 1281 KB

Open AccessArticle

An Empirical Study of Federated BERT for Decentralized Twitter Sentiment Analysis

by Oumaima Louzar, Abdelaziz Elbaghdadi, Ahmed El Oualkadi, Ouafae Baida and Abdelouahid Lyhyaoui

Informatics 2026, 13(5), 73; https://doi.org/10.3390/informatics13050073 - 18 May 2026

Viewed by 187

Twitter/x has become a key platform for analyzing public opinion on a large scale; however, traditional centralized approaches raise significant concerns regarding privacy and data governance. To address these challenges, this paper presents an empirical study of a federated learning approach based on [...] Read more.

Twitter/x has become a key platform for analyzing public opinion on a large scale; however, traditional centralized approaches raise significant concerns regarding privacy and data governance. To address these challenges, this paper presents an empirical study of a federated learning approach based on a BERT model for decentralized sentiment analysis at the tweet level. This study focuses on evaluating the effectiveness of transformer-based models under realistic non-independent and identically distributed (non-IID) data distributions across distributed clients. The proposed approach enables collaborative model training without sharing raw tweet data, thereby preserving user privacy while leveraging knowledge from multiple sources. The model is evaluated over 100 communication rounds using the Sentiment140 dataset, distributed among four clients with heterogeneous data distributions. Experimental results demonstrate stable convergence and robust performance, with an accuracy of 95.00%, an F1 score of 95.00%, and a PR-AUC of 96.76%. It should be noted that the federated model performs within 1.2% of a centralized baseline, indicating minimal performance degradation despite data sharing constraints. Full article

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

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11 pages, 837 KB

Open AccessArticle

Enhancing the Efficiency of Blockchain Verification Through Resource-Weighted Node Selection

by Vedika Jorika and Nagaratna Medishetty

Informatics 2026, 13(5), 71; https://doi.org/10.3390/informatics13050071 - 8 May 2026

Viewed by 621

Abstract

Blockchain technology has emerged as a foundational paradigm for building decentralized, transparent, and secure systems, particularly in environments that operate without centralized authority. At the core of these systems are consensus mechanisms that ensure transaction validity and maintain trust among distributed participants. However, [...] Read more.

Blockchain technology has emerged as a foundational paradigm for building decentralized, transparent, and secure systems, particularly in environments that operate without centralized authority. At the core of these systems are consensus mechanisms that ensure transaction validity and maintain trust among distributed participants. However, the efficiency of a blockchain network is strongly influenced by how verifier (or validator) nodes are selected, particularly in sharded architectures where transaction processing is distributed across multiple shards. A critical challenge in blockchain design is selecting appropriate nodes for transaction verification in a manner that is efficient, fair, and resilient to adversarial behavior, while also minimizing communication overhead. Existing approaches often rely primarily on resource availability or on the ability to create blocks, particularly in sharded blockchain architectures. Building on these ideas, this paper proposes a Resource Weighted–Block Score selection algorithm, which integrates a node’s block score with its computational resource availability to guide verifier node selection. Simulation-based evaluation demonstrates that the proposed approach significantly reduces transaction verification latency and improves overall node utilization, thereby enhancing network performance and scalability in sharded blockchain 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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20 pages, 10156 KB

Open AccessArticle

Unveiling the Risk of Unsafe Image Generation in Stable Diffusion Through a Cross-Attention Mechanism

by Yong Zhuang, Yiheng Jing, Wenzhe Yi, Xiaoyang Xu and Juan Wang

Future Internet 2026, 18(5), 248; https://doi.org/10.3390/fi18050248 - 7 May 2026

Viewed by 587

Abstract

Text-to-image diffusion models such as Stable Diffusion enable high-quality image synthesis from text and are widely deployed due to their open-source nature and low computational requirements. However, this accessibility also makes them attractive targets for misuse, including the generation of not-safe-for-work and otherwise [...] Read more.

Text-to-image diffusion models such as Stable Diffusion enable high-quality image synthesis from text and are widely deployed due to their open-source nature and low computational requirements. However, this accessibility also makes them attractive targets for misuse, including the generation of not-safe-for-work and otherwise restricted content. In this paper, we propose EvilPrompt, a jailbreak attack that exploits the cross-attention mechanism in Stable Diffusion. The attack operates purely at inference time using plain-text prompts and does not require fine-tuning or modification of model parameters. By selectively reweighting cross-attention for specific tokens, EvilPrompt preserves the overall semantic structure of the prompt while steering the generation toward prohibited content. This enables fine-grained control over malicious semantics without introducing explicit unsafe keywords. We evaluate EvilPrompt on two real-world prompt sets, 4chan and Lexica, each containing 500 prompts. The attack achieves an Attack Success Rate (ASR) of 97.4% on 4chan and 98.0% on Lexica, yielding an overall average ASR of 97.7%. The attack maintains high semantic alignment between prompts and generated images. Bootstrapping Language-Image Pre-training (BLIP) similarity consistently exceeds 0.75 across all categories on both datasets. Human evaluation further confirms high visual realism, with mean scores above 7.0 on a 10-point scale, and strong semantic consistency, with mean scores above 7.3. These results demonstrate that cross-attention manipulation provides an effective and practical jailbreak pathway. We further analyze how commonly used text-level moderation affects the success of such attacks. Although the strongest defense configuration (HateCoT with GPT-4) reduces the ASR to 5.9%, it introduces 21.5 s of additional latency and a cost of $0.01182 per query. Lighter-weight alternatives such as Perspective API leave nearly half (45.0%) of attacks successful. These observations indicate that safeguards acting only on the input or final output are insufficient to capture attention-level manipulations. Overall, our results reveal a fundamental limitation of post-generation safety pipelines when confronted with inference-time control of cross-attention. Full article

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

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32 pages, 2513 KB

Open AccessArticle

CryptoKANs+: KAN-Inspired Self-Learning Polynomial Networks for Efficient Privacy-Preserving Machine Learning

by Omar Tahmi, Chamseddine Talhi and Hakima Ould-Slimane

J. Cybersecur. Priv. 2026, 6(3), 86; https://doi.org/10.3390/jcp6030086 - 6 May 2026

Viewed by 388

Abstract

Processing sensitive data in cloud-based neural networks raises privacy concerns, which Homomorphic Encryption addresses by enabling privacy-preserving machine learning. In our previous work, we introduced CryptoKANs, enabling efficient Kolmogorov–Arnold Network (KAN) inference over encrypted data via polynomial approximation of spline-based activation functions using [...] Read more.

Processing sensitive data in cloud-based neural networks raises privacy concerns, which Homomorphic Encryption addresses by enabling privacy-preserving machine learning. In our previous work, we introduced CryptoKANs, enabling efficient Kolmogorov–Arnold Network (KAN) inference over encrypted data via polynomial approximation of spline-based activation functions using KAN symbolization. To avoid performance degradation, CryptoKAN required min–max scaling of pre-activation inputs to a small interval—a requirement that could negatively affect training. In addition, a direct theoretical structural comparison with Multi-Layer Perceptron (MLP)-based solutions, such as CryptoNets, was missing. In this work, we address these limitations by presenting CryptoKAN+, a KAN-inspired network integrating self-learned polynomial activations through a Fully Connected Quadratic Transformation (FCQT) layer. By enforcing polynomial activations during training, this design replaces spline functions without post-training symbolization, eliminates the need for interval scaling, absorbs subsequent linear transformations, and reduces multiplicative depth for efficient encrypted inference. Experiments show that CryptoKAN+ achieves competitive accuracy while slightly improving encrypted inference efficiency—a natural consequence of compacting weights with self-learned activations. Overall, this work provides a formal analysis of the structural relationship between KANs and MLPs and demonstrates how enforcing polynomial activations during training enables efficient encrypted inference while preserving accuracy. Full article

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

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25 pages, 881 KB

Open AccessArticle

Beyond Pattern Matching: A Cognitive-Driven Framework for DGA Detection via Dual-Perspective Anomaly Perception

by Xiang Peng, Jun He, Lin Ni and Gang Yang

Electronics 2026, 15(9), 1934; https://doi.org/10.3390/electronics15091934 - 2 May 2026

Viewed by 350

Abstract

Domain Generation Algorithms (DGAs) pose a persistent threat by enabling malware to dynamically generate numerous command-and-control domains, evading traditional blocklists. While machine learning-based detectors have achieved high accuracy, they operate as statistical pattern matchers and lack the human-like anomaly perception that enables security [...] Read more.

Domain Generation Algorithms (DGAs) pose a persistent threat by enabling malware to dynamically generate numerous command-and-control domains, evading traditional blocklists. While machine learning-based detectors have achieved high accuracy, they operate as statistical pattern matchers and lack the human-like anomaly perception that enables security experts to intuitively recognize unnatural domains. This paper introduces CogNormDGA, a cognitive-driven framework that models normal domain characteristics from a defender’s perspective while also anticipating how attackers might exploit cognitive blind spots. Inspired by dual-process theory, CogNormDGA combines intuitive, pattern-based screening (System 1) with analytical, rule-based evaluation of phonotactic, morphological, and semantic violations (System 2). The cognitive principles of System 1 and System 2 are computationally realized as two distinct pathways: an Attentional Salience Network and a Linguistic Constraint Evaluator, respectively. The framework produces interpretable outputs via attention saliency maps and cognitive violation reports. Extensive experiments on 400,000 domains spanning 33 DGA families demonstrate that CogNormDGA achieves competitive detection performance (F1-score 0.941) while establishing a cognitive-driven detection paradigm that produces human-aligned explanations—a property critical for practical security. It shows promising results on low-entropy and novel DGA families. Human subject studies confirm strong alignment between the model’s internal explanations and expert reasoning. Furthermore, CogNormDGA is particularly effective against low-entropy DGA families that exploit cognitive blind spots. By bridging cognitive science and cybersecurity, our work offers an interpretable and human-aligned approach to threat detection, with promising resilience that requires further validation. 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, 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 983

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 2 | Viewed by 884

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 1106

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 3508

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 996

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

Cited by 4 | Viewed by 2917

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

Cited by 1 | Viewed by 1522

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 10 | Viewed by 6761

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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