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Disruptive attacks on artificial neural networks: A systematic review of attack techniques, detection methods, and protection strategies

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Intelligent Systems with Applications 2025年 26卷

作者： Alobaid, Ahmad Bonny, Talal Alrahhal, Maher Research Institute of Sciences and Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

This paper provides a systematic review of disruptive attacks on artificial neural networks (ANNs). As neural networks become increasingly integral to critical applications, their vulnerability to various forms of attack poses significant security challenges. This review categorizes and analyzes recent advancements in attack techniques, detection methods, and protection strategies for ANNs. It explores various attacks, including adversarial attacks, data poisoning, fault injections, membership inference, model inversion, timing, and watermarking attacks, examining their methodologies, limitations, impacts, and potential improvements. Key findings reveal that while detection and protection mechanisms such as adversarial training, noise injection, and hardware-based defenses have advanced significantly, many existing solutions remain vulnerable to adaptive attack strategies and scalability challenges. Additionally, fault injection attacks at the hardware level pose an emerging threat with limited countermeasures. The review identifies critical gaps in defense strategies, particularly in balancing robustness, computational efficiency, and real-world applicability. Future research should focus on scalable defense solutions to ensure effective deployment across diverse ANN architectures and critical applications, such as autonomous systems. Furthermore, integrating emerging technologies, including generative AI models and hybrid architectures, should be prioritized to better understand and mitigate their vulnerabilities. © 2025 The Author(s)

关键词： Deep neural networks

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Expectation maximization—vector approximate message passing based generalized linear model for channel estimation in intelligent reflecting surface-assisted millimeter multi-user multiple-input multiple-output systems

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PeerJ computer Science 2025年 11卷 e2582-e2582页

作者： Ali, Shoukath K. Philip, Sajan P. Khan, Arfat Ahmad Moses, Leeban Cengiz, Korhan Akleylek, Sedat Ivković, Nikola Department of Electronics and Communication Engineering Presidency University Karnataka Bengaluru India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu Erode India Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Department of Electrical-Electronics Engineering Istinye University Istanbul Turkey Institute of Computer Science University of Tartu Tartu Estonia Faculty of Organization and Informatics University of Zagreb Pavlinska Varaždin Croatia

Channel estimation poses a main challenge in intelligent reflecting surface (IRS)assisted millimeter wave (mmWave) multi-user multiple-input multiple-output (MIMO) systems due to the substantial number of antennas at the base station (BS) and the passive reflective elements within the IRS lacking sufficient signal processing capabilities. This article addresses this challenge by proposing a channel estimation technique for IRS-assisted mmWave MIMO systems. The problem of channel estimation is normally taken as a compressed sensing (CS) problem, typically addressed through algorithms such as Orthogonal Matching Pursuit (OMP), Generalized Approximate Message Passing (GAMP), and Vector Approximate Message Passing with Expectation-Maximization (EM-VAMP). EM-VAMP demonstrates better performance only when a Gaussian mixture (GM) distribution is chosen as the prior for the sparse channel, especially at high signal-to-noise ratios (SNRs). To address this, the article introduces the application of generalized linear models (GLMs), extensions of standard linear models, providing increased flexibility in modeling data that deviates from Gaussian distribution. Numerical results unveil that the proposed Its EM-VAMP-GLM is much more robust to the existing OMP, GAMP and EM-LAMP algorithms. Copyright 2025 K et al. Distributed under Creative Commons CC-BY 4.0

关键词： Gaussian distribution

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Hybrid BiGRU-CNN Model for Load Forecasting in Smart Grids with High Renewable Energy Integration

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Ullah, Kaleem Shakir, Daniyal Abid, Usama Alahmari, Saad Aslam, Sheraz Ullah, Zahid US-Pakistan Centre for Advanced Studies in Energy University of Engineering and Technology Peshawar Peshawar Pakistan Sr. Business Analyst S&P Global Islamabad Islamabad Pakistan Department of Computer Science and Information Technology School of Computing Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Department of Computer Science CTL eurocolege Limassol Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Lombardia Italy

Integrating renewable energy sources into smart grids increases supply and demand management because renewable energy sources are intermittent and variable. To overcome this type of challenge, short-term load forecasting (STLF) is essential for managing energy, demand-side flexibility, and the stability of smart grids with renewable energy integration. This paper presents a new model called BiGRU-CNN to improve the operation of STLF in smart grids. The BiGRU-CNN model integrates bidirectional gated recurrent units (BiGRUs) to model temporal dependencies and convolutional neural networks (CNNs) to extract spatial patterns from energy consumption data. The newly developed BiGRU captures past and future contexts through bidirectional processing, and the CNN component extracts high-level features to enhance the accuracy of load demand prediction. The BiGRU-CNN model is compared with two other hybrid models, CNN-LSTM and CNN-GRU, on real-world data from American electric power (AEP) and ISONE datasets. Simulation results show that the proposed BiGRU-CNN outperforms other models, with single-step forecasting yielding root mean square error (RMSE) of 121.43 (AEP) and 123.57 (ISONE), mean absolute error (MAE) of 90.95 (AEP) and 62.97 (ISONE), and mean absolute percentage error (MAPE) of 0.61% (AEP) and 0.41% (ISONE). For multi-step forecasting, the model yields RMSE of 680.02 (AEP) and 581.12 (ISONE), MAE of 481.12 (AEP) and 411.20 (ISONE), and MAPE of 3.27% (AEP) and 2.91% (ISONE). The results show that the BiGRU-CNN model can generate accurate and reliable STLF, which is useful in managing massive renewable energy-integrated smart grids. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Convolutional neural networks

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Hybrid-RIS Empowered UAV-Assisted ISAC Systems: Transfer Learning-based DRL

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IEEE Transactions on Communications 2025年

作者： Saikia, Prajwalita Jee, Anand Singh, Keshav Huang, Wan-Jen Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros A. National Sun Yat-sen University Institute of Communications Engineering Kaohsiung Taiwan Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi110016 India University of Western Macedonia Department of Electrical and Computer Engineering Kozani50131 Greece University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several low mobility targets. We formulate a sum-rate optimization problem that accounts for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory, and transmit beamformer for integrated sensing and communication (ISAC). Subsequently, we introduce a transfer learning based approach combining with deep deterministic policy gradient (DDPG) to enhance the overall data rate while minimizing the time it takes for users to transmit data. Additionally, we present an alternating optimization (AO) algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and offers a solution that is very close to optimal. Finally, we showcase the superiority of the proposed scheme through Monte Carlo simulations. Also, we have compared the performance with perfect channel state information (CSI) counterpart. The outcomes of simulations confirm the theoretical analysis and demonstrate the efficiency of the proposed framework. Additionally, the results reveal the advantages of incorporating HRIS aided UAV assisted ISAC in improving the quality of both communication and sensing performance. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

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A Unified Multi-Domain Face Normalization Framework for Cross-domain Prototype Learning and Heterogeneous Face Recognition

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IEEE Transactions on Information Forensics and Security 2025年

作者： Pang, Meng Zhang, Wenjun Lu, Yang Cheung, Yiu-Ming Zhou, Nanrun Nanchang University School of Mathematics and Computer Sciences China Xiamen University Department of Computer Science and Technology School of Informatics China Hong Kong Baptist University Department of Computer Science Hong Kong Hong Kong Shanghai University of Engineering Science School of Electronic and Electrical Engineering China

Face normalization is a critical technique for improving the robustness and generalizability of face recognition systems by reducing intra-personal variations arising from expressions, poses, occlusions, illuminations, and domain shifts. Existing normalization methods, however, often lack the flexibility to handle multi-factorial variations and exhibit limited cross-domain adaptability. To address these challenges, we propose a Unified Multi-Domain Face Normalization Network (UMFN), which is designed to process facial images with diverse variations from various domains and reconstruct frontal, neutralized facial prototypes in the target domain. As an unsupervised domain adaptation model, the UMFN facilitates concurrent training across multiple cross-domain datasets and demonstrates robust prototype reconstruction capabilities. Notably, the UMFN functions as a joint prototype and feature learning framework, extracting domain-agnostic identity features through a decoupling mapping network and adversarial training with a feature domain classifier. Furthermore, we design an efficient Heterogeneous Face Recognition (HFR) network that integrates these domain-agnostic features and the identity-discriminative features extracted from normalized prototypes, enhanced by contrastive learning to improve identity recognition accuracy. Empirical evaluation on multiple cross-domain benchmark datasets validate the effectiveness of the UMFN for face normalization and the superiority of the HFR network for heterogeneous face recognition. © 2005-2012 IEEE.

关键词： Adversarial machine learning

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Joint Beamforming and Power Allocation Design for Wirelessly Charged Unmanned Inspecting Vehicles in Power Transmission Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Huang, Yingjie Abdelhady, Amr M. Wang, Qianggang Ye, Jia Chi, Yuan Eltawil, Ahmed M. El-Sherif, Amr A. ElBatt, Tamer Chongqing University State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing400044 China King Abdullah University of Science and Technology Computer Electrical and Mathematical Science and Engineering Division Province Makkah23955-6900 Saudi Arabia Electronics and Communications Department Egypt University of Informatics Cairo Governorate Egypt Department of Computer Science and Engineering The American University Cairo Governorate Cairo Egypt

The rapid development of the smart grid has intensified research efforts towards automating power transmission system inspections using unmanned intelligent vehicles (UIVs). A major challenge hindering their large-scale deployment is the short operational endurance of these vehicles. This paper addresses this limitation by proposing the use of an energy transmitter (ET) and a reconfigurable intelligent surface for wirelessly charging the vehicles through radio frequency (RF) signals. Additionally, we tackle the nonlinearity of RF-to-direct current (DC) conversion, which shows efficiency saturation at high power levels, by introducing a novel power-splitting (PS) energy harvesting (EH) receiver architecture in the inspection vehicles. The inspection tasks are divided into multiple time slots, and a power transfer efficiency (PTE) maximization problem is formulated to jointly optimize the beamforming design at both the transmitter and the RIS, as well as the transmit power allocation and receiving power splitter settings for each time slot. This approach aims to meet the energy requirements of the unmanned inspection vehicles while adhering to the total available power constraint at the ET. To solve the non-convex PTE maximization problem, we propose an efficient solution utilizing alternating optimization, the Dinkelbach’s method, and successive convex approximation, considering the unique properties of channel structure and finite phase resolution. Numerical results demonstrate that our proposed system and design significantly extend the lifespan of inspection vehicles and achieve high energy efficiency in a low-complexity manner. © 2017 IEEE.

关键词： Inductive power transmission

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Public space security management using digital twin technologies

arXiv

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arXiv 2025年

作者： Zindros, Stylianos Chronis, Christos Radoglou-Grammatikis, Panagiotis Argyriou, Vasileios Sarigiannidis, Panagiotis Varlamis, Iraklis Papadopoulos, Georgios Th. Department of Informatics and Telematics Harokopio University of Athens Athens Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom

As the security of public spaces remains a critical issue in today’s world, Digital Twin technologies have emerged in recent years as a promising solution for detecting and predicting potential future threats. The applied methodology leverages a Digital Twin of a metro station in Athens, Greece, using the FlexSim simulation software. The model encompasses points of interest and passenger flows, and sets their corresponding parameters. These elements influence and allow the model to provide reasonable predictions on the security management of the station under various scenarios. Experimental tests are conducted with different configurations of surveillance cameras and optimizations of camera angles to evaluate the effectiveness of the space surveillance setup. The results show that the strategic positioning of surveillance cameras and the adjustment of their angles significantly improves the detection of suspicious behaviors and with the use of the DT it is possible to evaluate different scenarios and find the optimal camera setup for each case. In summary, this study highlights the value of Digital Twins in real-time simulation and data-driven security management. The proposed approach contributes to the ongoing development of smart security solutions for public spaces and provides an innovative framework for threat detection and prevention. © 2025, CC BY.

关键词： Subway stations

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AI-Driven Cybersecurity and Functional Framework for Remote Laboratories: A Software engineering Perspective

AI-Driven Cybersecurity and Functional Framework for Remote ...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nesreen Otoum Enrique Arias Jesús Jover Alfaro Department of Software Engineering University of Petra Amman Jordan Department of Computer Systems University of Castilla-La Mancha Albacete Spain Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Remote laboratories have reshaped the way engineering courses provide hands-on experimentation by enabling students to control physical lab equipment via online interfaces. nevertheless, their ease, However, these platforms face challenges such as security vulnerabilities, performance instability, and limited user support. This paper introduces a layered framework that integrates artificial intelligence (AI) into a secure, modular design. Using conventional software engineering techniques, our approach improves cybersecurity through intelligent threat detection, automated resource balancing, and context-aware user interaction. We provide a comprehensive explanation of the framework's architecture, which methodically addresses critical requirements. This includes a proactive cybersecurity apparatus that adapts to emergent threats, as well as real-time device control and data acquisition. Ultimately, this work aims to assist educators and institutions build or modernize remote lab environments that offer robust protection and seamless learning outcomes, even under rapidly shifting demands.

关键词： Remote laboratories computer architecture Threat assessment Real-time systems Resource management Reliability computer security Protection Standards Software engineering

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Evaluating the Impact of Data Cleaning for Improving Road Damage Detection Through Vision Transformer

Evaluating the Impact of Data Cleaning for Improving Road Da...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Andy Desman Kahlil Muchtar Maulisa Oktiana Maya Fitria Yuwaldi Away Fardian Fardian Hammam Riza Dept. of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Department of Informatics Universitas Syiah Kuala The Master Program in Artificial Intelligence Universitas Syiah Kuala (USK) Banda Aceh Indonesia Department of Informatics BRIN (National Research and Innovation Agency) Indonesia The Master Program in Artificial Intelligence Universitas Syiah Kuala Banda Aceh Indonesia

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

The WHO predicts that by 2030 road accidents will be the 5th leading cause of death. Globally, road accidents account for 1.25 million casualties each year, and road defects cause 34% of these casualties. The road survey process in many countries have several challenges, one of which is detection using cameras that do not have a recognition system. In this study, a model with YOLOS architecture based on Vision Transformer trained on the RDD2022 dataset successfully recognizes road damage well, as indicated by the number of objects detected, bounding box on accurate objects, and the ability to recognize objects with inconsistent shadow and light inference. This research uses assessment parameters such as Average Precision (AP) and Average Recall (AR) to determine the overall performance of the model. The model achieves the highest AP value at Intersection of Union (IoU) 0.5, 0.75, and 0.5-0.95, worth 62.1%, 37.1%, and 36.2% respectively, and the highest AR value in Large, Medium, and Small Areas, worth 42.1%, 60.3%, and 75.4% respectively. The supplementary material can be found through this link: https://***/watch?v=LzkI2e_IORE.

关键词： YOLO Surveys computer vision Road accidents Roads Manuals Transformers Cameras Cleaning Tuning

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