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WebShield 5.0: Harnessing AI and NLP to combat web threats in Industry 5.0

Alexandria engineering Journal 2025年 127卷 677-689页

作者： Verma, Priyanka O'Shea, Donna Newe, Thomas Vidyarthi, Ankit Gupta, Deepak Ali, Jabir Aldawsari, Hamad Breslin, John G. School of Computer Science University of Galway University road GalwayH91 TK33 Ireland Department of Electronics and Computer Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Digital Engineering University of Limerick Castletroy LimerickV94 T9PX Ireland Department of CSE&IT Jaypee Institute of Information Technology sector 62 Noida201309 India Maharaja Agrasen Institute of Technology Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida Delhi India Department of Computer Science Haql University College University of Tabuk Tabuk Saudi Arabia Data Science Institute University of Galway University road GalwayH91 TK33 Ireland

Industry 5.0 characterized by the integration of human intelligence and advanced technologies is inherently more connected and interdependent than previous industrial paradigms. This increased connectivity exposes to various web-based attacks and calls for strong security controls. To address the challenges and enhance attack detection, this paper introduces Ingress Manager (IM), a novel approach that amalgamates Natural Language Processing (NLP) with Machine Learning (ML) to mitigate web-based threats. By combining multimodal data and utilizing the Mayfly optimization algorithm for feature selection, IM carries out a thorough analysis for efficient web-based attack detection. Mayfly Optimization is considered to be a variation of Particle Swarm Optimization (PSO), combining the benefits of Firefly Algorithm, Genetic Algorithm (GA), and PSO. Experiments on the HTTP CSIC-2010 dataset show that this integration is effective, as evidenced by the notable gains in accuracy, precision, and F-score above baseline models. Notable performance metrics such as accuracy of 98.5753% along with thorough component analysis (ablation study) add deeper understanding to the proposed approach. The paper's contributions lie in its utilization of Industry 5.0 principles, the incorporation of Mayfly optimization for feature selection, and the innovative combination of NLP and ML for robust web-based attack detection. © 2025

关键词： Genetic algorithms

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation

Adaptive Particle Swarm Optimization of U-net Parameters for...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Jin Zhao Jun Wang Min Han Jianchao Fan School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science and Department of Data Science City University of Hong Kong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models.

关键词： Deep learning Image segmentation Adaptation models Accuracy Heuristic algorithms Clustering algorithms Radar polarimetry Particle swarm optimization Tuning Aquaculture

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DBSSL: A Scheme to Detect Backdoor Attacks in Self-Supervised Learning Models

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Huang, Yuxian Yang, Geng Yuan, Dong Yu, Shui Nanjing University of Posts and Telecommunication College of Computer Science and Software China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China University of Sydney School of Electrical and Information Engineering Australia University of Technology Sydney School of Computer Science Australia

Recently, self-supervised learning has garnered significant attention for its ability to extract high-quality features from unlabeled data. However, existing research indicates that backdoor attacks can pose significant threats to self-supervised learning. Additionally, due to the substantial training overhead, traditional backdoor detection methods in supervised learning, such as meta-learning, are not well-suited for self-supervised learning. Current backdoor detection methods for self-supervised learning can only defend against backdoor attacks triggered by patch. To address this challenge, we proposes DBSSL, a scheme that can efficiently detect backdoor attacks in self-supervised learning models. In DBSSL, we connect backdoor attacks with adversarial attacks and use adversarial perturbations for detection. Specifically, we first conduct targeted adversarial attacks on the samples, classifying them into different classes. Then, we employ the Z-Score method to analyze these adversarial perturbations. If outliers are present, we can infer that the self-supervised learning model has been backdoored. Unlike previous works, our scheme is better suited for downstream users with constrained computation resources and exhibits excellent detection capabilities for backdoor attacks caused by patch -type or global triggers. Abundant theoretical analysis has demonstrated the feasibility of our scheme. Moreover, extensive experiments show that our method performs well in detecting prevalent backdoor attacks in self-supervised learning, achieving detection accuracy exceeding 95%. © 2004-2012 IEEE.

关键词： Self-supervised learning

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Error Bound Analysis for the Regularized Loss of Deep Linear Neural Networks

arXiv

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arXiv 2025年

作者： Chen, Po Jiang, Rujun Wang, Peng School of Data Science Fudan University Shanghai China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

The optimization foundations of deep linear networks have received significant attention lately. However, due to the non-convexity and hierarchical structure, analyzing the regularized loss of deep linear networks remains a challenging task. In this work, we study the local geometric landscape of the regularized squared loss of deep linear networks, providing a deeper understanding of its optimization properties. Specifically, we characterize the critical point set and establish an error-bound property for all critical points under mild conditions. Notably, we identify the sufficient and necessary conditions under which the error bound holds. To support our theoretical findings, we conduct numerical experiments demonstrating that gradient descent exhibits linear convergence when optimizing the regularized loss of deep linear *** Codes 90C26, 68T07, 65K10 Copyright © 2025, The Authors. All rights reserved.

关键词： Errors

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Revealing Security Flaws in Cross-Modal Retrieval Models through Video Poisoning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Jin, Ming Hu, Wenbo Hong, Richang Zhu, Lei Hefei University of Technology School of Computer and Information Hefei China Data Space Research Institute Hefei Comprehensive National Science Center China Tongji University School of Electronic and Information Engineering Shanghai China

Video-text cross-modal retrieval is widely studied to improve retrieval accuracy. However, the security of video-text cross-modal retrieval models receives little attention. If attackers exploit the security vulnerabilities in these models, it poses a significant threat to the retrieval models. Thus, identifying security flaws in video-text cross-modal retrieval models becomes the focus of our research. We are the first to design a video poisoning model to uncover security vulnerabilities in retrieval models. Existing poisoning models have certain limitations when it comes to exploiting vulnerabilities in retrieval models. These include failing to comprehensively embed malicious information into the original video and being unable to maintain visual consistency between the original and poisoned videos. These limitations can result in unsuccessful attacks on retrieval models and an inability to effectively identify security flaws within them. To address these shortcomings, we design an efficient poisoning model that embeds malicious information thoroughly into the original clean data to attack video-text cross-modal retrieval models. We are the first to use a poisoning model to attack retrieval models, thereby uncovering their security vulnerabilities. Second, we introduce a bi-level poisoning module to ensure that malicious information is thoroughly embedded into the original video, thereby enhancing the attack capability of the poisoning model. Finally, we design an adversarial module to improve visual consistency between the original and poisoned videos, thus enhancing the concealment of malicious information within the training data of retrieval models. Our poisoning model can identify security flaws in video-text cross-modal retrieval models, providing insights into improving the security of retrieval models. The effectiveness of our model is validated on the MSR-VTT, LSMDC, and MSVD datasets. © 1991-2012 IEEE.

关键词： Content based retrieval

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Novel cross-dimensional coarse-finegrained complementary network for image-text matching

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PeerJ computer Science 2025年 11卷 e2725-e2725页

作者： Liu, Meizhen Khairuddin, Anis Salwa Mohd Hasikin, Khairunnisa Liu, Weitong Department of Electrical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia School of Data and Computer Science Shandong Women’s University Shandong Jinan China Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Kuala Lumpur Malaysia

The fundamental aspects of multimodal applications such as image-text matching, and cross-modal heterogeneity gap between images and texts have always been challenging and complex. Researchers strive to overcome the challenges by proposing numerous significant efforts directed toward narrowing the semantic gap between visual and textual modalities. However, existing methods are usually limited to computing the similarity between images (image regions) and text (text words), ignoring the semantic consistency between fine-grained matching of word regions and coarse-grained overall matching of image and text. Additionally, these methods often ignore the semantic differences across different feature dimensions. Such limitations may result in an overemphasis on specific details at the expense of holistic understanding during image-text matching. To tackle this challenge, this article proposes a new Cross-Dimensional Coarse-Fine-Grained Complementary Network (CDGCN). Firstly, the proposed CDGCN performs fine-grained semantic alignment of image regions and sentence words based on cross-dimensional dependencies. Next, a Coarse-Grained Cross-Dimensional Semantic Aggregation module (CGDSA) is developed to complement local alignment with global image-text matching ensuring semantic consistency. This module aggregates local features across different dimensions as well as within the same dimension to form coherent global features, thus preserving the semantic integrity of the information. The proposed CDGCN is evaluated on two multimodal datasets, Flickr30K and MS-COCO against state-of-the-art methods. The proposed CDGCN achieved substantial improvements with performance increment of 7.7–16% for both datasets. © 2025 Liu et al.

关键词： Coarse-grained modeling

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Multi-agent Scheduling for Shared Manufacturing Systems

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Procedia computer Science 2025年 253卷 727-736页

作者： Ege Duran Cemalettin Ozturk Barry O’Sullivan SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments.

关键词： Multiagent systems Scheduling planning parallel machine

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PP-DTV: A prior projection enhanced directional TV algorithm for limited-angle EPRI based on symmetric-arc projection distributions

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Zhang, Yanjun Liu, Peng Fang, Chenyun Qiao, Zhiwei School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Department of Big Data and Intelligent Engineering Shanxi Institute of Technology Shanxi Yangquan045000 China

The scanning time-consumption constrains the advancement of electron paramagnetic resonance imaging (EPRI). Limited-angle EPRI can decrease scanning time by reducing the angular range. However, as the angular range is reduced, the data model becomes more ill-posed and it becomes increasingly challenging to improve the reconstruction accuracy of the images. By designing suitable scanning configurations and optimization models, the reconstruction accuracy of images can be improved. For the scanning configuration, we propose the projection distribution with symmetric-arc structure to alleviate ill-posed conditions. It achieves the same effect as the full angle by using only half the angular range. For the optimization model, we design a prior projection enhanced directional total variation (PP-DTV) method. The objective term is the prior projection information-guided data fidelity term, and the constraints terms are the three DTV terms. We use the Chambolle-Pock (CP) algorithm to solve the model. We verify the correctness of the PP-DTV method on simulation data and explore the reconstruction ability of the PP-DTV method with symmetric-arc structure on simple and complex phantoms. The experimental results show that compared to TV, DTV and high-order DTV (HDTV) methods, the reconstructed image edges of our method are clearer and more complete, and there are fewer artifacts in the interior of the image. This finding provides new ideas and methods for other imaging modalities. © 2025 Elsevier Ltd

关键词： Electron resonance

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Indian Gross Domestic Product Per Capita Prediction based on Economic Indicators

Indian Gross Domestic Product Per Capita Prediction based on...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Sarveswaran MG Mahalakshmi B Varun Vaishnav Shruti Mishra Sandeep Kumar Satapathy Pradeep Kumar Mallick School of Computer Science & Engineering VIT University Chennai Tamilnadu India Centre for Advanced Data Science Vellore Institute of Technology Chennai Tamil Nadu India School of Computer Engineeing KIIT University Odisha India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

This paper is about identifying the contributing socio-economic indicators that affect the Gross Domestic Product Per Capita of India with historical data from 1960 to 2021 and build a methodology to predict the Gross Domestic Product per capita based on the contributing factors. To predict India's GDP per capita, an ensemble prediction was used, which included Support Vector Regression (SVR), the Extreme Gradient Boosting Model, and the ARIMA Model. It is critical to forecast GDP per capita and its influencing factors for better policy formulation, resource allocation, risk management, investment decisions, long-term planning, social welfare, and other purposes. This paper could contribute to encouraging long-term economic growth and improve citizens' quality of life. The findings of this paper provide an in-depth understanding of the intricate relationship between socioeconomic indicators and Gross Domestic Product in India.

关键词： Support vector machines Economic indicators Predictive models Boosting Planning Risk management Resource management Socioeconomics Investment

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