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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security information and Event Management systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Ethical Guidelines for the Application of Generative AI in German Journalism

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Digital Society 2025年第1期4卷 1-43页

作者： Hofeditz, Lennart Jung, Anna-Katharina Mirbabaie, Milad Stieglitz, Stefan Faculty of Business Administration and Economics Hochschule Niederrhein – University of Applied Sciences Mönchengladbach Germany Faculty of Computer Science University of Duisburg-Essen Duisburg Germany Faculty of Information Systems and Applied Computer Science University of Bamberg Bamberg Germany Faculty of Economics and Social Sciences University of Potsdam Potsdam Germany

Generative Artificial Intelligence (genAI) holds immense potential in revolutionizing journalism and media production processes. By harnessing genAI, journalists can streamline various tasks, including content creation, curation, and dissemination. Through genAI, journalists already automate the generation of diverse news articles, ranging from sports updates and financial reports to weather forecasts. However, this raises ethical questions of high relevance for media organizations and societies especially when genAI is used for more sensitive topics and at larger scale. To not jeopardize trustworthiness in journalistic organizations, it is important that the use of genAI in journalism is guided by moral principles. We therefore conducted 18 interviews with researchers and practitioners with expertise in AI-based technologies, journalism, and ethics from a German perspective in order to identify guidelines for the ethical use of genAI in media organizations. We derived requirements for the ethical introduction of genAI and actionable guidelines which explain how decision makers in media organizations should address ethical principles for the use of AI in the news production life cycle, in order to contribute to trustworthiness of journalistic organizations and products.

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IoT in urban development: insight into smart city applications, case studies, challenges, and future prospects

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PeerJ computer science 2025年 11卷 1-42页

作者： Salih, Sayeed Abdelmaboud, Abdelzahir Husain, Omayma Motwakel, Abdelwahed Elshaﬁe, Hashim Sharif, Mahir Hamdan, Mosab Department of Management Information Systems College of Business Administration in Hawtat Bani Tamim Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Humanities Research Center Sultan Qaboos University Muscat Oman Department of Information Technology Ajman University Ajman United Arab Emirates Department of Computer Science University of Khartoum Khartoum Sudan College of Computer Science King Khalid University Abha Saudi Arabia Computer Science Department Faculty of Computer Science Information Technology Omdurman Islamic University Omdurman Sudan Computer and Self Development Prince Sattam bin Abdelaziz University Al-Kharj Saudi Arabia School of Computing National College of Ireland Dublin Ireland

With the integration of Internet of Things (IoT) technology, smart cities possess the capability to advance their public transportation modalities, address prevalent trafﬁc congestion challenges, reﬁne infrastructure, and optimize communication frameworks, thereby augmenting their progression towards heightened urbanization. Through the integration of sensors, cell phones, artiﬁcial intelligence (AI), data analytics, and cloud computing, smart cities worldwide are evolving to be more efﬁcient, productive, and responsive to their residents’ needs. While the promise of smart cities has been marked over the past decade, notable challenges, especially in the realm of security, threaten their optimal realization. This research provides a comprehensive survey on IoT in smart cities. It focuses on the IoT-based smart city components. Moreover, it provides explanation for integrating different technologies with IoT for smart cities such as AI, sensing technologies, and networking technologies. Additionally, this study provides several case studies for smart cities. In addition, this study investigates the challenges of adopting IoT in smart cities and provides prevention methods for each challenge. Moreover, this study provides future directions for the upcoming researchers. It serves as a foundational guide for stakeholders and emphasizes the pressing need for a balanced integration of innovation and safety in the smart city landscape. © 2024 Salih et al. Distributed under Creative Commons CC-BY 4.0

关键词： Smart city

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Enhancing Cultural Tourism with VR: A Virtual Tour of Wat Pho

Enhancing Cultural Tourism with VR: A Virtual Tour of Wat Ph...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Pitcha Seripanichkarn Chananart Piyasoranart San Ratanasanya Porawat Visutsak Department of Computer and Information Science Faculty of Applied Science KMUTNB Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper presents the development of an interactive virtual reality (VR) web application for exploring Wat Pho, a UNESCO Memory of the World heritage site in Bangkok, Thailand. Aimed at enhancing cultural tourism and accessibility, the application provides a 360° virtual tour of key locations within the temple grounds. Developed using Unity and Firebase, the application integrates interactive elements such as navigational arrows, Thai audio narration, textual information, and a chatbot for user queries. This immersive VR experience offers a novel way to engage with Wat Pho’s rich history and cultural significance for both local and remote audiences, overcoming barriers of physical access.

关键词： Surveys Headphones Accuracy Navigation Immersive experience User experience Cultural differences History Iterative methods Testing

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in Engineering & sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Enhancing Breast Cancer Diagnosis: A Semi-Supervised Deep Learning Model for Automated MRI Tumor Segmentation

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Traitement du Signal 2025年第2期42卷 737-750页

作者： Alsemmeari, Rayan A. Iqbal, Zakir Bakhsh, Sheikh Tahir Alghamdi, Bandar M. Alsulami, Abdulaziz A. Alturki, Badraddin Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science COMSATS University Islamabad Abbottabad22060 Pakistan Cardiff School of Technology Cardiff Metropolitan University CardiffCF5 2YB United Kingdom Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Saudi Arabia

The upsurge in the number of breast cancer patients makes it important to diagnose the disease early so that effective treatment can save the patient's life. Breast cancer diagnosis is challenging;however, the adoption of various deep-learning techniques has made this hard work much more accessible for radiologists to diagnose breast cancer at an early stage. Numerous applications have been developed to provide practical solutions to aid the radiologist in medical image analysis. Magnetic Resonance Imaging (MRI) is considered the most accurate screening technique for breast cancer identification, and it has substantially contributed to decreasing the mortality rate by early breast cancer detection. In MRI imaging, breast tumor detection and segmentation are still regarded as a critical task due to the limited availability of annotated MRI scans that require more laborious to annotate data with accurate ground truth which is a time-consuming process and less feasible in medical imaging. This research presents a Semi-Supervised Deep Learning for Automated Tumor Segmentation (SDATS). A semi-supervised learning model is employed, utilizing labeled and unlabeled data for segmentation. Segment Anything Model (SAM) is used for tumors, using bounding boxes to isolate regions of interest and generating precise segmentation masks. The YOLOv8 model is utilized for breast tumor detection, identifying bounding boxes for regions of interest. Integrating YOLOv8 and SAM makes the proposed model more rigorous and aims to enhance efficiency without using pixel-level annotation for segmentation. This allows for more efficient processing of large datasets and accelerates the diagnostic process. Furthermore, the SDATS diagnoses breast cancer with quicker and more precise automatic segmentation. ©2025 The authors.

关键词： Mammography

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Optimization of 6G resource allocation using CyberTwin function-based service enhancement scheme

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Eurasip Journal on Wireless Communications and Networking 2025年第1期2025卷 1-27页

作者： Aldrees, Asma Min, Hong Daradkeh, Yousef Ibrahim Dutta, Ashit Kumar Anjum, Mohd Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha61421 Saudi Arabia School of Computing Gachon University Seongnam13120 Korea Republic of Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Al-Kharj16273 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences Almaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Department of Computer Engineering Aligarh Muslim University Aligarh202002 India

The most innovative Service-Optimized Logging for Resource Allocation (SOL-RA) is in situations that support 6G connection. The CyberTwin architecture optimizes the quality of service provided to end users in 6G communications by using the dependability of terahertz connections and interactions of a machine-type nature. Elastically sharing elastic resources among users is made possible by the proposed resource allocation strategy, which makes use of proprietary logger functions. This ensures that efficient allocation is achieved without overlapping or lengthy wait periods. Within the framework of SOL-RA, a categorization mechanism is implemented for dense requests, which differentiates them as either stationary or priority services. Resource allocations are dynamically done based on this categorization, which enhances the organization's responsiveness to different needs. Through a tree classifier learning mechanism, CyberTwin logs play an essential part in the processing of requests and the preparation of resources. This helps to ensure that resources are distributed without inefficiency. Requests that are generating a backlog are identified and processed in parallel by the system, which significantly reduces the amount of time that is wasted waiting. In order to guarantee a specialized distribution for the categorized outputs, resource allocations are directed by CyberTwin data logs. An examination of the performance of the SOL-RA scheme takes into account important metrics such service latency, service backlog, resource usage, and request-to-response ratio. This research offers insights into the efficacy of the scheme in maximizing service quality and resource consumption in 6G settings. © The Author(s) 2025.

关键词： Resource allocation

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GPSOM: group-based particle swarm optimization with multiple strategies for engineering applications

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Journal of Big Data 2025年第1期12卷 1-40页

作者： Yan, Jialing Hu, Gang Jia, Heming Hussien, Abdelazim G. Abualigah, Laith Department of Applied Mathematics Xi’an University of Technology Xi’an710054 China School of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China School of Information Engineering Sanming University Sanming365004 China Department of Computer and Information Science Linköping University Linköping58183 Sweden Faculty of Science Fayoum University Faiyum63514 Egypt Computer Science Department Al al-Bayt University Mafraq25113 Jordan

Particle Swarm Optimization (PSO) is a classic optimization algorithm;however, it has issues when solving high-dimensional complex optimization problems, such as sensitivity to initial parameters, insufficient population diversity, and susceptibility to becoming stuck in local optima. To this end, we propose a particle swarm optimization algorithm with multiple swarm strategies (GPSOM) that significantly improves the algorithm’s optimization performance by designing grouping and diversification strategies for the population. GPSOM divides the population into three subgroups and adopts targeted strategies based on different phases: sub-swarm 1 focuses on exploration and improves population diversity and local search ability by dynamically adjusting operators;sub-swarm 2 focuses on exploitation, introducing sine cosine factors and adaptive mechanisms to traverse the solution space efficiently;sub-swarm 3 achieves global and local balance, generating high-quality solutions through adaptive factors and position adjustment operators. The experimental results show that GPSOM performs significantly better than traditional algorithms on the Combinatorial Evolutionary Computing (CEC) 2020 benchmark test set (hereinafter referred to as 20TS), and its reliability and efficiency have been demonstrated in practical applications, such as 15 engineering examples, vehicle cruise control, and robot path optimization. GPSOM is an efficient, stable, practical optimization algorithm that effectively solves high-dimensional complex optimization problems. © The Author(s) 2025.

关键词： Particle swarm optimization (PSO)

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RDM: A Light Field Super-Resolution Model Improved with Retinex Decomposition 16

RDM: A Light Field Super-Resolution Model Improved with Reti...

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16th International Conference on Signal Processing systems, ICSPS 2024

作者： Lyu, Wenqi Ke, Wei Sheng, Hao Ma, Xiao Liu, Lixue Faculty of Applied Sciences Macao Polytechnic University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781510689251

Light field cameras hold significant value in applications such as depth estimation, 3D video acquisition, and image super-resolution. Compared to traditional single-image super-resolution methods, light field images can capture object information from multiple spatial and angular perspectives, which is more conducive to capturing detail information. However, current advanced light field super-resolution models still exhibit issues like blurriness, jaggedness, and dimness in 4x light field super-resolution. To address this, we propose a novel light field super-resolution framework that utilizes the Retinex decomposition concept to solve common problems such as ghosting and overlap, thereby improving image quality. By decomposing the light field image into illumination and reflection components and processing them separately, our model significantly enhances detail preservation and color consistency. Experimental results on multiple datasets demonstrate that our model exhibits outstanding performance, both subjectively and objectively, highlighting the effectiveness of this framework and its broad application potential in the field of computational imaging. © 2025 SPIE.

关键词： Image acquisition

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Surface-Continuous Scene Representation for Light Field Depth Estimation via Planarity Prior

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IEEE Transactions on Circuits and systems for Video Technology 2025年第5期35卷 5051-5066页

作者： Chen, Rongshan Sheng, Hao Yang, Da Cui, Zhenglong Cong, Ruixuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing Hangzhou International Innovation Institute Zhejiang Hangzhou311115 China Macao Polytechnic University Faculty of Applied Sciences China

Light field (LF) imaging captures both spatial and angular information of the real world, enabling precise depth estimation. However, images are merely discrete expressions of scenes. Limited by imaging technology, LF camera cannot capture the infinite rays emitted by scenes, leading to the discrete information storage (e.g. pixel). Consequently, previous deep learning methods have encountered challenges in accurately extracting depth information from LF images. In this paper, we investigate a surface-continuous scene representation using planarity prior and design PlaneNet, a Plane-based Network that successfully generates highly detailed depth maps for real scenes. Specifically, inspired by the plane assumption that real-world scenes generally yield piecewise smooth surfaces, we refine it to the pixel level for continuous surface approximation, which can overcome the limitations of discrete representation. Rather than explicitly parameterizing planes as multiple coefficients, we propose a novel plane regular sampling operator (PRSO), enabling the network to fit smooth depth surfaces easily. To explore the role of our theory at the feature level, we also introduce PRSO into the intermediate layers of PlaneNet. Experiments show that our method achieves state-of-the-art performance on both synthetic and real-world LF scenes, ranking 1st (MSE) on the HCI 4D Light Field benchmark. Furthermore, we explore the utilization of our representation in multiple LF depth estimation networks, and experiments demonstrate improved performance when surface-continuous representation is applied. © 1991-2012 IEEE.

关键词： Pixels

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