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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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TezNet: An Efficient Convolutional Neural Network for Optical Coherence Tomography Classification 6

TezNet: An Efficient Convolutional Neural Network for Optica...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Qaiser, Muhid Abbas, Muhammad Sohail Muhammad, Asif Haider, Khadija Khan, Haroon National University of Computer and Emerging Sciences Department of Artificial Intelligence and Data Science Islamabad Pakistan National University of Computer and Emerging Sciences Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Retinal diseases such as Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME) can severely impair vision and are challenging to reverse if detected late. Optical Coherence Tomography (OCT) serves as a non-invasive imaging modality to diagnose these conditions through cross-sectional retinal images. Current advancements in retinal disease detection predominantly focus on fine-tuning larger models to achieve high accuracy, often at the expense of substantial training time and data requirements. We introduce an innovative Convolutional Neural Network (CNN) named TezNet that is deeper and wider than existing custom CNNs for the dataset used in this research yet maintains a lightweight architecture with only 3.6 million parameters. This high efficiency is accomplished by leveraging depthwise and spatial separable convolutional techniques without employing preprocessing techniques or noise reduction filters. The data augmentation techniques generate diverse image qualities, ensuring robust predictions across various scenarios. By training exclusively on a single dataset, our model achieves rapid training times and optimal resource utilization, minimizing the risk of overfitting. This blend of advanced parameter optimization, minimal preprocessing, and strategic data augmentation underscores the potential of our CNN model for swift, precise, and resource-efficient retinal disease detection. TezNet model achieves a remarkable 96% accuracy, competing closely with state-of-the-art networks like ResNet50 (over 23M parameters), InceptionV3 (over 25M parameters), and ResNeXt (89M parameters). © 2024 IEEE.

关键词： Optical coherence tomography

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Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

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computers, Materials & Continua 2022年第12期73卷 4763-4777页

作者： Mir Mushhood Afsar Shizza Saqib Yazeed Yasin Ghadi Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073Korea

Virtual reality is an emerging field in the whole *** problem faced by people today is that they are more indulged in indoor technology rather than outdoor ***,the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person *** system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the *** track the human movement,sensors Micro Processor Unit(MPU6050)are used that are connected with Bluetoothmodules andArduino responsible for sending the sensor data to the ***,the sensor data is sent to a machine learning model,which detects the game played by the *** detected game will be operated on human gestures.A publicly available dataset named IM-Sporting Behaviors is initially used,which utilizes triaxial accelerometers attached to the subject’s wrist,knee,and below neck regions to capture important aspects of human *** main objective is that the person is enjoying while playing the game and simultaneously is engaged in some kind of sporting *** proposed system uses artificial neural networks classifier giving an accuracy of 88.9%.The proposed system should apply to many systems such as construction,education,offices and the educational *** experimentation proved the validity of the proposed system.

关键词： Artificial neural networks bluetooth connection inertial sensors machine learning virtual reality exergaming

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Using Blockchain to Overcome the Issues in Land Registry Management: A Systematic Review 1

Using Blockchain to Overcome the Issues in Land Registry Man...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Riaz, Ayesha Ikram, Muhammad Jawad Asadullah, Najmussaba University of Engineering and Technology Department of Computer Software Engineering Mardan Pakistan Jeddah International College Department of Computer Science and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

A Blockchain network contains a distributed ledger that is used to store a secure and permanent record of transactions among multiple parties. As the registries of land records are historically stored in the form of paper documentation where, there might arise the issue of losing, destroying, and modifying needed documents. This land registration process is complicated and time-consuming and required a lot of effort to transfer ownership of the land registry or change any information. For this purpose, various contributions have been made by different researchers to overcome the problem of the classical land registration process by using blockchain technology. In this survey, we shed light on some advantages (reduce time and effort, security, permanency, etc.) of blockchain to identify some early problems in the paper-based land registration process. © 2023 IEEE.

关键词： Blockchain

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QoSCAPE: QoS-Centric Adaptive Path engineering with Blockchain-Enabled Reinforcement Learning

QoSCAPE: QoS-Centric Adaptive Path Engineering with Blockcha...

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2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Karakus, Murat Guler, Evrim Ayaz, Furkan Uludag, Suleyman Department of Software Engineering Ankara University Ankara06830 Turkey Department of Computer Engineering Bartin University Bartin74110 Turkey Department of Computer Science University of Michigan-Flint FlintMI48502 United States

ISBN: (纸本)9798350379433

In the evolving landscape of effective network management, path engineering, also known as traffic engineering, optimization and selection remain a critical challenge. This paper presents an innovative framework that integrates software Defined Networking (SDN), Blockchain technology (BC), and Reinforcement Learning (RL) to enhance the efficiency and security of network path optimization. SDN's centralized control enables dynamic network traffic management, while BC ensures secure, transparent, and immutable logging of network transactions, fostering trust and accountability. Reinforcement learning, capable of learning and adapting from environmental interactions, is employed to dynamically optimize routing decisions. Our proposed framework, QoSCAPE: QoS-Centric Adaptive Path engineering with Blockchain-Enabled Multi-Agent Reinforcement Learning, leverages the programmability of SDN to collect real-time network state information and the decentralization of BC to secure this data. The numerical results underscore the superior efficiency of QoSCAPE, which consistently achieves near-total request success rates within milliseconds, significantly outperforming traditional HRA and DRA methods in rapidly fulfilling network service demands in terms of Path Setup Time (PST) and Requests Serviced (RS) metrics, yet notably surpasses all other approaches by minimizing Network Message Overhead (NMO). Its optimization in reducing message volume ensures efficient resource usage and preserves network scalability, distinguishing it as a superior choice for multi-ISP routing frameworks. © 2024 IEEE.

关键词： Efficiency

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A Comparison of the Performance of ResNet and DenseNet for Obstacle Avoidance and Path Following on a Mobile Robot Using Embedded GPUs

A Comparison of the Performance of ResNet and DenseNet for O...

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2024 IEEE International Conference on Agents, ICA 2024

作者： Ma, Shengjun Karkal, Pawan Sudhir Ma, Jing Alam, Fakhrul Auckland University of Technology Electrical and Electronic Engineering Department Auckland New Zealand Auckland University of Technology Computer Science and Software Engineering Department Auckland New Zealand

ISBN: (纸本)9798331539917

In this study, we evaluated the performance of deep learning-based navigation models on an novel platform that combines the Jetson Xavier NX with an RC four-wheel-drive car for autonomous driving. Focusing on data collection and model training using a single-camera setup, obstacle avoidance and path navigation were prioritised. The study compares the effectiveness of ResNet18, ResNet34, and DenseNet121 models by analysing training time, accuracy, obstacle avoidance efficiency, and path navigation capabilities. The results highlight the computational advantages of the Jetson Xavier NX over the Jetson Nano, demonstrating enhanced real-time processing and decision-making capabilities. This integration of the Jetson Xavier NX with an RC vehicle not only improves overall performance but also introduces a novel approach to autonomous vehicle development. The findings offer valuable insights into optimising deep learning models and hardware configurations for improved autonomous navigation. © 2024 IEEE.

关键词： Mobile robots

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A Tutorial on Federated Learning from Theory to Practice:Foundations,software Frameworks,Exemplary Use Cases,and Selected Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 824-850页

作者： M.Victoria Luzón Nuria Rodríguez-Barroso Alberto Argente-Garrido Daniel Jiménez-López Jose M.Moyano Javier Del Ser Weiping Ding Francisco Herrera Department of Software Engineering Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain Department of Communications Engineering University of the Basque Country(UPV/EHU)and also with TECNALIABasque Research&Technology Alliance(BRTA)Spain School of Information Science and Technology Nantong UniversityNantong 226019China

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

关键词： Data privacy distributed machine learning federated learning software frameworks

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A Cloud Computing Model for Libyan Higher Education Institutions 3

A Cloud Computing Model for Libyan Higher Education Institut...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Imragaa, Najwa A. Alfsai, Entesar H. Gargoum, Bothaina F. Maatuk, Abdelsalam M. University of Benghazi Department of Computer Science Benghazi Libya University of Benghazi Department of Software Engineering Benghazi Libya

ISBN: (纸本)9798350319897

Cloud computing is now changing the IT landscape and playing a significant economic role for companies that develop software. Users of educational resources now have more flexibility and access because of cloud computing. Students, faculty members, and support staff all use cloud computing in academic institutions. Internet-based computing, known as "Cloud Computing,"allows users to share IT resources securely and efficiently. The use of cloud computing in universities has many benefits, such as access to files, e-mail, databases, educational resources, and search applications as requested by faculty, administrators, staff, and students at a university alike. Moreover, cloud computing significantly reduces the complexity and cost of IT in universities. This paper discusses cloud computing, its structures, types, and services it provides, and then examines the possibility of implementing cloud services in universities and discovers the various opportunities and benefits of cloud services. The paper presents a proposed model for cloud computing for Libyan universities, which was applied to the University of Benghazi and produced satisfactory results and facilitated a lot of university work. Through the application of the proposed model, effective management of the technological needs of the Libyan university has been achieved, such as connecting programs and providing a platform for development, data storage and computing. © 2023 IEEE.

关键词： Application programs

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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