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40th Annual computer Security Applications Conference, ACSAC 2024

作者： Yilmaz, Yagiz Cetin, Orcun Ozturk, Omer Said Ekmekcioglu, Emre Arief, Budi Hernandez-Castro, Julio Faculty of Engineering and Natural Sciences Sabancı University Istanbul Turkey School of Computing University of Kent Canterbury United Kingdom Computer Systems Department Universidad Politécnica de Madrid Madrid Spain

ISBN: (纸本)9798331520885

This study assessed the impact and effectiveness of Distributed Denial of Service (DDoS) attacks during a period of about four months of the Russo-Ukrainian war, by observing the exchanges between the opposing sides. The data collection phase took place between the 28th of November 2022 and the 15th of April 2023. In total, we monitored 1,257 websites and web applications targeted in the conflict, with 633 targeted by pro-Russian and 624 by pro-Ukrainian entities. Only a small fraction (1.27%) of the targets remained unaffected, whereas 30.63% faced complete shutdowns. When considering the extent of the attacks conducted by the belligerents in the war, the attacks by pro-Russian entities showed a slightly more successful overall impact, with 36.18% of their targets were taken down, compared to 25.00% on the opposite side. Businesses demonstrated greater resilience against DDoS attacks compared to governmental and educational institutions. An in-depth analysis revealed significant differences in target categories, despite both sides primarily targeting businesses. Our findings regarding the usage of DDoS protection services among the 1,257 analysed targets showed that only 13.37% used such services. Among these minority of users, 70.24% had protection from the beginning of our analysis, while 29.76% adopted it only after experiencing attacks. We also looked into the use of geolocation-based access policies on websites targeted by pro-Ukrainian entities. Our findings indicated that most of these websites do not implement geolocation-based access restrictions. To an extent, such restrictions could have been useful for preventing some unsophisticated attacks. Surprisingly, only a small percentage (4.50%) restricted access to solely Russian addresses, while a fraction (12.56%) seemed to implement adaptive access policies in response to cyberattacks. Lastly, and quite surprisingly for us, we discovered that a significant number of targets on the Russian side were using an

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Human Gait Recognition:A Deep Learning and Best Feature Selection Framework

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computers, Materials & Continua 2022年第1期70卷 343-360页

作者： Asif Mehmood Muhammad Attique Khan Usman Tariq Chang-Won Jeong Yunyoung Nam Reham R.Mostafa Amira ElZeiny Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Department of Information Systems Faculty of Computers and Information SciencesMansoura UniversityMansoura35516Egypt Department of Information Systems Faculty of Computers and InformationDamietta UniversityDamiettaEgypt

Background—Human Gait Recognition(HGR)is an approach based on biometric and is being widely used for *** is adopted by researchers for the past several *** factors are there that affect the system performance such as the walking variation due to clothes,a person carrying some luggage,variations in the view ***—In this work,a new method is introduced to overcome different problems of HGR.A hybrid method is proposed or efficient HGR using deep learning and selection of best *** major steps are involved in this work-preprocessing of the video frames,manipulation of the pre-trained CNN model VGG-16 for the computation of the features,removing redundant features extracted from the CNN model,and *** the reduction of irrelevant features Principal Score and Kurtosis based approach is proposed named *** that,the features of PSbK are fused in one ***,this fused vector is fed to the One against All Multi Support Vector Machine(OAMSVM)classifier for the final ***—The system is evaluated by utilizing the CASIA B database and six angles 00◦,18◦,36◦,54◦,72◦,and 90◦are used and attained the accuracy of 95.80%,96.0%,95.90%,96.20%,95.60%,and 95.50%,***—The comparison with recent methods show the proposed method work better.

关键词： Human gait recognition deep features extraction features fusion features selection

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Blockchain-Based Decentralized Breast Cancer Patient Monitoring System for Remote Healthcare Security 5

Blockchain-Based Decentralized Breast Cancer Patient Monitor...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Dilshan, K.N. Rathnayake, H.A.C.K. Pathirana, L.P.T.L. Dhanapala, D.H.S.V. Fernando, Harinda Abeygunawardhana, Pradeep K.W Sri Lanka Institute of Information Technology Faculty of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Breast cancer is a disease that is prevalent all over the world, and that is essential for the efficient treatment and care of breast cancer patients who are monitored remotely. However, traditional conventional centralized systems for patient monitoring face challenges in providing effective and efficient care for breast cancer patients, including limited access to medical records, ineffective communication, and patient participation. Fragmentation of records and difficulty in obtaining information can hinder patient care in terms of both privacy and data security. The proposed system uses machine learning, blockchain, smart contracts, hybrid access control, and hybrid intrusion prevention and detection to diagnose breast cancer decentralized. The technology analyses medical data using advanced machine learning algorithms to diagnose breast cancer accurately and provide healthcare practitioners with relevant insights. The blockchain stores patient information, diagnostic results, and metadata transparently and securely to protect data integrity and privacy. Smart contracts automate and enforce rules, securing data and improving diagnostic workflow. User roles and attributes are used to manage access to sensitive data and improve collaboration. Patients, doctors, and researchers have different permits and limits. Secures granular data and prevents unauthorized access. Scalability and network congestion are addressed via a hybrid network management system. The system optimizes resource allocation, load balancing, and data transfer by integrating centralized and decentralized methodologies. These features reduce delays and assure seamless operation. Experimental results reveal that the suggested system detects breast cancer more accurately than centralized methods. Blockchain, smart contracts, and access control ensure data integrity, privacy, and over-security. Scalability and reactivity are improved by the hybrid network management system, making breast cancer detec

关键词： Blockchain

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A novel approach to predicting liquefaction-induced settlements using Kolmogorov–Arnold Networks (KANs)

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Discover Geoscience 2024年第1期2卷 1-14页

作者： Karakaş, Seyidcem Garanti BBVA Payment Systems İstanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Soil liquefaction refers to the loss of strength and bearing capacity of soil under dynamic loads. Structures built on liquefied soils experience settlement. In this study, the newly developed Kolmogorov–Arnold Networks (KAN) method, an innovative artificial neural network technique, was utilized to predict liquefaction-induced settlement. The KAN method, which extends beyond the traditional Multi-Layer Perceptron (MLP) approach, was compared with the Random Forest method for benchmarking purposes. Using data derived from laboratory and field studies, models were constructed using both methods, and their performances were analyzed. According to the results, the KAN model outperformed the RF model in terms of R2, MAE, MSE, and RMSE metrics. Additionally, the feature importance analysis on the KAN model identified cyclic stress ratio (csr) and corrected SPT blow count (n1_60) as the most significant variables. These results underscore the potential of the KAN method in enhancing predictive accuracy and reliability in geotechnical applications, paving the way for its broader acceptance and implementation in real-world scenarios.

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Sustainable-Security Assessment Through a Multi Perspective Benchmarking Framework

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computers, Materials & Continua 2022年第6期71卷 6011-6037页

作者： Ahmed Saeed Alfakeeh Abdulmohsen Almalawi Fawaz Jaber Alsolami Yoosef B.Abushark Asif Irshad Khan Adel Aboud SBahaddad Md Mottahir Alam Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow-226025Uttar PradeshIndia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow-226028Uttar PradeshIndia

The current cyber-attack environment has put even the most protected systems at risk as the hackers are nowmodifying technologies to exploit even the tiniest of weaknesses and infiltrate *** this situation,it’s critical to design and construct software that is both secure and *** security is the most well-defined aspect of health information software systems,it is equally significant to prioritise sustainability because any health information software system will be more effective if it provides both security and sustainability to the customers at the same *** this league,it is crucial to determine those characteristics in the systems that can help in the accurate assessment of the sustainable-security of the health information software during the development *** research work employed the Fuzzy Analytic Network Process(Fuzzy ANP)to estimate the impact of the overall sustainable-security of health information software systems and their characteristics in order to achieve a high level of ***,the study validates the efficacy of the Fuzzy ANP procedure by testing it on five different versions of a health information software system through Fuzzy Technique for Order of Preference by Similarity to Ideal Solutions(Fuzzy TOPSIS).Despite the sensitivity of the health information software systems,this study employedmultiple versions of health information software *** compared with the existing procedures for testing the sustainable-security of health information software systems,the outcomes were conclusive and significantly more *** saving time and resources,the mechanism suggested in this research work aims to establish an outline that software practitioners can follow to enhance the sustainablesecurity of health information software systems.

关键词： Sustainable-security sustainable health information software systems analytic network process fuzzy logic

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied Mathematics and Mechanics(English Edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTERO Y.ESPINOSA-ALMEYDA R.RODRIGUEZ-RAMOS J.MERODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FEM) asymptotic homogenization method(AHM) micropolar elasticity

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Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

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computers, Materials & Continua 2023年第9期76卷 3483-3498页

作者： Asma A.Alhashmi Abdulbasit A.Darem Sultan M.Alanazi Abdullah M.Alashjaee Bader Aldughayfiq Fuad A.Ghaleb Shouki A.Ebad Majed A.Alanazi Department of Computer Science Northern Border UniversityArar9280Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Information Systems College of Computer Science and InformationJouf UniversitySakakaAljoufSaudi Arabia School of Computing University Teknologi Malaysia81310 UTM Johor BahruJohor81310Malaysia Department of Computer and Electronic Engineering Sana’a Community CollegeSana’a5695Yemen

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

关键词： API-based hybrid malware detection model static and dynamic analysis malware detection

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Digitalization of Building Electrical systems Towards Energy Efficiency: Planning and Design Insights

Digitalization of Building Electrical Systems Towards Energy...

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Renewable Energies and Smart Technologies (REST), International Conference on

作者： Edon Hajdini Agnesë Avdiu Mimoza Ibrani Department of Power Systems Faculty of Electrical and Computer Engineering (Alumni) Prishtina Republic of Kosovo Department of Telecommunications Faculty of Electrical and Computer Engineering Prishtina Republic of Kosovo

ISBN: (数字)9798350358902

ISBN: (纸本)9798350358919

The digitalization of building electrical systems is considered a transformative step towards enhancing energy efficiency. In this paper are presented the design and analysis in-sights of power digitalization in electrical systems for commercial buildings using EcoStruxure Power Design software, allowing for the simulation of electrical loads and providing the right circuit breakers and cable sections of feeders based on the planned load. To comprehensively analyze and harness the benefits of digitalization, we initially developed a conventional electrical system. Through this conventional scheme, shortcomings, and devised strategies are identified to enhance energy efficiency, preventive maintenance, proactive maintenance, as well as monitoring and remote commanding after implementation. To conduct the comparative analysis, we present a design of a digital electrical system where implementation within the project is structured across two layers: connected products and edge control. This integrated approach optimizes the operational efficiency and performance of the electrical system while leveraging the capabilities afforded by digitalization. A comparative analysis of the conventional electrical system and the digital electrical system is presented, highlighting the importance and advantages of digitalization.

关键词： Renewable energy sources Buildings Reliability engineering Energy efficiency Software Maintenance Software reliability

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data systems 9

Towards Eco-Sustainability and Green Analytics Model to Meas...

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9th International Conference on Information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

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