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12th International Conference on Computational Data and Social Networks, CSoNet 2023

作者： Nguyen, Bui Kim Ngan Nguyen, Binh Thuc Tram Tran, Tuan-Dung Duy, Phan The Pham, Van-Hau Faculty of Computer Networks and Communications University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Information Security Laboratory University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819706686

The emergence of Blockchain technology has inaugurated a transformative era by its multifaceted advantages and wide-ranging applications across diverse industries. Nevertheless, while holding great promise, Blockchain encounters a significant challenge in achieving interoperability within the complex landscape of multi-blockchain ecosystems. The imperative necessity for seamless data and digital asset exchange is evident, yet it is accompanied by the escalating complexities of managing network entity identities dispersed across disparate systems resulting in a stark dearth of cohesive connectivity and agile interaction. Henceforth, in response to these challenges, we present ‘MIDAS’ as a captivating research endeavor aimed at fostering seamless interoperability among a multitude of blockchain networks. MIDAS acts as a key intermediary, skillfully facilitating cross-chain interactions, elevating user experience, and ensuring secure data access for authorized users. In order to augmenting this architecture’s prowess while also focusing on user-centric data control, we introduce a pioneering decentralized identity management system known as "Blockchain Interoperability Decentralized Identifier" (BIDI). BIDI offers a range of powerful features, including strict entity management, seamless connections, and customized data access controls for authorized users, with a focus on user-centric and data ownership. In other words, our framework is a significant step towards improving the effectiveness and versatility of blockchain technology, breaking down barriers for harmonious coexistence among multiple blockchains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Sentiment Analysis using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency

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Annals of Data Science 2024年第6期11卷 2019-2034页

作者： Loomba, Sankalp Dave, Madhavi Arolkar, Harshal Sharma, Sachin School of Cyber Security and Digital Forensics National Forensic Sciences University Gujarat Gandhinagar India Adani Institute of Digital Technology Management Gujarat Ahmedabad India Faculty of Computer Application and Information Technology GLS University Gujarat Ahmedabad India Indrashil University Gujarat Mehsana India

Due to the ever-increasing computing power and easy availability, social-networking platforms like Facebook, Twitter, etc. have become a popular medium to express one’s views instantly, be it about political situations, commercial products, or social occurrences. Twitter is a powerful source of information, whose data can be utilized to investigate the opinions of users through a process called Opinion Mining or Sentiment Analysis. Using the principles of Natural Language Processing and data science, this paper presents a comparative evaluation of multiple lexicon-based sentiment analysis algorithms to extract public opinion from tweets. The study explores the nuances of sentiment analysis using data science methodology, assessing how various lexicon-based algorithms may successfully identify and classify sentiments expressed in tweets from the Indian community about cryptocurrency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Sentiment analysis

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Optimizing Resource Allocation Framework for Multi-Cloud Environment

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computers, Materials & Continua 2023年第5期75卷 4119-4136页

作者： Tahir Alyas Taher M.Ghazal Badria Sulaiman Alfurhood Ghassan F.Issa Osama Ali Thawabeh Qaiser Abbas Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)43600BangiSelangorMalaysia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Skyline University College University City Sharjah1797SharjahUAE Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia

Cloud computingmakes dynamic resource provisioning more *** a functioning service is crucial,and changes are made when particular criteria are *** research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to *** allocation is a complex challenge due to the versatile service providers and resource *** engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of *** objective of a coherent and rational resource allocation is to attain the quality of *** also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud *** three main parameters of the proposed framework are data accessibility,optimization,and *** an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service *** CloudSim simulator has been used to validate the suggested *** experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained *** results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

关键词： Multi-cloud query optimization cloud resources allocation modelling virtualization

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Machine Learning Supported Network Attack Detection with a Novel Method Based on Random Forest-Based Feature Fusion 17

Machine Learning Supported Network Attack Detection with a N...

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17th International Conference on information security and Cryptology, ISCTurkiye 2024

作者： Cinar, Cihan Dogru, Ibrahim Alper Atacak, Ismail Information Security Engineering Graduate School of Natural and Applied Sciences Gazi University Ankara Turkey Department of Computer Engineering Faculty of Technology Gazi University Ankara Turkey

ISBN: (纸本)9798331533649

Nowadays, Innovative techniques, including artificial intelligence and machine learning (ML), have been employed in attack detection systems across numerous domains to create effective detection mechanisms that identify attacks more quickly and accurately. In this study, we propose a novel machine learning-based method that utilizes Random Forest (RF)-based feature fusion (RFBFF) to detect anomalies in computer networks. This method integrates two basic procedures: feature fusion and classification. In feature fusion, unlike traditional feature selection, all features are utilized in the creation of the new data set. This allows for the incorporation of the impact of each feature on the outcome. The feature fusion procedure generates a reduced dataset by combining similar features based on RF-derived gain scores, while the classification procedure uses this refined dataset to label the attack status in the network using various ML algorithms. Specifically, Decision Tree (DT), K-Nearest Neighbor (k-NN), Additive Logistic Regression (Logitboost), and Naïve Bayes (NB) have been employed as classification algorithms. The effectiveness of the proposed method in network attack detection has been validated by comparing it with ML methods based on RF-based feature selection (RFBFS). For a robust comparison and see the performance of the model in the real world, the NSL-KDD and UNSW_NB15 data sets, which contain data packet examples of normal or attack traffic that can be encountered in the internet environment, categorized with binary classification, were preferred as the benchmark data set. Experimental results demonstrate that the proposed approach excels in detecting network attacks, achieving an accuracy rate of 0.995 on the NSL-KDD dataset and 0.924 on the UNSW-NB15 dataset. Leveraging the gain scores of all features, even if they are relatively low, has been found to positively impact the model's overall performance. © 2024 IEEE.

关键词： Network intrusion

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Utilizing Machine Learning in Medical Diagnosis: Systematic Review and Empirical Analysis 24

Utilizing Machine Learning in Medical Diagnosis: Systematic ...

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24th International Arab Conference on information technology, ACIT 2023

作者： Alazaidah, Raed Hassan, Mohammad Al-Rbabah, Lara Samara, Ghassan Yusof, Marina Al-Sherideh, Ala'a Saeb Zarqa University Ai Faculty of Information Technology Department of Data Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan The Hashemite University Faculty of Engineering Department of Computer Engineering Zarqa Jordan Selanger Malaysia Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798350384307

During the last few years, several real-life applications have attempted to utilize the proven high capabilities of artificial intelligence in general and machine learning in particular. Machine learning has been utilized in several domains, such as spam detection, image recognition, recommendation systems, self-driving cars, and medical diagnosis. This paper aims to survey the most related work of utilizing machine learning in the domain of medical diagnosis. Moreover, the paper proposes a comparative analysis for identifying and determining the best classification model and feature selection method in mind of handling medical datasets. Hence, four different medical datasets have been used to train twenty-three classification models and four well-known feature selection methods with respect to several evaluation metrics such as Accuracy, True Positive ratio, False Positive ratio, Precision, and Recall. The results reveal that RandomForest, J48, and SMO classifiers are the best classifiers when it comes to handling medical datasets respectively. Furthermore, the Gain Ratio method is the best choice for handling the step of feature selection. © 2023 IEEE.

关键词： Classification Feature Selection Machine Learning Medical Diagnosis Prediction

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Multi-Party Privacy Policy Management in the Context of Health Monitoring: An Exploratory Study 23

Multi-Party Privacy Policy Management in the Context of Heal...

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8th International Conference on Sustainable information Engineering and technology, SIET 2023

作者： Sun, Luyi Yeng, Prosper Fauzi, Muhammad Ali Khatiwada, Pankaj Yang, Bian Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik Norway Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (纸本)9798400708503

There is an increasing number of assisted living technologies and health monitoring systems to provide electronic health services and help patients and the elderly stay at home longer. It ensures users' safety and reduces caregivers' workload. Regardless of the benefits that health monitoring systems have brought to users, they are in the face of privacy challenges, which can influence users' adoption of these technologies. One of the challenges is multi-party privacy policy management. As different parties may have different considerations in the same scenario, they can make different privacy decisions. Therefore, a privacy policy management scheme is required to deal with conflicts. In this paper, we present the work of a user study with the scope of collecting several participants' opinions on multi-party privacy policy management in the context of health monitoring. One focus group and eight semi-structured interviews were conducted. Our questions for participants adopt strategies from online social networks. After that, we discuss participants' views on privacy decision conflict resolution in different health conditions and whether a recommendation system is needed to help with multi-party privacy policy management. We try to make prioritization based on participants' views and the emergency level of events in different scenarios, increase technology adoption, and ensure health services are better delivered. The results reflect the importance of adopting flexible strategies like majority weighted voting in different health conditions, which serves as a basis for multi-party privacy policy scheme development. © 2023 Owner/Author.

关键词： Social networking (online)

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A Survey of information security Roles and Trends in the Quranic Perspective 1

A Survey of Information Security Roles and Trends in the Qur...

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1st International Conference on Cyber security and Computing, CyberComp 2024

作者： Yunus, Mohd Amin Mohd Rahman, Nurul Hidayah Ab Hamid, Isredza Rahmi A. Universiti Tun Hussein Onn Malaysia Applied Information System Faculty of Computer Science and Information Technology Parit Raja Malaysia Centre of Information Security Research Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia

ISBN: (纸本)9798350387728

In this study, we surveyed the existing information security literature related to the Quran to contribute to the knowledge gap(s) in seeking the role of information security in the Quran research area. Materials published in English were located between January 2019 and February 2024 by searching various academic databases, including Google Scholar, IEEEXplore, ACM Digital Library, Springer and ScienceDirect. Thirty-two publications were collected and classified into ten domains of information security Common Body of Knowledge to identify the research trends. The results show that Domain 3 (Cryptography and Digital Watermarking) is the most studied area, followed by Domain 2 (Ethical and Legal Issues) with eight publications and Domain 5 (Access Control) with three publications. Other domains consist of only 1 or 2 publications each. Our observation also showed that three recurring themes of studies that are the implementation of information security techniques to preserve digital Quran authenticity, the association of Quran verses in supporting information security approaches;and the development of information security techniques from the Quran epistemology. Overall, exploring the research trends and interconnected areas could lead to advancements in information security practices and Quranic studies, fostering how traditional wisdom influences modern technologies. © 2024 IEEE.

关键词： authenticity ethical information security Quranic

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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

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Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah Walid El-Shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia ***-quality images require well-tuned communication *** Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and *** data need a space away fromunauthorized access,modification,or *** requirements are to be fulfilled by digital image watermarking and *** paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication *** introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver *** proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD *** addition,image encryption is implemented based on chaos and DNA *** encrypted watermarked images are then transmitted through the wireless SC-FDMA *** linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted *** subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this *** study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked *** addition,different variants of SC-FDMA bas

关键词： Cybersecurity applications image transmission channel models modulation techniques watermarking and encryption

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Utilizing Smartwatches and Deep Learning Models for Enhanced Avalanche Victim Identification, Localization, and Efficient Recovery Strategies: An In-depth Study 5

Utilizing Smartwatches and Deep Learning Models for Enhanced...

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5th International Conference on information Management and Machine Intelligence, ICIMMI 2023

作者： Jha, Khyati Pasbola, Mansi Rai, Hari Mohan Amanzholova, Saule Department of Computer Science Dronacharya Group of Institutions Greater Noida India Faculty of Computer Technologies and Cyber Security International University of Information Technology Almaty Kazakhstan

ISBN: (纸本)9798400709418

This article explores the transformative impact of modern technology on avalanche response and safety, addressing the longstanding challenges associated with avalanches in mountainous regions. Historically, avalanches have presented significant dangers, compounded by the difficulties of conducting rescue operations in remote and harsh environments. However, recent technological advancements, including smartwatches with SOS features and specialized mobile applications, have ushered in a new era of avalanche safety. Smartwatches have emerged as crucial tools in avalanche safety. Equipped with SOS features and synchronized with mobile apps, these devices have revolutionized the way we approach avalanche emergencies. In this study, we propose a methodology that leverages smartwatch data and deep learning models to detect individuals buried under avalanche debris. This approach aims to significantly improve the chances of successful and timely rescue operations. The proposed methodology involves collecting data from smartwatches, which are equipped with various sensors to monitor location, movement, and environmental conditions. Deep learning models are then deployed to analyze this data, identifying patterns and anomalies associated with avalanche incidents. By harnessing these advanced technologies, our methodology offers the potential for real-time avalanche detection, thereby enhancing overall safety outcomes. One of the key strengths of this methodology is its adaptability to diverse avalanche scenarios. The study envisions a future where technology-driven avalanche safety becomes an integral component of outdoor activities, mitigating risks and enabling more effective response strategies in the face of this natural hazard. © 2023 ACM.

关键词： Deep learning

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