INTRODUCTION: The challenge of distributing false information continues despite the significant impact of social media on opinions. The suggested framework, which is a metaheuristic method, is presented in this research to detect bogus news. Employing a hybrid metaheuristic RDAVA methodology coupled with Bi-LSTM, the method leverages African Vulture Optimizer and Red Deer Optimizer.OBJECTIVES: The objective of this study is to assess the effectiveness of the suggested model in identifying false material on social media by employing social network analysis tools to combat disinformation.METHODS: Employing the data sets from BuzzFeed, FakeNewsNet, and ISOT, the suggested model is implemented on the MATLAB Platform and acquires high accuracy rates of 97% on FakeNewsNet and 98% on BuzzFeed and ISOT. A comparative study with current models demonstrates its superiority.RESULTS: Outperforming previous models with 98% and 97% accuracy on BuzzFeed/ISOT and FakeNewsNet, respectively, the suggested model shows remarkable performance.CONCLUSION: The proposed strategy shows promise in addressing the problem of false information on social media in the modern day by effectively countering fake news. Its incorporation of social network analysis methods and metaheuristic methodologies makes it a powerful instrument for identifying false news.
{"title":"Integrating Metaheuristics and Two-Tiered Classification for Enhanced Fake News Detection with Feature Optimization","authors":"Poonam Narang, Ajay Vikram Singh, Himanshu Monga","doi":"10.4108/eetsis.5069","DOIUrl":"https://doi.org/10.4108/eetsis.5069","url":null,"abstract":"INTRODUCTION: The challenge of distributing false information continues despite the significant impact of social media on opinions. The suggested framework, which is a metaheuristic method, is presented in this research to detect bogus news. Employing a hybrid metaheuristic RDAVA methodology coupled with Bi-LSTM, the method leverages African Vulture Optimizer and Red Deer Optimizer.OBJECTIVES: The objective of this study is to assess the effectiveness of the suggested model in identifying false material on social media by employing social network analysis tools to combat disinformation.METHODS: Employing the data sets from BuzzFeed, FakeNewsNet, and ISOT, the suggested model is implemented on the MATLAB Platform and acquires high accuracy rates of 97% on FakeNewsNet and 98% on BuzzFeed and ISOT. A comparative study with current models demonstrates its superiority.RESULTS: Outperforming previous models with 98% and 97% accuracy on BuzzFeed/ISOT and FakeNewsNet, respectively, the suggested model shows remarkable performance.CONCLUSION: The proposed strategy shows promise in addressing the problem of false information on social media in the modern day by effectively countering fake news. Its incorporation of social network analysis methods and metaheuristic methodologies makes it a powerful instrument for identifying false news.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"6 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: In the constantly growing Internet of Things (IoT), device security is crucial. As IoT gadgets pervade our lives, detecting unforeseen assaults is crucial to protecting them. Behavioral analysis, machine learning, and collaborative intelligence may be needed to protect against new dangers. This short discusses the need of detecting unexpected IoT attacks and essential security strategies for these interconnected environments.OBJECTIVES: This research uses the BoT-IoT dataset to create an enhanced IoT intrusion detection system. The goals are to optimize a CNN architecture for effective pattern recognition, address imbalanced data, and evaluate model performance using precision, recall, F1-score, and AUC-ROC measures. Improving IoT ecosystem reliability and security against unknown assaults is the ultimate goal.METHODS: The proposed methods use the BoT-IoT dataset to create a comprehensive IoT intrusion detection system. This involves tuning a Convolutional Neural Network (CNN) architecture to improve pattern recognition. Oversampling and class weighting address imbalanced data issues. RESULTS: The comprehensive evaluation of our innovative unknown attack detection method shows promise, suggesting it may be better than existing methods. A high accuracy, precision, recall, and f-measure of 98.23% were attained using an advanced model and feature selection methods. This achievement was achieved by using features designed to identify unknown attacks in the dataset, proving the proposed methodology works.CONCLUSION: This research presents an improved IoT Intrusion Detection System using the BoT-IoT dataset. The optimised Convolutional Neural Network architecture and imbalanced data handling approaches achieved 98.23% accuracy.
{"title":"Hybrid CNN Approach for Unknown Attack Detection in Edge-Based IoT Networks","authors":"R. R. Papalkar, Abrar S Alvi","doi":"10.4108/eetsis.4887","DOIUrl":"https://doi.org/10.4108/eetsis.4887","url":null,"abstract":"INTRODUCTION: In the constantly growing Internet of Things (IoT), device security is crucial. As IoT gadgets pervade our lives, detecting unforeseen assaults is crucial to protecting them. Behavioral analysis, machine learning, and collaborative intelligence may be needed to protect against new dangers. This short discusses the need of detecting unexpected IoT attacks and essential security strategies for these interconnected environments.OBJECTIVES: This research uses the BoT-IoT dataset to create an enhanced IoT intrusion detection system. The goals are to optimize a CNN architecture for effective pattern recognition, address imbalanced data, and evaluate model performance using precision, recall, F1-score, and AUC-ROC measures. Improving IoT ecosystem reliability and security against unknown assaults is the ultimate goal.METHODS: The proposed methods use the BoT-IoT dataset to create a comprehensive IoT intrusion detection system. This involves tuning a Convolutional Neural Network (CNN) architecture to improve pattern recognition. Oversampling and class weighting address imbalanced data issues. RESULTS: The comprehensive evaluation of our innovative unknown attack detection method shows promise, suggesting it may be better than existing methods. A high accuracy, precision, recall, and f-measure of 98.23% were attained using an advanced model and feature selection methods. This achievement was achieved by using features designed to identify unknown attacks in the dataset, proving the proposed methodology works.CONCLUSION: This research presents an improved IoT Intrusion Detection System using the BoT-IoT dataset. The optimised Convolutional Neural Network architecture and imbalanced data handling approaches achieved 98.23% accuracy.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140750557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Rajawat, S. B. Goyal, Manoj Kumar, Saurabh Kumar
INTRODUCTION: The implementation of robust security protocols is imperative in light of the exponential growth of blockchain-based platforms such as Ethereum. The importance of developing more effective strategies to detect and counter potential attacks is growing in tandem with the sophistication of the methods employed by attackers. In this study, we present a novel approach that leverages quantum computing to identify and predict attack vectors on the Ethereum blockchain. OBJECTIVES: The primary objective of this study is to suggest an innovative methodology for enhancing the security of Ethereum by leveraging quantum computing. The purpose of this study is to demonstrate that QRBM and QDN are efficient in identifying and predicting security flaws in blockchain transactions. METHODS: We combined methods from quantum computing with social network research approaches. An enormous dataset containing both genuine Ethereum transactions and a carefully chosen spectrum of malicious activity indicative of popular attack vectors was used to train our model, the QRBM. Thanks to the dataset, the QRBM was able to learn to distinguish between typical and out-of-the-ordinary activities. RESULTS: In comparison to more conventional deep learning models, the QRBM showed substantially better accuracy when it came to identifying transaction behaviours. The model's improved scalability and efficiency were made possible by its quantum nature, which is defined by features like entanglement and superposition. Specifically, the QRBM handled non-informative inputs better and solved problems faster. CONCLUSION: This study paves the way for further investigation into quantum-enhanced cybersecurity measures and highlights the promise of quantum neural networks in strengthening the security of blockchain technology. According to our research, quantum computing has the potential to be an essential tool in creating Ethereum-style blockchain security systems that are more advanced, efficient, and resilient.
{"title":"Quantum Deep Neural Network Based Classification of Attack Vectors on the Ethereum Blockchain","authors":"A. Rajawat, S. B. Goyal, Manoj Kumar, Saurabh Kumar","doi":"10.4108/eetsis.5572","DOIUrl":"https://doi.org/10.4108/eetsis.5572","url":null,"abstract":"INTRODUCTION: The implementation of robust security protocols is imperative in light of the exponential growth of blockchain-based platforms such as Ethereum. The importance of developing more effective strategies to detect and counter potential attacks is growing in tandem with the sophistication of the methods employed by attackers. In this study, we present a novel approach that leverages quantum computing to identify and predict attack vectors on the Ethereum blockchain. \u0000OBJECTIVES: The primary objective of this study is to suggest an innovative methodology for enhancing the security of Ethereum by leveraging quantum computing. The purpose of this study is to demonstrate that QRBM and QDN are efficient in identifying and predicting security flaws in blockchain transactions. \u0000METHODS: We combined methods from quantum computing with social network research approaches. An enormous dataset containing both genuine Ethereum transactions and a carefully chosen spectrum of malicious activity indicative of popular attack vectors was used to train our model, the QRBM. Thanks to the dataset, the QRBM was able to learn to distinguish between typical and out-of-the-ordinary activities. \u0000RESULTS: In comparison to more conventional deep learning models, the QRBM showed substantially better accuracy when it came to identifying transaction behaviours. The model's improved scalability and efficiency were made possible by its quantum nature, which is defined by features like entanglement and superposition. Specifically, the QRBM handled non-informative inputs better and solved problems faster. \u0000CONCLUSION: This study paves the way for further investigation into quantum-enhanced cybersecurity measures and highlights the promise of quantum neural networks in strengthening the security of blockchain technology. According to our research, quantum computing has the potential to be an essential tool in creating Ethereum-style blockchain security systems that are more advanced, efficient, and resilient.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"39 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140376802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ana María Choquehuanca-Sánchez, Keiko Donna Kuzimoto-Saldaña, Jhonatan Rubén Muñoz-Huanca, Dennis Gerardo Requena-Manrique, Rodrigo Antony Trejo-Lozano, Josemaria Isimer Vasquez-Martinez, Edy Guillermo Zenozain-Gara, William Joel Marín Rodriguez
The article discusses emerging technologies in information systems project management. Project management is a modern discipline that began to take shape from 1900 and has evolved and adapted to the needs of society and business. Emerging technologies such as artificial intelligence, blockchain, augmented and virtual reality, and process automation are transforming the way information systems projects are managed. These technologies can be used to analyze large amounts of data, ensure data integrity and security, visualize a project's design and perform virtual testing, and automate tasks to reduce project time and cost. It is important for companies to be aware of these technologies and use them effectively to improve the efficiency and profitability of their projects.
{"title":"Emerging technologies in information systems project management","authors":"Ana María Choquehuanca-Sánchez, Keiko Donna Kuzimoto-Saldaña, Jhonatan Rubén Muñoz-Huanca, Dennis Gerardo Requena-Manrique, Rodrigo Antony Trejo-Lozano, Josemaria Isimer Vasquez-Martinez, Edy Guillermo Zenozain-Gara, William Joel Marín Rodriguez","doi":"10.4108/eetsis.4632","DOIUrl":"https://doi.org/10.4108/eetsis.4632","url":null,"abstract":"The article discusses emerging technologies in information systems project management. Project management is a modern discipline that began to take shape from 1900 and has evolved and adapted to the needs of society and business. Emerging technologies such as artificial intelligence, blockchain, augmented and virtual reality, and process automation are transforming the way information systems projects are managed. These technologies can be used to analyze large amounts of data, ensure data integrity and security, visualize a project's design and perform virtual testing, and automate tasks to reduce project time and cost. It is important for companies to be aware of these technologies and use them effectively to improve the efficiency and profitability of their projects.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140220239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: To explore the integration and innovation path of enterprise marketing data management based on deep learning to adapt to today's competitive business environment. With the continuous development of information technology, enterprises are faced with a large amount of marketing data, and how to efficiently manage and integrate these data has become an essential issue for enterprises to improve their market competitiveness. Deep learning, as a necessary technical means of artificial intelligence, provides enterprises with more intelligent and precise data processing tools.OBJECTIVES: The primary purpose of the study is to solve the problems of marketing data management in traditional enterprises and to achieve better integration and management of data through deep learning technology. Specifically, the goal is to explore the potential of deep learning in improving data processing efficiency and accurately analyzing user behavior and trends. By achieving these goals, organizations can better understand market needs, develop more effective marketing strategies, and stand out in a competitive marketplace.METHODS: This study adopts a comprehensive approach, including a literature review, case study, and empirical analysis of deep learning algorithms. First, the main issues of current enterprise marketing data management and the latest progress in deep learning were understood through an in-depth study of the literature in related fields. Second, several enterprise cases were selected to gain a deeper understanding of the challenges and needs of enterprises in marketing data management through field research and data collection. Finally, a series of deep learning algorithms were designed and implemented to validate their effectiveness in real-world applications and analyze their impact on data integration and innovation paths.RESULTS: The results of the study show that deep learning has significant advantages in enterprise marketing data management. By using deep learning algorithms, enterprises are able to handle large-scale marketing data more efficiently and achieve intelligent data integration and accurate analysis. This not only improves the efficiency of data processing but also provides enterprises with deeper market insights that help develop more targeted marketing strategies.CONCLUSION: The results of the study are of guiding significance for enterprises to realize data-driven marketing decision-making, which provides strong support for enterprises to maintain their competitive advantages in the highly competitive market. Future research can further explore the application of deep learning in different industries and scenarios, as well as how to optimize deep learning algorithms further to meet the changing needs of enterprises.
{"title":"Integration and Innovation Path Analysis of Enterprise Marketing Data Management Based on Deep Learning","authors":"Xiaofeng Wang","doi":"10.4108/eetsis.4799","DOIUrl":"https://doi.org/10.4108/eetsis.4799","url":null,"abstract":"INTRODUCTION: To explore the integration and innovation path of enterprise marketing data management based on deep learning to adapt to today's competitive business environment. With the continuous development of information technology, enterprises are faced with a large amount of marketing data, and how to efficiently manage and integrate these data has become an essential issue for enterprises to improve their market competitiveness. Deep learning, as a necessary technical means of artificial intelligence, provides enterprises with more intelligent and precise data processing tools.OBJECTIVES: The primary purpose of the study is to solve the problems of marketing data management in traditional enterprises and to achieve better integration and management of data through deep learning technology. Specifically, the goal is to explore the potential of deep learning in improving data processing efficiency and accurately analyzing user behavior and trends. By achieving these goals, organizations can better understand market needs, develop more effective marketing strategies, and stand out in a competitive marketplace.METHODS: This study adopts a comprehensive approach, including a literature review, case study, and empirical analysis of deep learning algorithms. First, the main issues of current enterprise marketing data management and the latest progress in deep learning were understood through an in-depth study of the literature in related fields. Second, several enterprise cases were selected to gain a deeper understanding of the challenges and needs of enterprises in marketing data management through field research and data collection. Finally, a series of deep learning algorithms were designed and implemented to validate their effectiveness in real-world applications and analyze their impact on data integration and innovation paths.RESULTS: The results of the study show that deep learning has significant advantages in enterprise marketing data management. By using deep learning algorithms, enterprises are able to handle large-scale marketing data more efficiently and achieve intelligent data integration and accurate analysis. This not only improves the efficiency of data processing but also provides enterprises with deeper market insights that help develop more targeted marketing strategies.CONCLUSION: The results of the study are of guiding significance for enterprises to realize data-driven marketing decision-making, which provides strong support for enterprises to maintain their competitive advantages in the highly competitive market. Future research can further explore the application of deep learning in different industries and scenarios, as well as how to optimize deep learning algorithms further to meet the changing needs of enterprises.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":" 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140213635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diego Armando Castillo-Ñopo, Khateryn Fiorela Loyola-Blanco, Raul Castro-Marca, Gian Davor La Rosa-Gavino, Jherson Giovanny Aragón-Retuerto, Hegel Alvaro Rafael-Sifuentes, William Joel Marín Rodriguez
This article deals with project management in information systems, whose relevance lies in the vital importance of these systems in modern companies. Information systems are essential for decision making and data management in today's interconnected world. Project management, on the other hand, coordinates elements such as scope, resources, costs, schedules and risks to achieve defined objectives. The systems development life cycle (SDLC) structures the process, encompassing phases such as scope definition, planning, execution, monitoring and closure. These phases are integrated with risk management, which identifies, evaluates and mitigates threats and opportunities. Mitigation strategies act before adversity, while contingency planning prepares for the unforeseen. That is why risk management is integrated throughout the project life cycle to anticipate and address challenges. The combination of both aspects is critical in a constantly evolving technology environment. In addition, organizational culture and communication play a critical role. A culture of awareness and accountability, transparency in communication and active stakeholder participation are essential. Training and continuous adaptation allow learning from past experiences and improving practices.
{"title":"Risk management in large-scale information system projects","authors":"Diego Armando Castillo-Ñopo, Khateryn Fiorela Loyola-Blanco, Raul Castro-Marca, Gian Davor La Rosa-Gavino, Jherson Giovanny Aragón-Retuerto, Hegel Alvaro Rafael-Sifuentes, William Joel Marín Rodriguez","doi":"10.4108/eetsis.4608","DOIUrl":"https://doi.org/10.4108/eetsis.4608","url":null,"abstract":"This article deals with project management in information systems, whose relevance lies in the vital importance of these systems in modern companies. Information systems are essential for decision making and data management in today's interconnected world. Project management, on the other hand, coordinates elements such as scope, resources, costs, schedules and risks to achieve defined objectives. The systems development life cycle (SDLC) structures the process, encompassing phases such as scope definition, planning, execution, monitoring and closure. These phases are integrated with risk management, which identifies, evaluates and mitigates threats and opportunities. Mitigation strategies act before adversity, while contingency planning prepares for the unforeseen. That is why risk management is integrated throughout the project life cycle to anticipate and address challenges. The combination of both aspects is critical in a constantly evolving technology environment. In addition, organizational culture and communication play a critical role. A culture of awareness and accountability, transparency in communication and active stakeholder participation are essential. Training and continuous adaptation allow learning from past experiences and improving practices.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140216195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Karla Martell, Rosa Cueto-Orbe, S. Vela-del-Aguila, Julio Iván Torres-Manrique, Karen Reátegui-Villacorta, C. Alejandría-Castro
Abstract: This article reviews the challenges and opportunities facing companies in business management in the era of information. Challenges in managing large volumes of data, emerging trends in cybersecurity, and companies' ability to adapt to the digitalized environment are analyzed. The methodology used includes an exhaustive search of articles in indexed journals and the application of inclusion criteria to select 50 relevant articles. Key findings include obstacles in data management, the increasing sophistication of cyber threats, and business adaptation strategies such as digital transformation and the integration of emerging technologies. In conclusion, the importance of addressing these challenges and leveraging the opportunities presented by technology to enhance business efficiency and competitiveness is highlighted.
{"title":"Business Management in the Information Age: Use of Systems, Data Processing and Scalability for Organizational Efficiency","authors":"Karla Martell, Rosa Cueto-Orbe, S. Vela-del-Aguila, Julio Iván Torres-Manrique, Karen Reátegui-Villacorta, C. Alejandría-Castro","doi":"10.4108/eetsis.5408","DOIUrl":"https://doi.org/10.4108/eetsis.5408","url":null,"abstract":"Abstract: This article reviews the challenges and opportunities facing companies in business management in the era of information. Challenges in managing large volumes of data, emerging trends in cybersecurity, and companies' ability to adapt to the digitalized environment are analyzed. The methodology used includes an exhaustive search of articles in indexed journals and the application of inclusion criteria to select 50 relevant articles. Key findings include obstacles in data management, the increasing sophistication of cyber threats, and business adaptation strategies such as digital transformation and the integration of emerging technologies. In conclusion, the importance of addressing these challenges and leveraging the opportunities presented by technology to enhance business efficiency and competitiveness is highlighted.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":" 61","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: Vulnerability detection is crucial for preventing severe security incidents like hacker attacks, data breaches, and network paralysis. Traditional methods, however, face challenges such as low efficiency and insufficient detail in identifying code vulnerabilities. OBJECTIVES: This paper introduces E-GVD, an advanced method for source code vulnerability detection, aiming to address the limitations of existing methods. The objective is to enhance the accuracy of function-level vulnerability detection and provide detailed, understandable insights into the vulnerabilities. METHODS: E-GVD combines Graph Neural Networks (GNNs), which are adept at handling graph-structured data, with residual connections and advanced Programming Language (PL) pre-trained models. RESULTS: Experiments conducted on the real-world vulnerability dataset CodeXGLUE show that E-GVD significantly outperforms existing baseline methods in detecting vulnerabilities. It achieves a maximum accuracy gain of 4.98%, indicating its effectiveness over traditional methods. CONCLUSION: E-GVD not only improves the accuracy of vulnerability detection but also contributes by providing fine-grained explanations. These explanations are made possible through an interpretable Machine Learning (ML) model, which aids developers in quickly and efficiently repairing vulnerabilities, thereby enhancing overall software security.
{"title":"E-GVD: Efficient Software Vulnerability Detection Techniques Based on Graph Neural Network","authors":"Haiye Wang, Zhiguo Qu, Le Sun","doi":"10.4108/eetsis.5056","DOIUrl":"https://doi.org/10.4108/eetsis.5056","url":null,"abstract":"INTRODUCTION: Vulnerability detection is crucial for preventing severe security incidents like hacker attacks, data breaches, and network paralysis. Traditional methods, however, face challenges such as low efficiency and insufficient detail in identifying code vulnerabilities. OBJECTIVES: This paper introduces E-GVD, an advanced method for source code vulnerability detection, aiming to address the limitations of existing methods. The objective is to enhance the accuracy of function-level vulnerability detection and provide detailed, understandable insights into the vulnerabilities. METHODS: E-GVD combines Graph Neural Networks (GNNs), which are adept at handling graph-structured data, with residual connections and advanced Programming Language (PL) pre-trained models. RESULTS: Experiments conducted on the real-world vulnerability dataset CodeXGLUE show that E-GVD significantly outperforms existing baseline methods in detecting vulnerabilities. It achieves a maximum accuracy gain of 4.98%, indicating its effectiveness over traditional methods. CONCLUSION: E-GVD not only improves the accuracy of vulnerability detection but also contributes by providing fine-grained explanations. These explanations are made possible through an interpretable Machine Learning (ML) model, which aids developers in quickly and efficiently repairing vulnerabilities, thereby enhancing overall software security.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":" 70","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
INTRODUCTION: The satellite's physical and technical capabilities limit high spectral and spatial resolution image acquisition. In Remote Sensing (RS), when high spatial and spectral resolution data is essential for specific Geographic Information System (GIS) applications, Pan Sharpening (PanS) becomes imperative in obtaining such data. OBJECTIVES: Study aims to enhance the spatial resolution of the multispectral Landsat-8 (L8) images using a synthetic panchromatic band generated by averaging four fine-resolution bands in the Sentinel-2 (S2) images. METHODS: Evaluation of the proposed multi-satellite PanS approach, three different PanS techniques, Smoothed Filter Intensity Modulation (SFIM), Gram-Schmidt (GS), and High Pass Filter Additive (HPFA) are used for two different study areas. The techniques' effectiveness was evaluated using well-known Image Quality Assessment Metrics (IQAM) such as Root Mean Square Error (RMSE), Correlation Coefficient (CC), Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), and Relative Average Spectral Error (RASE). This study leveraged the GEE platform for datasets and implementation. RESULTS: The promising values were provided by the GS technique, followed by the SFIM technique, whereas the HPFA technique produced the lowest quantitative result. CONCLUSION: In this study, the spectral bands of the MS image’s performance show apparent variation with respect to that of the different PanS techniques used.
{"title":"Image Quality Assessment of Multi-Satellite Pan-Sharpening Approach: A Case Study using Sentinel-2 Synthetic Panchromatic Image and Landsat-8","authors":"Greetta Pinheiro, Ishfaq Hussain Rather, Aditya Raj, S. Minz, Sushil Kumar","doi":"10.4108/eetsis.5496","DOIUrl":"https://doi.org/10.4108/eetsis.5496","url":null,"abstract":"INTRODUCTION: The satellite's physical and technical capabilities limit high spectral and spatial resolution image acquisition. In Remote Sensing (RS), when high spatial and spectral resolution data is essential for specific Geographic Information System (GIS) applications, Pan Sharpening (PanS) becomes imperative in obtaining such data. \u0000OBJECTIVES: Study aims to enhance the spatial resolution of the multispectral Landsat-8 (L8) images using a synthetic panchromatic band generated by averaging four fine-resolution bands in the Sentinel-2 (S2) images. \u0000METHODS: Evaluation of the proposed multi-satellite PanS approach, three different PanS techniques, Smoothed Filter Intensity Modulation (SFIM), Gram-Schmidt (GS), and High Pass Filter Additive (HPFA) are used for two different study areas. The techniques' effectiveness was evaluated using well-known Image Quality Assessment Metrics (IQAM) such as Root Mean Square Error (RMSE), Correlation Coefficient (CC), Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS), and Relative Average Spectral Error (RASE). This study leveraged the GEE platform for datasets and implementation. \u0000RESULTS: The promising values were provided by the GS technique, followed by the SFIM technique, whereas the HPFA technique produced the lowest quantitative result. \u0000CONCLUSION: In this study, the spectral bands of the MS image’s performance show apparent variation with respect to that of the different PanS techniques used.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140221707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Web-based augmented reality (AR) systems have many use cases and opportunities in Product Visualisation, Education and Training, Advertising and Marketing, Navigation and Wayfinding, Virtual Try-On, Interactive Storey Telling, Museums and Cultural Heritage, Training and Simulation, Gamification and more. As such, this research paper, A Web-Based Augmented Reality System, will explore these technologies and their use cases in the form of a literature review and several examples utilising the likes of Vectary, Blippar, Model Viewer and World Cast AR. The purpose of which, is to demonstrate a level of understanding of these virtual technologies, to develop them and to develop their future with practical use cases.
基于网络的增强现实（AR）系统在产品可视化、教育和培训、广告和营销、导航和寻路、虚拟试穿、交互式楼层介绍、博物馆和文化遗产、培训和模拟、游戏化等领域有许多用例和机会。因此，这篇题为 "基于网络的增强现实系统 "的研究论文将通过文献综述和利用 Vectary、Bliippar、Model Viewer 和 World Cast AR 等软件的几个示例，探讨这些技术及其使用案例。其目的是展示对这些虚拟技术的理解程度，开发这些技术，并通过实际应用案例发展其未来。
{"title":"A Web-Based Augmented Reality System","authors":"Kevin Francis McNally, Hoshang Koviland","doi":"10.4108/eetsis.5481","DOIUrl":"https://doi.org/10.4108/eetsis.5481","url":null,"abstract":"Web-based augmented reality (AR) systems have many use cases and opportunities in Product Visualisation, Education and Training, Advertising and Marketing, Navigation and Wayfinding, Virtual Try-On, Interactive Storey Telling, Museums and Cultural Heritage, Training and Simulation, Gamification and more. As such, this research paper, A Web-Based Augmented Reality System, will explore these technologies and their use cases in the form of a literature review and several examples utilising the likes of Vectary, Blippar, Model Viewer and World Cast AR. The purpose of which, is to demonstrate a level of understanding of these virtual technologies, to develop them and to develop their future with practical use cases.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"28 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140227147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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