Hameed Khan, Kamal K. Kushwah, Jitendra S Thakur, G. Soni, Abhishek Tripathi
Mobile Ad Hoc Networks (MANETs) pose a dynamically organized wireless network, posing a challenge to establishing quality of service (QoS) due to limitations in bandwidth and the ever-changing network topology. These networks are created by assembling nodes systematically, lacking a central infrastructure, and dynamically linking devices such as mobile phones and tablets. Nodes employ diverse methods for service delivery, all while giving priority to network performance. The effectiveness of protocols is crucial in determining the most efficient paths between source and destination nodes, ensuring the timely delivery of messages. Collaborative agreements with MANETs improve accessibility, allow for partial packet delivery and manage network load, ultimately minimizing delays and contributing to exceptional carrier performance. This article conducts a comparative analysis of simulation parameters for AODV, DSR, and MP-OLSR protocols to explore QoS limitations associated with different routing protocols. The study primarily focuses on evaluating various quality metrics for service improvement, assessing protocol performance. Simulation results underscore the DSR protocol's 80% superior throughput compared to AODV and MP-OLSR. However, in terms of delay and packet delivery ratio, the hybrid protocol outperforms both AODV and DSR protocols. These findings provide a distinct perspective for testing the compliance services of MANETs.
移动 Ad Hoc 网络（MANET）是一种动态组织的无线网络，由于带宽的限制和不断变化的网络拓扑结构，对建立服务质量（QoS）提出了挑战。这些网络是通过系统地组装节点而创建的，缺乏中央基础设施，并动态地连接移动电话和平板电脑等设备。节点采用多种方法提供服务，同时优先考虑网络性能。协议的有效性对于确定源节点和目的节点之间最有效的路径、确保信息的及时传递至关重要。与城域网的协作协议可提高可访问性，允许部分数据包传送，并管理网络负载，最终最大限度地减少延迟，为实现卓越的载波性能做出贡献。本文对 AODV、DSR 和 MP-OLSR 协议的仿真参数进行了比较分析，以探讨与不同路由协议相关的 QoS 限制。研究的主要重点是评估用于改善服务的各种质量指标，评估协议性能。仿真结果表明，与 AODV 和 MP-OLSR 相比，DSR 协议的吞吐量高出 80%。不过，在延迟和数据包传送率方面，混合协议的表现优于 AODV 和 DSR 协议。这些发现为测试城域网的合规性服务提供了一个独特的视角。
{"title":"Improving Mobile Ad hoc Networks through an investigation of AODV, DSR, and MP-OLSR Routing Protocols","authors":"Hameed Khan, Kamal K. Kushwah, Jitendra S Thakur, G. Soni, Abhishek Tripathi","doi":"10.4108/eetsis.5686","DOIUrl":"https://doi.org/10.4108/eetsis.5686","url":null,"abstract":" \u0000Mobile Ad Hoc Networks (MANETs) pose a dynamically organized wireless network, posing a challenge to establishing quality of service (QoS) due to limitations in bandwidth and the ever-changing network topology. These networks are created by assembling nodes systematically, lacking a central infrastructure, and dynamically linking devices such as mobile phones and tablets. Nodes employ diverse methods for service delivery, all while giving priority to network performance. The effectiveness of protocols is crucial in determining the most efficient paths between source and destination nodes, ensuring the timely delivery of messages. Collaborative agreements with MANETs improve accessibility, allow for partial packet delivery and manage network load, ultimately minimizing delays and contributing to exceptional carrier performance. This article conducts a comparative analysis of simulation parameters for AODV, DSR, and MP-OLSR protocols to explore QoS limitations associated with different routing protocols. The study primarily focuses on evaluating various quality metrics for service improvement, assessing protocol performance. Simulation results underscore the DSR protocol's 80% superior throughput compared to AODV and MP-OLSR. However, in terms of delay and packet delivery ratio, the hybrid protocol outperforms both AODV and DSR protocols. These findings provide a distinct perspective for testing the compliance services of MANETs.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"122 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731578","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 paper addresses the integration of intelligent technology in university physical education, highlighting the need for improved analysis methods for sports equipment image recognition apps to enhance teaching quality.OBJECTIVES: The study aims to develop a more accurate and efficient APP use analysis method for sports equipment image recognition, utilizing intelligent optimization algorithms and kernel limit learning machines.METHODS: The proposed method involves constructing an APP usage effect analysis index system, improving kernel limit learning machines through talent mining algorithms, and validating the model using user behavior data. The method integrates a talent mining algorithm to enhance the kernel limit learning machine (KELM). This integration aims to refine the learning machine’s ability to accurately analyze the large datasets generated by the APP's use, optimizing the parameters to improve prediction accuracy and processing speed.RESULTS: Preliminary tests on the sports equipment image intelligent recognition response APP demonstrate improved accuracy and efficiency in analyzing the APP's usage effects in physical education settings. The study compares the performance of the TDA-KELM algorithm with other algorithms like ELM, KELM, GWO-KELM, SOA-KELM, and AOA-KELM. The TDA-KELM algorithm showed the smallest relative error of 0.025 and a minimal time of 0.0025, indicating higher accuracy and efficiency. The analysis highlighted that the TDA-KELM algorithm outperformed others in analyzing the usage effects of sports equipment image recognition apps, with lower errors and faster processing times.CONCLUSION: The study successfully develops an enhanced APP use analysis method, showcasing potential for more accurate and real-time analysis in the application of sports equipment image recognition in physical education.
{"title":"Application of Sports Equipment Image Intelligent Recognition Response APP in Sports Training and Teaching","authors":"Yang Ju","doi":"10.4108/eetsis.5470","DOIUrl":"https://doi.org/10.4108/eetsis.5470","url":null,"abstract":"INTRODUCTION: The paper addresses the integration of intelligent technology in university physical education, highlighting the need for improved analysis methods for sports equipment image recognition apps to enhance teaching quality.OBJECTIVES: The study aims to develop a more accurate and efficient APP use analysis method for sports equipment image recognition, utilizing intelligent optimization algorithms and kernel limit learning machines.METHODS: The proposed method involves constructing an APP usage effect analysis index system, improving kernel limit learning machines through talent mining algorithms, and validating the model using user behavior data. The method integrates a talent mining algorithm to enhance the kernel limit learning machine (KELM). This integration aims to refine the learning machine’s ability to accurately analyze the large datasets generated by the APP's use, optimizing the parameters to improve prediction accuracy and processing speed.RESULTS: Preliminary tests on the sports equipment image intelligent recognition response APP demonstrate improved accuracy and efficiency in analyzing the APP's usage effects in physical education settings. The study compares the performance of the TDA-KELM algorithm with other algorithms like ELM, KELM, GWO-KELM, SOA-KELM, and AOA-KELM. The TDA-KELM algorithm showed the smallest relative error of 0.025 and a minimal time of 0.0025, indicating higher accuracy and efficiency. The analysis highlighted that the TDA-KELM algorithm outperformed others in analyzing the usage effects of sports equipment image recognition apps, with lower errors and faster processing times.CONCLUSION: The study successfully develops an enhanced APP use analysis method, showcasing potential for more accurate and real-time analysis in the application of sports equipment image recognition in physical education. ","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"28 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140741196","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 today's information age, user interface design and interaction experience are crucial to the success of online platforms.OBJECTIVES: Through in-depth analysis of the user interface design features and user interaction experience of the "Duolingo" platform, this study reveals the potential correlation between them and proposes effective improvement methods to enhance user satisfaction and efficiency.METHODS: Interaction design principles were adopted to guide the improvement and optimization of the user interface. These principles include usability, consistency, and feedback to improve overall user satisfaction with the platform by actively considering user behavior and needs in the design. At the same time, specific mathematical models and equations are used to quantitatively analyze the efficiency and smoothness of the user interaction process, providing designers with more precise directions for improvement.RESULTS: Optimized user interface design and interaction experience can significantly improve user satisfaction and usage efficiency. Users operate the platform more smoothly, which provides useful reference and guidance for the design and development of e-learning platforms.CONCLUSION: Through in-depth analysis of the case of the "Duolingo" platform and the introduction of user experience evaluation methods and interaction design principles, this study has come up with a series of effective improvement measures and verified their effectiveness through experiments. It has certain theoretical and practical significance for improving the user experience of online learning platforms and promoting the design and development of Internet products.
{"title":"Research on User Interface Design and Interaction Experience: A Case Study from \"Duolingo\" Platform","authors":"Yan Qi, Rui Xu","doi":"10.4108/eetsis.5461","DOIUrl":"https://doi.org/10.4108/eetsis.5461","url":null,"abstract":"INTRODUCTION: In today's information age, user interface design and interaction experience are crucial to the success of online platforms.OBJECTIVES: Through in-depth analysis of the user interface design features and user interaction experience of the \"Duolingo\" platform, this study reveals the potential correlation between them and proposes effective improvement methods to enhance user satisfaction and efficiency.METHODS: Interaction design principles were adopted to guide the improvement and optimization of the user interface. These principles include usability, consistency, and feedback to improve overall user satisfaction with the platform by actively considering user behavior and needs in the design. At the same time, specific mathematical models and equations are used to quantitatively analyze the efficiency and smoothness of the user interaction process, providing designers with more precise directions for improvement.RESULTS: Optimized user interface design and interaction experience can significantly improve user satisfaction and usage efficiency. Users operate the platform more smoothly, which provides useful reference and guidance for the design and development of e-learning platforms.CONCLUSION: Through in-depth analysis of the case of the \"Duolingo\" platform and the introduction of user experience evaluation methods and interaction design principles, this study has come up with a series of effective improvement measures and verified their effectiveness through experiments. It has certain theoretical and practical significance for improving the user experience of online learning platforms and promoting the design and development of Internet products.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140746298","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: With the rise of social media platforms, the prevalence of truculent posts has become a major concern. These posts, which exhibit anger, aggression, or rudeness, not only foster a hostile environment but also have the potential to stir up harm and violence. OBJECTIVES: It is essential to create efficient algorithms for detecting virulent posts so that they can recognise and delete such content from social media sites automatically. In order to improve accuracy and efficiency, this study evaluates the state-of-the-art in truculent post detection techniques and suggests a unique method that combines deep learning and natural language processing. The major goal of the proposed methodology is to successfully regulate hostile social media posts by keeping an eye on them. METHODS: In order to effectively identify the class labels and create a deep-learning method, we concentrated on comprehending the negation words, sarcasm, and irony using the LSTM model. We used multilingual BERT to produce precise word embedding and deliver semantic data. The phrases were also thoroughly tokenized, taking into consideration the Hindi language, thanks to the assistance of the Indic NLP library. RESULTS: The F1 scores for the various classes are given in the "Proposed approach” as follows: 84.22 for non-hostile, 49.26 for hostile, 68.69 for hatred, 49.81 for fake, and 39.92 for offensive CONCLUSION: We focused on understanding the negation words, sarcasm and irony using the LSTM model, to classify the class labels accurately and build a deep-learning strategy.
{"title":"Truculent Post Analysis for Hindi Text","authors":"Mitali Agarwal, Poorvi Sahu, Nisha Singh, Jasleen, Puneet Sinha, Rahul Kumar Singh","doi":"10.4108/eetsis.5641","DOIUrl":"https://doi.org/10.4108/eetsis.5641","url":null,"abstract":"INTRODUCTION: With the rise of social media platforms, the prevalence of truculent posts has become a major concern. These posts, which exhibit anger, aggression, or rudeness, not only foster a hostile environment but also have the potential to stir up harm and violence. \u0000OBJECTIVES: It is essential to create efficient algorithms for detecting virulent posts so that they can recognise and delete such content from social media sites automatically. In order to improve accuracy and efficiency, this study evaluates the state-of-the-art in truculent post detection techniques and suggests a unique method that combines deep learning and natural language processing. The major goal of the proposed methodology is to successfully regulate hostile social media posts by keeping an eye on them. \u0000METHODS: In order to effectively identify the class labels and create a deep-learning method, we concentrated on comprehending the negation words, sarcasm, and irony using the LSTM model. We used multilingual BERT to produce precise word embedding and deliver semantic data. The phrases were also thoroughly tokenized, taking into consideration the Hindi language, thanks to the assistance of the Indic NLP library. \u0000RESULTS: The F1 scores for the various classes are given in the \"Proposed approach” as follows: 84.22 for non-hostile, 49.26 for hostile, 68.69 for hatred, 49.81 for fake, and 39.92 for offensive \u0000CONCLUSION: We focused on understanding the negation words, sarcasm and irony using the LSTM model, to classify the class labels accurately and build a deep-learning strategy.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"14 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140745681","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: With the rapid development of science and technology, intelligent painting exhibitions have gradually attracted people's attention with their unique forms. This study aims to create an interactive art space using Internet of Things (IoT) technology to provide audiences with a more prosperous and deeper art experience. OBJECTIVES: The primary purpose of this study is to explore how to use IoT technology to transform a painting exhibition into a digital space that can interact with the audience. By fusing art and technology, the researchers aim to promote innovation in traditional art presentations and stimulate the audience's freshness and interest in art.METHODS: In the Smart Painting exhibition, the researchers used advanced Internet of Things (IoT) technology to incorporate the audience's movements, emotions, and feedback into the artworks through sensors, wearable devices, and cloud computing. The digital devices in the exhibition space could sense the audience's presence and generate and adjust the art content in real-time according to their movements or emotional state, creating a unique display that interacted with the audience. RESULTS: After implementing the Smart Painting exhibition, the audience's sense of participation and immersion in the art display was significantly increased. Through IoT technology, viewers can interact with the artwork in real-time and feel a more personalized art experience. The digitized exhibition space provided the audience a new level of perception, deepening their understanding and appreciation of the artworks. CONCLUSION: This study demonstrates the feasibility of using IoT technology to create interactive art spaces and shows that this innovation can inject new vitality into traditional painting exhibitions. Through digitalization, the interactivity of the art space is enhanced, providing the audience with a more profound art experience. This approach provides artists with new possibilities for creativity and opens up a fresh vision of participatory art for the audience. The Smart Painting Exhibition is expected to become a new model for integrating art and technology, pushing the art world towards a more innovative and open future.
{"title":"Smart Painting Exhibitions: Utilizing Internet of Things Technology Creating Interactive Art Spaces","authors":"Xiaoyan Peng, Chuang Chen","doi":"10.4108/eetsis.5375","DOIUrl":"https://doi.org/10.4108/eetsis.5375","url":null,"abstract":"INTRODUCTION: With the rapid development of science and technology, intelligent painting exhibitions have gradually attracted people's attention with their unique forms. This study aims to create an interactive art space using Internet of Things (IoT) technology to provide audiences with a more prosperous and deeper art experience. OBJECTIVES: The primary purpose of this study is to explore how to use IoT technology to transform a painting exhibition into a digital space that can interact with the audience. By fusing art and technology, the researchers aim to promote innovation in traditional art presentations and stimulate the audience's freshness and interest in art.METHODS: In the Smart Painting exhibition, the researchers used advanced Internet of Things (IoT) technology to incorporate the audience's movements, emotions, and feedback into the artworks through sensors, wearable devices, and cloud computing. The digital devices in the exhibition space could sense the audience's presence and generate and adjust the art content in real-time according to their movements or emotional state, creating a unique display that interacted with the audience. RESULTS: After implementing the Smart Painting exhibition, the audience's sense of participation and immersion in the art display was significantly increased. Through IoT technology, viewers can interact with the artwork in real-time and feel a more personalized art experience. The digitized exhibition space provided the audience a new level of perception, deepening their understanding and appreciation of the artworks. CONCLUSION: This study demonstrates the feasibility of using IoT technology to create interactive art spaces and shows that this innovation can inject new vitality into traditional painting exhibitions. Through digitalization, the interactivity of the art space is enhanced, providing the audience with a more profound art experience. This approach provides artists with new possibilities for creativity and opens up a fresh vision of participatory art for the audience. The Smart Painting Exhibition is expected to become a new model for integrating art and technology, pushing the art world towards a more innovative and open future. ","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"10 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140745818","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}
Kashish Bhurani, Aashna Dogra, Prerna Agarwal, P. Shrivastava, Thipendra P Singh, Mohit Bhandwal
INTRODUCTION: Data integrity protection has become a significant priority for both consumers and organizations as cloud storage alternatives have multiplied since they provide scalable solutions for individuals and organizations alike. Traditional cloud storage systems need to find new ways to increase security because they are prone to data modification and unauthorized access thus causing data breaches. OBJECTIVES: The main objective of this study is to review usage of smart contracts and blockchain technology to ensure data integrity in cloud storage. METHODS: . Case studies, performance evaluations, and a thorough literature review are all used to demonstrate the effectiveness of the suggested system. RESULTS: This research has unveiled a revolutionary approach that capitalizes on the fusion of smart contracts and cloud storage, fortified by blockchain technology. CONCLUSION: This theoretical analysis demonstrate that smart contracts offer a dependable and scalable mechanism for maintaining data integrity in cloud storage, opening up a promising area for further research and practical application.
{"title":"Smart Contracts for Ensuring Data Integrity in Cloud Storage with Blockchain","authors":"Kashish Bhurani, Aashna Dogra, Prerna Agarwal, P. Shrivastava, Thipendra P Singh, Mohit Bhandwal","doi":"10.4108/eetsis.5633","DOIUrl":"https://doi.org/10.4108/eetsis.5633","url":null,"abstract":"INTRODUCTION: Data integrity protection has become a significant priority for both consumers and organizations as cloud storage alternatives have multiplied since they provide scalable solutions for individuals and organizations alike. Traditional cloud storage systems need to find new ways to increase security because they are prone to data modification and unauthorized access thus causing data breaches. \u0000OBJECTIVES: The main objective of this study is to review usage of smart contracts and blockchain technology to ensure data integrity in cloud storage. \u0000METHODS: . Case studies, performance evaluations, and a thorough literature review are all used to demonstrate the effectiveness of the suggested system. \u0000RESULTS: This research has unveiled a revolutionary approach that capitalizes on the fusion of smart contracts and cloud storage, fortified by blockchain technology. \u0000CONCLUSION: This theoretical analysis demonstrate that smart contracts offer a dependable and scalable mechanism for maintaining data integrity in cloud storage, opening up a promising area for further research and practical application.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"21 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140741322","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 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}
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