Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053492
C. Raj, W. R. Babu, V. Gomathi, S. Bharath, G. Jagatap, S. S. Kumar
The need for drinking water is very essential in today's world. The government has been adopting a variety of measures to save drinking water and spend it economically. It is everyone's duty to save water and use it sparingly. Water is wasted due to breakage of the water pipe, excessive use of water, measurement in a faulty instrument and so on. The leakage of water from the pipeline and the usage of water is intelligently detected and measured by PLC – Remote I/O Module based system. Each house or utility is installed with intelligent flow meters and the inputs or readings are collected from the individual meters by water distribution board via the iOT. Faults and other abnormal conditions are identified instantaneously, and the health of the distribution system is monitored by SCADA.
{"title":"Remote Intelligent System for Monitoring and Control of Water Distribution Network Using Remote I/O Module for Smart City","authors":"C. Raj, W. R. Babu, V. Gomathi, S. Bharath, G. Jagatap, S. S. Kumar","doi":"10.1109/IDCIoT56793.2023.10053492","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053492","url":null,"abstract":"The need for drinking water is very essential in today's world. The government has been adopting a variety of measures to save drinking water and spend it economically. It is everyone's duty to save water and use it sparingly. Water is wasted due to breakage of the water pipe, excessive use of water, measurement in a faulty instrument and so on. The leakage of water from the pipeline and the usage of water is intelligently detected and measured by PLC – Remote I/O Module based system. Each house or utility is installed with intelligent flow meters and the inputs or readings are collected from the individual meters by water distribution board via the iOT. Faults and other abnormal conditions are identified instantaneously, and the health of the distribution system is monitored by SCADA.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82251301","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}
Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053447
S. Mishra, Rajendra Kumar Dwivedi
Groups play a crucial role in affecting decisions of individuals who are part of the group. When it comes to social networks the group here may be small with some 10-15 members or very big contacting more than 100 members. Thus, there is high possibility of individuals belonging to one or more groups in social networks. It thus becomes important to activate influential members of a group to ensure maximum information propagation. This work proposes a community-based seed selection algorithm. The communities are first identified node embedding which performs graph clustering. After which proportionate distribution of seed nodes is carried out to ensure fair selection. Mapping node features to lower dimensional space and similar nodes getting placed closer to each other proves a better technique for community detection and is also expandable if new nodes get introduced in the network.
{"title":"Leveraging Deep Learning to Spot Communities for Influence Maximization in Social Networks","authors":"S. Mishra, Rajendra Kumar Dwivedi","doi":"10.1109/IDCIoT56793.2023.10053447","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053447","url":null,"abstract":"Groups play a crucial role in affecting decisions of individuals who are part of the group. When it comes to social networks the group here may be small with some 10-15 members or very big contacting more than 100 members. Thus, there is high possibility of individuals belonging to one or more groups in social networks. It thus becomes important to activate influential members of a group to ensure maximum information propagation. This work proposes a community-based seed selection algorithm. The communities are first identified node embedding which performs graph clustering. After which proportionate distribution of seed nodes is carried out to ensure fair selection. Mapping node features to lower dimensional space and similar nodes getting placed closer to each other proves a better technique for community detection and is also expandable if new nodes get introduced in the network.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81876423","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}
Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053441
K. Thilak, S. Ashwinkarthik, M. Varatharaj, V. Muralidharan, M. Vinosh, Yayati Shinde
Autonomous Electric Vehicles (AEVs) use the next generation batteries and other upgraded technologies to transform passengers from a boarding point to their destination more efficiently, without the need for drivers and fossil fuel-driven internal combustion engines. In today's electric vehicles, the commonly used batteries are lithium-ion batteries. This paper presents the study of Battery Monitoring Systems (BMS) in AEVs. The aim of the monitoring system in a battery is to improve the efficiency of electric vehicles. Therefore, Very Large-Scale Integration (VLSI) plays a role in AEVs due to the growth in efficiency of the BMS. To overcome the issues in BMS, some modules are also developed in the battery monitoring system to improve the state of technology.
{"title":"An Investigation on Battery Management System for Autonomous Electric Vehicles","authors":"K. Thilak, S. Ashwinkarthik, M. Varatharaj, V. Muralidharan, M. Vinosh, Yayati Shinde","doi":"10.1109/IDCIoT56793.2023.10053441","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053441","url":null,"abstract":"Autonomous Electric Vehicles (AEVs) use the next generation batteries and other upgraded technologies to transform passengers from a boarding point to their destination more efficiently, without the need for drivers and fossil fuel-driven internal combustion engines. In today's electric vehicles, the commonly used batteries are lithium-ion batteries. This paper presents the study of Battery Monitoring Systems (BMS) in AEVs. The aim of the monitoring system in a battery is to improve the efficiency of electric vehicles. Therefore, Very Large-Scale Integration (VLSI) plays a role in AEVs due to the growth in efficiency of the BMS. To overcome the issues in BMS, some modules are also developed in the battery monitoring system to improve the state of technology.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83566554","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}
Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053423
Manicka Raja M, Mokesh Anand Kumar G, Sanchita T, S. A
Artificial Intelligence is growing to be the future of today’s world. It builds a path for communicable applications and an easier understanding for the system and the user. By adopting the advantages of artificial intelligence, our project focuses on estimating and delivering the right resources for a college student. The web app, Edu. Social is created to determine and seek deep skills of a student and provide them with the right resources to heighten their knowledge. The web app would analyze the right resources based on the initial and prime inputs given by the students. The resources comprise placement opportunities, certification suggestions, workshops, seminars, activities and many more that are happening around the respective student. The web app does not just focus on the academic perspective but also the inner passion of a student. Therefore, this platform would be a great benefit for a student to gain experience and to see the viable resources put forth according to their field of interest and choice. The main focus of this Web Application is to keep students up to date by notifying them about new technologies available and where to focus on by providing frequent assessments to know where they stand.
{"title":"AI-Powered Mobility Educational Application for Enhancing Student Learning","authors":"Manicka Raja M, Mokesh Anand Kumar G, Sanchita T, S. A","doi":"10.1109/IDCIoT56793.2023.10053423","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053423","url":null,"abstract":"Artificial Intelligence is growing to be the future of today’s world. It builds a path for communicable applications and an easier understanding for the system and the user. By adopting the advantages of artificial intelligence, our project focuses on estimating and delivering the right resources for a college student. The web app, Edu. Social is created to determine and seek deep skills of a student and provide them with the right resources to heighten their knowledge. The web app would analyze the right resources based on the initial and prime inputs given by the students. The resources comprise placement opportunities, certification suggestions, workshops, seminars, activities and many more that are happening around the respective student. The web app does not just focus on the academic perspective but also the inner passion of a student. Therefore, this platform would be a great benefit for a student to gain experience and to see the viable resources put forth according to their field of interest and choice. The main focus of this Web Application is to keep students up to date by notifying them about new technologies available and where to focus on by providing frequent assessments to know where they stand.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87114562","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}
Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053542
Chen Fuxiong
A novel VR intelligent guiding system with image processing and programming methods is designed in this study. The fundamental goal of VR technology is to achieve realistic experiences and also natural technology-based human-computer interaction. human-computer interaction based on the natural technologies, so a system that achieves or partially achieves such a goal can be referred to as virtual reality systems. In the designed system, the novel contains the image processing and programming methods. (1) The novel image virtualization method is designed to provide the novel idea of the image pre-processing. (2) The suitable programming framework is designed to make the system efficient. Through the experiment on the UI and the systematic implementation, the performance is tested.
{"title":"A Novel Virtual Reality (VR) based Intelligent Guiding System","authors":"Chen Fuxiong","doi":"10.1109/IDCIoT56793.2023.10053542","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053542","url":null,"abstract":"A novel VR intelligent guiding system with image processing and programming methods is designed in this study. The fundamental goal of VR technology is to achieve realistic experiences and also natural technology-based human-computer interaction. human-computer interaction based on the natural technologies, so a system that achieves or partially achieves such a goal can be referred to as virtual reality systems. In the designed system, the novel contains the image processing and programming methods. (1) The novel image virtualization method is designed to provide the novel idea of the image pre-processing. (2) The suitable programming framework is designed to make the system efficient. Through the experiment on the UI and the systematic implementation, the performance is tested.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82619176","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}
Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053422
Upendra Shetty D R, Anusha Patil, Mohana Mohana
One of most frequent cybersecurity vulnerabilities is malicious websites or malicious uniform resource location (URL). Each year, people are losing billions of rupees by hosting gratuitous material (spam, malware, unsuitable adverts, spoofing etc.) and tempting naïve visitors to fall for scams. Email, adverts, web searches, or connections from other websites can all encourage people to visit these websites. Users click on the malicious URL in each instance, a trustworthy system that can categorize and identify dangerous URLs is needed due to rise in phishing, spamming, and malware occurrences. Due to the enormous amount of data, changing patterns and technologies, as well as the complex relationships between characteristics, non-availability of training data, non-linearity and the presence of outliers made classification challenging. In the proposed work, malicious URLs are detected for various applications. Dataset has been categorized into four types i.e., Phishing, Benign, Defacement and Malware. Totally 6,51,191 URLs have been used for proposed implementation. Three machine learning algorithms such as random forest, LightGBM and XGBoost were implemented to detect and classify malicious URLs.
{"title":"Malicious URL Detection and Classification Analysis using Machine Learning Models","authors":"Upendra Shetty D R, Anusha Patil, Mohana Mohana","doi":"10.1109/IDCIoT56793.2023.10053422","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053422","url":null,"abstract":"One of most frequent cybersecurity vulnerabilities is malicious websites or malicious uniform resource location (URL). Each year, people are losing billions of rupees by hosting gratuitous material (spam, malware, unsuitable adverts, spoofing etc.) and tempting naïve visitors to fall for scams. Email, adverts, web searches, or connections from other websites can all encourage people to visit these websites. Users click on the malicious URL in each instance, a trustworthy system that can categorize and identify dangerous URLs is needed due to rise in phishing, spamming, and malware occurrences. Due to the enormous amount of data, changing patterns and technologies, as well as the complex relationships between characteristics, non-availability of training data, non-linearity and the presence of outliers made classification challenging. In the proposed work, malicious URLs are detected for various applications. Dataset has been categorized into four types i.e., Phishing, Benign, Defacement and Malware. Totally 6,51,191 URLs have been used for proposed implementation. Three machine learning algorithms such as random forest, LightGBM and XGBoost were implemented to detect and classify malicious URLs.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72994949","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}
Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053463
Mukesh Chinta, Anagani Likhita, Yamini Aravapalli
Heavy rainfalls leading to floods in cities and villages is a common sight in our country. These situations lead to destruction of roadways and bridges, and often public infrastructure as an aftermath. Inspection of such facilities to assess the damage and identify any potential vulnerability is a tedious process. Some of the cracks/crevices might not be even visible to the naked eye. An automated system which can detect cracks saves money, time and even lives. This will help us improve road safety which is the reason for major accidents. The proposed work uses machine learning concepts to implement such a system which automatically detects the cracks on the roads, bridges and will send an alert to the concerned authorities there by potentially reducing the risk for disaster occurrence. Convolutional Neural Networks (CNN) can be used for the identification of cracks. By integrating the CNN Classifier with the camera, the cracks can be automatically detected in that region and reported.
{"title":"Crevices Recognition on Asphalt Surfaces using Convolutional Neural Network","authors":"Mukesh Chinta, Anagani Likhita, Yamini Aravapalli","doi":"10.1109/IDCIoT56793.2023.10053463","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053463","url":null,"abstract":"Heavy rainfalls leading to floods in cities and villages is a common sight in our country. These situations lead to destruction of roadways and bridges, and often public infrastructure as an aftermath. Inspection of such facilities to assess the damage and identify any potential vulnerability is a tedious process. Some of the cracks/crevices might not be even visible to the naked eye. An automated system which can detect cracks saves money, time and even lives. This will help us improve road safety which is the reason for major accidents. The proposed work uses machine learning concepts to implement such a system which automatically detects the cracks on the roads, bridges and will send an alert to the concerned authorities there by potentially reducing the risk for disaster occurrence. Convolutional Neural Networks (CNN) can be used for the identification of cracks. By integrating the CNN Classifier with the camera, the cracks can be automatically detected in that region and reported.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79613284","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}
Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053489
Rohan Mittal, Sreenitya Mandava, Tanmay S. Shetty, Harshita Patel
With the growing demand for reforestation and a sustainable neighborhood, everyone has begun to grow their own plants. However, the survival of a plant depends on many factors. A common problem faced by general customers is that their purchased plants, in gardens or balconies, fail to live long. This might happen because of many reasons, but the most recurrent one is the plant not adapting to the environmental conditions. Thus, personalizing the plant preferences is essential for users, so that they can buy the plants with high confidence of them surviving long. Here, this research work intends to develop an application with various filtering options, to determine the environmental conditions of the location, and the quality of lifestyle the plants can be provided with. To do so, we performed a comparison of the popular classification algorithms and found that the Random Forest Classifier served our purpose, successfully training an AI Model for predicting plants suiting the given conditions.
{"title":"Comparative Analysis of Classifiers in a Plant Recommendation System based on Environmental Factors","authors":"Rohan Mittal, Sreenitya Mandava, Tanmay S. Shetty, Harshita Patel","doi":"10.1109/IDCIoT56793.2023.10053489","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053489","url":null,"abstract":"With the growing demand for reforestation and a sustainable neighborhood, everyone has begun to grow their own plants. However, the survival of a plant depends on many factors. A common problem faced by general customers is that their purchased plants, in gardens or balconies, fail to live long. This might happen because of many reasons, but the most recurrent one is the plant not adapting to the environmental conditions. Thus, personalizing the plant preferences is essential for users, so that they can buy the plants with high confidence of them surviving long. Here, this research work intends to develop an application with various filtering options, to determine the environmental conditions of the location, and the quality of lifestyle the plants can be provided with. To do so, we performed a comparison of the popular classification algorithms and found that the Random Forest Classifier served our purpose, successfully training an AI Model for predicting plants suiting the given conditions.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79577906","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}
Pub Date : 2023-01-05DOI: 10.1109/IDCIoT56793.2023.10053558
S. Bhatlawande, S. Shilaskar, Tejal Gadad, S. Ghulaxe, Rachana Gaikwad
Facial recognition is important concept when it comes to identification and security. The traditional ways of securing homes like the use of locks and keys are inefficient. Developing a security system using artificial intelligence (AI) that will monitor the surroundings and act in case of emergencies is vital. This paper proposes a smart home security monitoring system that can make decisions based on facial recognition technology. It is implemented using Mediapipe for face detection and FaceNet model for facial feature extraction. The proposed face recognition model is 80.55% accurate. An android application is developed which allows user to interact with the system even from remote distances. A door opening mechanism is implemented with the help of ESP8266. With the aid of the feedback from the reed switch, an alarm sounds when someone tries to break into the residence. The developed system provides a whole new security approach by discarding the need for traditional methods of security. The accuracy of the system is 80.55%.
{"title":"Smart Home Security Monitoring System based on Face Recognition and Android Application","authors":"S. Bhatlawande, S. Shilaskar, Tejal Gadad, S. Ghulaxe, Rachana Gaikwad","doi":"10.1109/IDCIoT56793.2023.10053558","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053558","url":null,"abstract":"Facial recognition is important concept when it comes to identification and security. The traditional ways of securing homes like the use of locks and keys are inefficient. Developing a security system using artificial intelligence (AI) that will monitor the surroundings and act in case of emergencies is vital. This paper proposes a smart home security monitoring system that can make decisions based on facial recognition technology. It is implemented using Mediapipe for face detection and FaceNet model for facial feature extraction. The proposed face recognition model is 80.55% accurate. An android application is developed which allows user to interact with the system even from remote distances. A door opening mechanism is implemented with the help of ESP8266. With the aid of the feedback from the reed switch, an alarm sounds when someone tries to break into the residence. The developed system provides a whole new security approach by discarding the need for traditional methods of security. The accuracy of the system is 80.55%.","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79639618","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}
The concept of "cloud computing" has been presented as a promising strategy for providing online service hosting and distribution. Despite the widespread adoption of cloud computing, security remains a top priority. Several secure methods have been devised to safeguard communication in such scenarios, with the majority of these solutions based on attack signatures. Unfortunately, these technologies cannot always detect every possible danger. A machine learning method was recently outlined. The judgment could be inaccurate if the training set is missing examples from a certain category. In this research, an innovative firewall strategy for safe cloud-based computing is presented using machine learning system. The proposed methods estimate the final assault category categorization by combining the judgments of the nodes from the past with the decision of the machine learning algorithm in the present, a technique termed most frequent decision. Both learning efficiency and system precision are improved by this method. Our results are based on UNSW-NB-15, a publicly available dataset. According to the evidence provided by our data, it improves anomaly detection by 97.68 percent. A Machine Learning-Based Approach for Anomaly Detection for Secure Cloud Computing Environments
{"title":"A Machine Learning-Based Approach for Anomaly Detection for Secure Cloud Computing Environments","authors":"Priya Parameswarappa, Taral Shah, Govinda rajulu Lanke","doi":"10.1109/IDCIoT56793.2023.10053518","DOIUrl":"https://doi.org/10.1109/IDCIoT56793.2023.10053518","url":null,"abstract":"The concept of \"cloud computing\" has been presented as a promising strategy for providing online service hosting and distribution. Despite the widespread adoption of cloud computing, security remains a top priority. Several secure methods have been devised to safeguard communication in such scenarios, with the majority of these solutions based on attack signatures. Unfortunately, these technologies cannot always detect every possible danger. A machine learning method was recently outlined. The judgment could be inaccurate if the training set is missing examples from a certain category. In this research, an innovative firewall strategy for safe cloud-based computing is presented using machine learning system. The proposed methods estimate the final assault category categorization by combining the judgments of the nodes from the past with the decision of the machine learning algorithm in the present, a technique termed most frequent decision. Both learning efficiency and system precision are improved by this method. Our results are based on UNSW-NB-15, a publicly available dataset. According to the evidence provided by our data, it improves anomaly detection by 97.68 percent. A Machine Learning-Based Approach for Anomaly Detection for Secure Cloud Computing Environments","PeriodicalId":60583,"journal":{"name":"物联网技术","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75328320","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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