The Internet of Things (IoT) will significantly impact our social and economic lives in the near future. Many Internet of Things (IoT) applications aim to automate multiple tasks so inactive physical objects can behave independently of others. IoT devices, however, are also vulnerable, mostly because they lack the essential built-in security to thwart attackers. It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment. As a result, the IoT designs that are now in use do not completely support all of the advancements that have been made to include sophisticated features in IoT, such as Cloud computing, machine learning techniques, and lightweight encryption techniques. This paper presents a detailed analysis of the security requirements, attack surfaces, and security solutions available for IoT networks and suggests an innovative IoT architecture. The Seven-Layer Architecture in IoT provides decent attack detection accuracy. According to the level of risk they pose, the security threats in each of these layers have been properly categorized, and the essential evaluation criteria have been developed to evaluate the various threats. Also, Machine Learning algorithms like Random Forest and Support Vector Machines, etc., and Deep Learning algorithms like Artificial Neural Networks, Q Learning models, etc., are implemented to overcome the most damaging threats posing security breaches to the different IoT architecture layers.
{"title":"A Novel IoT Architecture, Assessment of Threats and Their Classification with Machine Learning Solutions","authors":"Oliva Debnath, Saptarshi Debnath, Sreyashi Karmakar, MD Tausif Mallick, Himadri Nath Saha","doi":"10.32604/jiot.2023.039391","DOIUrl":"https://doi.org/10.32604/jiot.2023.039391","url":null,"abstract":"The Internet of Things (IoT) will significantly impact our social and economic lives in the near future. Many Internet of Things (IoT) applications aim to automate multiple tasks so inactive physical objects can behave independently of others. IoT devices, however, are also vulnerable, mostly because they lack the essential built-in security to thwart attackers. It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment. As a result, the IoT designs that are now in use do not completely support all of the advancements that have been made to include sophisticated features in IoT, such as Cloud computing, machine learning techniques, and lightweight encryption techniques. This paper presents a detailed analysis of the security requirements, attack surfaces, and security solutions available for IoT networks and suggests an innovative IoT architecture. The Seven-Layer Architecture in IoT provides decent attack detection accuracy. According to the level of risk they pose, the security threats in each of these layers have been properly categorized, and the essential evaluation criteria have been developed to evaluate the various threats. Also, Machine Learning algorithms like Random Forest and Support Vector Machines, etc., and Deep Learning algorithms like Artificial Neural Networks, Q Learning models, etc., are implemented to overcome the most damaging threats posing security breaches to the different IoT architecture layers.","PeriodicalId":488829,"journal":{"name":"Journal on internet of things","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135501428","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 : 2023-01-01DOI: 10.32604/jiot.2023.040255
Kenedy A. Greyson
Rainwater harvesting (RWH) systems have been the source of domestic water for many years and still becoming essential in many communities of developing countries. However, due to various reasons, there are several sources of contamination in the rainwater cistern systems. Dissolved chemicals from the roofing, storage, and conveyance materials, together with the suspended particulate matter from the airborne, are examples of water contamination. In this work, the water quality monitoring system has been designed and implemented. Chemical and physical parameters of water samples were collected from three locations using a data acquisition (DAQ) system and rainwater quality was analyzed using Water Pollution Index (WPI). Results obtained from three locations have been presented.
{"title":"Suitability and Sustainability of Rainwater Quality Monitoring System in Cistern for Domestic Use","authors":"Kenedy A. Greyson","doi":"10.32604/jiot.2023.040255","DOIUrl":"https://doi.org/10.32604/jiot.2023.040255","url":null,"abstract":"Rainwater harvesting (RWH) systems have been the source of domestic water for many years and still becoming essential in many communities of developing countries. However, due to various reasons, there are several sources of contamination in the rainwater cistern systems. Dissolved chemicals from the roofing, storage, and conveyance materials, together with the suspended particulate matter from the airborne, are examples of water contamination. In this work, the water quality monitoring system has been designed and implemented. Chemical and physical parameters of water samples were collected from three locations using a data acquisition (DAQ) system and rainwater quality was analyzed using Water Pollution Index (WPI). Results obtained from three locations have been presented.","PeriodicalId":488829,"journal":{"name":"Journal on internet of things","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135400119","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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