{"title":"Security Challenges and Requirements for Industrial IoT Systems","authors":"V. Valentin, A. Mehaoua, F. Guenane","doi":"10.1201/9780429270567-5","DOIUrl":"https://doi.org/10.1201/9780429270567-5","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81425717","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 : 2020-11-19DOI: 10.1201/9780429270567-10
Chithraja Rajan, D. Sharma, D. P. Samajdar, Jyoti Patel
{"title":"Low Power Physical Layer Security Solutions for IoT Devices","authors":"Chithraja Rajan, D. Sharma, D. P. Samajdar, Jyoti Patel","doi":"10.1201/9780429270567-10","DOIUrl":"https://doi.org/10.1201/9780429270567-10","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"151 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77750637","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 : 2020-11-19DOI: 10.1201/9780429270567-11
P. Do, Phu Pham, T. Phan
{"title":"Some Research Issues of Harmful and Violent Content Filtering for Social Networks in the Context of Large-Scale and Streaming Data with Apache Spark","authors":"P. Do, Phu Pham, T. Phan","doi":"10.1201/9780429270567-11","DOIUrl":"https://doi.org/10.1201/9780429270567-11","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"195 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82124319","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}
{"title":"Cyber-Physical Systems in Healthcare","authors":"R. Raju, M. Moh","doi":"10.1201/9780429270567-2","DOIUrl":"https://doi.org/10.1201/9780429270567-2","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79603845","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}
Lorenzo Fernández Maimó, Alberto Huertas Celdrán, F. J. G. Clemente
{"title":"Anomaly Detection on Encrypted and High-Performance Data Networks by Means of Machine Learning Techniques","authors":"Lorenzo Fernández Maimó, Alberto Huertas Celdrán, F. J. G. Clemente","doi":"10.1201/9780429270567-7","DOIUrl":"https://doi.org/10.1201/9780429270567-7","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84356340","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 detection of security-related events using machine learning approaches has been extensively investigated in the past. Particularly, machine learningbased network intrusion detection has attracted a lot of attention due to its potential to detect unknown attacks. A number of classification techniques have been used for that purpose, but they were mostly classical schemes like decision trees. In this paper we go one step further and explore the use of a set of machine learning techniques denominated generically as “deep learning” that have been generating excellent results in other areas. We compare three recent techniques – generalized linear models, gradient boosting machines, and deep learning – with classical classifiers. The comparison is performed using a recent data set of network communication traces designed carefully for evaluating intrusion detection schemes. We show that deep learning techniques have an undeniable value over older algorithms, since better model fitting indicators can be achieved.
{"title":"Deep Learning for Network Intrusion Detection: An Empirical Assessment","authors":"A. Gouveia, M. Correia","doi":"10.1201/9780429270567-8","DOIUrl":"https://doi.org/10.1201/9780429270567-8","url":null,"abstract":"The detection of security-related events using machine learning approaches has been extensively investigated in the past. Particularly, machine learningbased network intrusion detection has attracted a lot of attention due to its potential to detect unknown attacks. A number of classification techniques have been used for that purpose, but they were mostly classical schemes like decision trees. In this paper we go one step further and explore the use of a set of machine learning techniques denominated generically as “deep learning” that have been generating excellent results in other areas. We compare three recent techniques – generalized linear models, gradient boosting machines, and deep learning – with classical classifiers. The comparison is performed using a recent data set of network communication traces designed carefully for evaluating intrusion detection schemes. We show that deep learning techniques have an undeniable value over older algorithms, since better model fitting indicators can be achieved.","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76148108","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}
XGBoost is a recent machine learning method that has been getting increasing attention. It won Kaggle’s Higgs Machine Learning Challenge, among several other Kaggle competitions, due to its performance. In this , we explore the use of XGBoost in the context of anomaly-based network intrusion detection, an area in which there is a considerable gap. We study not only the performance of XGBoost with two recent datasets, but also how to optimize its performance and model parameter choice. We also provide insights into which dataset features are best for performance tuning.
{"title":"Network Intrusion Detection with XGBoost","authors":"A. Gouveia, M. Correia","doi":"10.1201/9780429270567-6","DOIUrl":"https://doi.org/10.1201/9780429270567-6","url":null,"abstract":"XGBoost is a recent machine learning method that has been getting increasing attention. It won Kaggle’s Higgs Machine Learning Challenge, among several other Kaggle competitions, due to its performance. In this , we explore the use of XGBoost in the context of anomaly-based network intrusion detection, an area in which there is a considerable gap. We study not only the performance of XGBoost with two recent datasets, but also how to optimize its performance and model parameter choice. We also provide insights into which dataset features are best for performance tuning.","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78682395","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}
R. P. França, Ana Carolina Borges Monteiro, Rangel Arthur, Y. Iano
{"title":"An Overview of the Integration between Cloud Computing and Internet of Things (IoT) Technologies","authors":"R. P. França, Ana Carolina Borges Monteiro, Rangel Arthur, Y. Iano","doi":"10.1201/9780429270567-1","DOIUrl":"https://doi.org/10.1201/9780429270567-1","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90553489","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 blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as bank-ing, financing, trading, manufacturing, supply chain management, healthcare, and government. Blockchain and the Internet of Things (BIOT) apply the us-age of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable. The integration of blockchain technology and IoT creates modern decentralized systems. The BIOT models can be ap-plied by various industries including e-commerce to promote decentralization, scalability, and security. This research calls for innovative and advanced re-search on Blockchain and recommendation systems. We aim at building a se-cure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recom-mendation system with acceptable performance.
{"title":"Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain","authors":"Tzu-Yu Yeh, R. Kashef","doi":"10.4236/ait.2020.104004","DOIUrl":"https://doi.org/10.4236/ait.2020.104004","url":null,"abstract":"A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as bank-ing, financing, trading, manufacturing, supply chain management, healthcare, and government. Blockchain and the Internet of Things (BIOT) apply the us-age of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable. The integration of blockchain technology and IoT creates modern decentralized systems. The BIOT models can be ap-plied by various industries including e-commerce to promote decentralization, scalability, and security. This research calls for innovative and advanced re-search on Blockchain and recommendation systems. We aim at building a se-cure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recom-mendation system with acceptable performance.","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49215815","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}
Numerous Internet of Things (IoT) devices are being connected to the net-works to offer services. To cope with a large diversity and number of IoT ser-vices, operators must meet those needs with a more flexible and efficient net-work architecture. Network slicing in 5G promises a feasible solution for this issue with network virtualization and programmability enabled by NFV (Net-work Functions Virtualization). In this research, we use virtualized IoT plat-forms as the Virtual Network Functions (VNFs) and customize network slices enabled by NFV with different QoS to support various kinds of IoT services for their best performance. We construct three different slicing systems including: 1) a single slice system, 2) a multiple customized slices system and 3) a single but scalable network slice system to support IoT services. Our objective is to compare and evaluate these three systems in terms of their throughput, aver-age response time and CPU utilization in order to identify the best system de-sign. Validated with our experiments, the performance of the multiple slicing system is better than those of the single slice systems whether it is equipped with scalability or not.
{"title":"Enabling IoT Network Slicing with Network Function Virtualization","authors":"Ting-An Tsai, F. Lin","doi":"10.4236/ait.2020.103003","DOIUrl":"https://doi.org/10.4236/ait.2020.103003","url":null,"abstract":"Numerous Internet of Things (IoT) devices are being connected to the net-works to offer services. To cope with a large diversity and number of IoT ser-vices, operators must meet those needs with a more flexible and efficient net-work architecture. Network slicing in 5G promises a feasible solution for this issue with network virtualization and programmability enabled by NFV (Net-work Functions Virtualization). In this research, we use virtualized IoT plat-forms as the Virtual Network Functions (VNFs) and customize network slices enabled by NFV with different QoS to support various kinds of IoT services for their best performance. We construct three different slicing systems including: 1) a single slice system, 2) a multiple customized slices system and 3) a single but scalable network slice system to support IoT services. Our objective is to compare and evaluate these three systems in terms of their throughput, aver-age response time and CPU utilization in order to identify the best system de-sign. Validated with our experiments, the performance of the multiple slicing system is better than those of the single slice systems whether it is equipped with scalability or not.","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47513254","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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