Pub Date : 2021-03-02DOI: 10.1007/978-3-030-71017-0_6
R. Díaz, Luis Hernández-Álvarez, L. H. Encinas, Araceli Queiruga-Dios
{"title":"Chor-Rivest Knapsack Cryptosystem in a Post-quantum World","authors":"R. Díaz, Luis Hernández-Álvarez, L. H. Encinas, Araceli Queiruga-Dios","doi":"10.1007/978-3-030-71017-0_6","DOIUrl":"https://doi.org/10.1007/978-3-030-71017-0_6","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84820260","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}
With technology constantly becoming present in people’s lives, smart homes are increasing in popularity. A smart home system controls lighting, temperature, security camera systems, and appliances. These devices and sensors are connected to the internet, and these devices can easily become the target of attacks. To mitigate the risk of using smart home devices, the security and privacy thereof must be artificially smart so they can adapt based on user behavior and environments. The security and privacy systems must accurately analyze all actions and predict future actions to protect the smart home system. We propose a Hybrid Intrusion Detection (HID) system using machine learning algorithms, including random forest, X gboost, decision tree, K -nearest neighbors, and misuse detection technique.
{"title":"A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior","authors":"Faisal Alghayadh, D. Debnath","doi":"10.4236/AIT.2021.111002","DOIUrl":"https://doi.org/10.4236/AIT.2021.111002","url":null,"abstract":"With \u0000technology constantly becoming present in people’s lives, smart homes are \u0000increasing in popularity. A smart home system controls lighting, temperature, security \u0000camera systems, and appliances. These devices and sensors are connected to the \u0000internet, and these devices can easily become the target of attacks. To \u0000mitigate the risk of using smart home devices, the security and privacy thereof \u0000must be artificially smart so they can adapt based on user behavior and environments. \u0000The security and privacy systems must accurately analyze all actions and predict \u0000future actions to protect the smart home system. We propose a Hybrid Intrusion Detection \u0000(HID) system using machine learning algorithms, including random forest, X gboost, \u0000decision tree, K -nearest neighbors, and misuse detection technique.","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44304496","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}
Real time monitoring of herbicide spray droplet drift is important for crop production management and environmental protection. Existing spray droplet drift detection methods, such as water-sensitive paper and tracers of fluorescence and Rubidium chloride, are time-consuming and laborious, and the accuracies are not high in general. Also, the tracer methods indirectly quantify the spray deposition from the concentration of the tracer and may change the drift characteristics of the sprayed herbicides. In this study, a new optical sensor system was developed to directly detect the spray droplets without the need to add any tracer in the spray liquid. The system was prototyped using a single broadband programmable LED light source and a near infrared sensor containing 6 broadband spectral detectors at 610, 680, 730, 760, 810, and 860 nm to build a detection system for monitoring and analysis of herbicide spray droplet drift. A rotatory structure driven by a stepper motor in the system was created to shift the droplet capture line going under the optical sensor to measure and collect the spectral signals that reflect spray drift droplets along the line. The system prototype was tested for detection of small (Very Fine and Fine), medium (Medium), and large (Coarse) droplets within the droplet classifications of the American Society of Agricultural and Biological Engineers. Laboratory testing results indicated that the system could detect the droplets of different sizes and determine the droplet positions on the droplet capture line with 100% accuracy at the wavelength of 610 nm selected from the 6 bands to detect the droplets.
除草剂雾滴漂移的实时监测对作物生产管理和环境保护具有重要意义。现有的喷雾液滴漂移检测方法，如水敏纸、荧光示踪剂、氯化铷示踪剂等，耗时费力，一般精度不高。此外，示踪剂方法通过示踪剂的浓度间接量化喷雾沉积，并可能改变喷洒除草剂的漂移特性。在本研究中，开发了一种新的光学传感器系统，可以直接检测喷雾液滴，而无需在喷雾液中添加任何示踪剂。该系统采用单宽带可编程LED光源和包含6个610、680、730、760、810和860 nm宽带光谱探测器的近红外传感器，构建了除草剂喷雾液滴漂移监测与分析的检测系统。在系统中设计了一个由步进电机驱动的旋转结构，用于移动光学传感器下的液滴捕获线，以测量和收集沿线反射喷雾漂移液滴的光谱信号。在美国农业和生物工程师协会的液滴分类中，对系统原型进行了小液滴(Very Fine and Fine)、中液滴(medium)和大液滴(Coarse)的检测测试。实验室测试结果表明，该系统在检测液滴的6个波段中选择610 nm波长，可以检测不同大小的液滴，并以100%的准确率确定液滴在液滴捕获线上的位置。
{"title":"Development and Evaluation of an Optical Sensing System for Detection of Herbicide Spray Droplets","authors":"Yanbo Huang, Wei Ma, D. Fisher","doi":"10.4236/AIT.2021.111001","DOIUrl":"https://doi.org/10.4236/AIT.2021.111001","url":null,"abstract":"Real time monitoring of herbicide spray droplet drift is important for \u0000crop production management and environmental protection. Existing spray droplet \u0000drift detection methods, such as water-sensitive paper and tracers of \u0000fluorescence and Rubidium chloride, are time-consuming and laborious, and the \u0000accuracies are not high in general. Also, the tracer methods indirectly \u0000quantify the spray deposition from the concentration of the tracer and may \u0000change the drift characteristics of the sprayed herbicides. In this study, a \u0000new optical sensor system was developed to directly detect the spray droplets \u0000without the need to add any tracer in the spray liquid. The system was \u0000prototyped using a single broadband programmable LED light source and a near \u0000infrared sensor containing 6 broadband spectral detectors at 610, 680, 730, \u0000760, 810, and 860 nm to build a detection system for monitoring and analysis of \u0000herbicide spray droplet drift. A rotatory structure driven by a stepper motor \u0000in the system was created to shift the droplet capture line going under the \u0000optical sensor to measure and collect the spectral signals that reflect spray \u0000drift droplets along the line. The system prototype was tested for detection of \u0000small (Very Fine and Fine), medium (Medium), and large (Coarse) droplets within \u0000the droplet classifications of the American Society of Agricultural and \u0000Biological Engineers. Laboratory testing results indicated that the system \u0000could detect the droplets of different sizes and determine the droplet \u0000positions on the droplet capture line with 100% accuracy at the wavelength of \u0000610 nm selected from the 6 bands to detect the droplets.","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48090798","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 : 2021-01-01DOI: 10.1007/978-3-030-71017-0_50
Michael Galloway, Elijah Sparks, Mason Galloway
{"title":"On the Development of Low-Cost Autonomous UAVs for Generation of Topographic Maps","authors":"Michael Galloway, Elijah Sparks, Mason Galloway","doi":"10.1007/978-3-030-71017-0_50","DOIUrl":"https://doi.org/10.1007/978-3-030-71017-0_50","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82207934","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 : 2021-01-01DOI: 10.1007/978-3-030-71017-0_5
Salahaldeen Duraibi, F. Alqahtani, Frederick T. Sheldon, Wasim Alhamdani
{"title":"Suitability of Voice Recognition Within the IoT Environment","authors":"Salahaldeen Duraibi, F. Alqahtani, Frederick T. Sheldon, Wasim Alhamdani","doi":"10.1007/978-3-030-71017-0_5","DOIUrl":"https://doi.org/10.1007/978-3-030-71017-0_5","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82386750","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 : 2021-01-01DOI: 10.1007/978-3-030-71017-0_42
Shogo Kamata, Chunghan Lee, S. Date
{"title":"Per-user Access Control Framework for Link Connectivity and Network Bandwidth","authors":"Shogo Kamata, Chunghan Lee, S. Date","doi":"10.1007/978-3-030-71017-0_42","DOIUrl":"https://doi.org/10.1007/978-3-030-71017-0_42","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78966944","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 : 2021-01-01DOI: 10.1007/978-3-030-71017-0_23
Warren Z. Cabral, C. Valli, L. Sikos, Samuel G. Wakeling
{"title":"Analysis of Conpot and Its BACnet Features for Cyber-Deception","authors":"Warren Z. Cabral, C. Valli, L. Sikos, Samuel G. Wakeling","doi":"10.1007/978-3-030-71017-0_23","DOIUrl":"https://doi.org/10.1007/978-3-030-71017-0_23","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"43 1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76116407","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 : 2021-01-01DOI: 10.1007/978-3-030-71017-0_21
M. Ashrafuzzaman, Saikat Das, Ph.D., Y. Chakhchoukh, Salahaldeen Duraibi, S. Shiva, Frederick T. Sheldon
{"title":"Supervised Learning for Detecting Stealthy False Data Injection Attacks in the Smart Grid","authors":"M. Ashrafuzzaman, Saikat Das, Ph.D., Y. Chakhchoukh, Salahaldeen Duraibi, S. Shiva, Frederick T. Sheldon","doi":"10.1007/978-3-030-71017-0_21","DOIUrl":"https://doi.org/10.1007/978-3-030-71017-0_21","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"53 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91067874","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 : 2021-01-01DOI: 10.1007/978-3-030-71017-0_60
S. Amer
{"title":"Ethical Issues of the Use of AI in Healthcare","authors":"S. Amer","doi":"10.1007/978-3-030-71017-0_60","DOIUrl":"https://doi.org/10.1007/978-3-030-71017-0_60","url":null,"abstract":"","PeriodicalId":69922,"journal":{"name":"物联网(英文)","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85917621","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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