Mining of instant messaging data in the Internet of Things based on support vector machine

2020 ◽  
Vol 154 ◽  
pp. 278-287 ◽  
Author(s):  
Yang Chen
Keyword(s):  
Support Vector Machine ◽  
Internet Of Things ◽  
Instant Messaging ◽  
Support Vector ◽  
The Internet ◽  
The Internet Of Things

An information security analysis method of Internet of things based on balanced double SVM

2020 ◽  
Vol 39 (6) ◽  
pp. 8633-8642
Author(s):  
Zhu, Hongwei ◽  
Wang, Xuesong
Keyword(s):  
Support Vector Machine ◽  
Information Security ◽  
Internet Of Things ◽  
Security Analysis ◽  
Reference Value ◽  
Support Vector ◽  
The Internet ◽  
Analysis Method ◽  
Network Intrusion ◽  
The Internet Of Things

With the continuous progress of social science and technology, the development of the Internet of things is growing. With the development of Internet of things, security problems emerge in endlessly. During the period of COVID-19, the Internet of Things have been widely used to fight virus outbreak. However, the most serious security problem of the Internet of things is network intrusion. This paper proposes a balanced quadratic support vector machine information security analysis method for Internet of things. Compared with the traditional support vector machine Internet of things security analysis method, this method has a higher accuracy, and can shorten the detection time, with efficient and powerful characteristics. The method proposed in this paper has certain reference value to the Internet of things network intrusion problem. It provides better security for the Internet of things during the protection period of covid-19.

Application of feature extraction method based on support vector machine in Internet of things

2020 ◽  
Vol 39 (6) ◽  
pp. 8623-8632
Author(s):  
Tang Lin
Keyword(s):  
Support Vector Machine ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Smart Home ◽  
Rapid Development ◽  
Support Vector ◽  
The Internet ◽  
Machine Model ◽  
Network Intrusion ◽  
The Internet Of Things

Although much less fatal than the Ebola and previous SARS virus epidemics, the current coronavirus outbreak (COVID-19) has spread to more people in more countries in a much shorter time frame. With the rapid development of the Internet of things, it has played an important role to track/monitor transmission movements throughout the population. The technology infrastructure between mobile devices, wearable devices and sensors, smart home device makes it possible to readily deploy solutions to monitor and collect data and perform analysis to ensure policy make intelligent, rapid decisions. This research combines AOL and Support Vector Machine to form the Internet of things cycle through smart home. The parameters of Support Vector Machine model are optimized by ALO algorithm, which shortens the learning time and improves the performance of classifier. Then, the algorithm of ALO is used to optimize the Support Vector Machine intrusion detection method and agent technology, and the intrusion detection model is established. Experimental results show that the combination of these two can effectively reduce the false alarm rate of network intrusion.

scholarly journals An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1213 ◽  
Author(s):  
Qiao Tian ◽  
Yun Lin ◽  
Xinghao Guo ◽  
Jin Wang ◽  
Osama AlFarraj ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Wireless Communication ◽  
Radio Frequency ◽  
Success Rate ◽  
Mobile Internet ◽  
Support Vector ◽  
The Internet ◽  
Communication Device ◽  
Rf Fingerprint ◽  
The Internet Of Things

With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

scholarly journals Appling an Improved Method Based on ARIMA Model to Predict the Short-Term Electricity Consumption Transmitted by the Internet of Things (IoT)

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ni Guo ◽  
Wei Chen ◽  
Manli Wang ◽  
Zijian Tian ◽  
Haoyue Jin
Keyword(s):  
Internet Of Things ◽  
Arima Model ◽  
Electricity Consumption ◽  
Support Vector ◽  
Small Scale ◽  
The Internet ◽  
Short Term ◽  
Prediction Horizon ◽  
The Mean ◽  
The Internet Of Things

The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results.

Assessment on Different Classification Algorithms Used in Internet of Things Applications

2019 ◽  
Vol 9 (1) ◽  
pp. 1-11
Author(s):  
Akhil Bansal ◽  
Manish Kumar Ahirwar ◽  
Piyush K. Shukla
Keyword(s):  
Internet Of Things ◽  
Smart City ◽  
Economic Benefits ◽  
Support Vector ◽  
The Internet ◽  
Classification Algorithms ◽  
Complex Environment ◽  
The Past ◽  
Iot Devices ◽  
The Internet Of Things

The past few years have witnessed the Internet of Things (IoT) has evolved a lot and continues to evolve in various fields such as healthcare, agriculture, smart city, education, industries, automation, home care, etc. This advancement is caused by the development of sensor-enabled devices called IoT devices. The data collected from such devices will be used to identify and manage complex environment around us that will reduce the human intervention and also escalate efficiency, productivity, accuracy and economic benefits of the devices. In this survey article, the authors analyze how the datasets of different applications of the IoT such as agriculture, healthcare, smart city are processed and classified. The article also outlines the recent review of more common classification algorithms such as Support Vector Machine, Naïve Byes, Decision Tree, etc., that were used to classify the dataset with different parameters applied to the Internet of Things applications. In addition, this article also gives a brief review of the applications of the Internet of Things.

Multimedia system and database simulation based on internet of things and cloud service platform

2020 ◽  
pp. 1-12
Author(s):  
Zhang Caiqian ◽  
Zhang Xincheng
Keyword(s):  
Internet Of Things ◽  
Cloud Service ◽  
Multimedia System ◽  
The Internet ◽  
Multimedia Database ◽  
Cloud Platform ◽  
Service Platform ◽  
Device Management ◽  
Cloud Service Platform ◽  
The Internet Of Things

The existing stand-alone multimedia machines and online multimedia machines in the market have certain deficiencies, so they cannot meet the actual needs. Based on this, this research combines the actual needs to design and implement a multi-media system based on the Internet of Things and cloud service platform. Moreover, through in-depth research on the MQTT protocol, this study proposes a message encryption verification scheme for the MQTT protocol, which can solve the problem of low message security in the Internet of Things communication to a certain extent. In addition, through research on the fusion technology of the Internet of Things and artificial intelligence, this research designs scheme to provide a LightGBM intelligent prediction module interface, MQTT message middleware, device management system, intelligent prediction and push interface for the cloud platform. Finally, this research completes the design and implementation of the cloud platform and tests the function and performance of the built multimedia system database. The research results show that the multimedia database constructed in this paper has good performance.

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