Abstract
With the rise of 5G and Internet of things, especially the key technology of 5G, network slice cuts a physical network into multiple virtual end-to-end networks, each of them can obtain logically independent network resources to support richer services. 5G mobile data and sensor data converge to form a growing network traffic. Traffic explosion evolved into a mixed network type, and network viruses, worms, network theft and malicious attacks are also involved. How to distinguish traffic types, block malicious traffic and make effective use of sensor data under the background of 5G network slice, and also the significance of this study.
Highlights
With the advent of the era of 5G and artificial intelligence, machine learning plays an important role in image recognition, language processing, speech recognition and other fields
The Internet of things of home is selected as the research scene, through the construction of a sensor network, ZigBee device is connected to the Internet, to obtain temperature, humidity, power of home appliances, smoke concentration and other sensor data in the home, and it used the machine learning to study and analyze the flow data, identify the validity of flow, ensure the security of home network, summarize and analyze sensor data, and ensure the security of home physical environment
Under the background of 5G network slice, this paper proposes a home traffic analysis system combined with Internet of things [5, 6]
Summary
With the advent of the era of 5G and artificial intelligence, machine learning plays an important role in image recognition, language processing, speech recognition and other fields. This paper introduces the design and implementation of the home traffic analysis system combined with the Internet of things in detail and demonstrates its characteristics in the process of explaining it. The first is to create the Internet of things inside the smart home, using the sensor network architecture of ZigBee, collect data through the ZigBee node for each sensor device and collect data through the coordinator to the gateway, which forward the data to the server, realizing the traffic classification [12,13,14,15]. In order to compare the performance differences of different machine learning algorithms in the process of feature selection, this paper describes the feature selection of two different algorithms and measures them through experiments. Network slice is one of the key technologies of 5G, NFV is an essential technology of network slice, NFV isolated from traditional network hardware and software part and hardware by unified server deployment, the software shall be borne by the different network function (NF), so as to realize the demand of flexible assembly operations, Network slice allow to share the same physical network communication link, and complete the data exchange by virtual independent sub network, so as to better meet the needs of 5G everything connected
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