Chapter 827 Earth Orbit! Seven hundred feet of Dharma body!
I haven't finished coding today, so I'll update it later, probably around one or two in the morning. The main reason is that this chapter is really a bit laborious. It took me two or three hours to write it, but it's still a big difference. I just need to refresh it after the update.
This chapter will do.
Abstract: In order to reduce the secure transmission delay of multiple heterogeneous network data, a secure transmission technology for multiple heterogeneous network data based on machine learning is designed. By selecting the data source and defining the importance of data attributes, preprocessing the multiple heterogeneous network data,
And establish a multi-path parallel transmission architecture. On this basis, machine learning methods are used to estimate effective bandwidth and parameter filtering processing. Finally, bandwidth scheduling and channel security protocol systems are established, thereby completing secure transmission of multiple heterogeneous network data based on machine learning.
.Experimental results show that the secure transmission of multi-heterogeneous network data based on machine learning effectively reduces data transmission delays, reduces data transmission interruptions and data packet loss rates, and meets the design needs of data transmission technology.
Keywords: machine learning; multiple heterogeneous networks; secure data transmission; network data preprocessing; parallel transmission architecture
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1 Introduction
At present, communication technology is developing rapidly, and various networks have obvious characteristics. After years of reform and innovation, the transmission rate of wireless access technology is gradually approaching the limit. In this context, in order to meet various business needs, multi-network writing is needed
.However, the traditional writing mechanism cannot be used simultaneously and efficiently in the use of network transmission resources, cannot effectively ensure efficient transmission services, and will increase energy consumption in transmission, resulting in interference problems during the transmission process. Therefore, many
Scholars have carried out research on multiple network data transmission methods. In the literature [1], Shi Lingling and Li Jingzhao studied the secure data transmission mechanism in heterogeneous networks. The mechanism mainly uses an optimization-based AES-GCM authentication encryption algorithm and a
The secure data transmission mechanism combined with SHA's digital signature algorithm performs data transmission; in literature [2], Zhou Jing and Chen Chen studied a data security model based on heterogeneous networks, which encrypts the data in advance, and then
A secure transmission channel is established to transmit data. The above two methods can achieve certain results, but there are still certain shortcomings. In view of the above shortcomings, this paper applies machine learning methods to the secure transmission of data in multiple heterogeneous networks.
To solve the existing problems. The experimental results show that the multi-heterogeneous network data secure transmission technology studied this time effectively solves the existing problems and has certain practical application significance.
2 Multivariate heterogeneous network data preprocessing
In the secure transmission of data on multiple heterogeneous networks, a lot of data is useless. For this reason, it is necessary to select relevant data sources from the multiple network data for transmission, thereby improving the accuracy and efficiency of data transmission. In the process of selecting effective data sources
In, importance is used to measure the relationship between data attributes [3-4] to capture highly correlated data. The calculation expression is as follows: (1) In formula (1), T represents the comprehensive sum of all data sources.
The number of tables, (i, j) represents the correlation between the sample source classes. Based on the judgment of the importance of the data source, the collection of data tables with the highest degree of correlation can be selected and the irrelevant tables can be reduced. After the selection of the above important data sources is completed,
Analyze data attributes. Since a data source is composed of a set of data attributes, these attribute characteristics can reflect the basic information of the data to be transmitted. It is mainly measured by the correlation of data tuples, and the number of occurrences of tuple data is analyzed, that is
It is defined by tuple data density, and the data tuple density diagram is shown in Figure 1. In Figure 1, ε represents the radius of the specified neighborhood. According to this idea, the weight is assigned to each tuple data in the above data set.
[5-7], its expression is as follows: (2) In formula (2), w(C) represents the attribute weight, w(tk) represents the number of core tuples, δ represents outliers, and w(tb) represents
Number of tuples of edges.
3Multipath parallel transmission architecture
After the above preprocessing is completed, a multipath parallel transmission architecture is established. The main contents are as follows: traffic segmentation in advance. Communication flow segmentation is used by the sender to segment large data blocks into data units of different sizes or the same size [8].
The size is determined by the granularity of communication flow segmentation, which is mainly divided into the following categories: First, in packet-level business segmentation, the packet is the smallest component unit of the data flow. Therefore, the segmentation method has the smallest granularity, and the grouping probabilities are independent of each other and can be sent
to the sending end; second, traffic segmentation at the flow level [9], which encapsulates a specific destination address in the packet header, and then aggregates packets with the same destination address into data flows. These different data flows are independent of each other and passed through a unique
Flow identifiers are used to distinguish them. The use of flow-level segmentation technology can effectively solve the impact of data distortion on multipath transmission [10]. Third, traffic segmentation at the sub-flow level, the data flow with the same destination header is divided into multiple sub-flows.
, the packets in all sub-flows have the same destination address, which solves the load imbalance problem in the flow segmentation algorithm to a certain extent. The multi-path parallel transmission architecture is shown in Figure 2. In addition, in bandwidth aggregation
In the architecture, the scheduling algorithm is the core of determining the service transmission mode and the scheduling order of service subflows [11], ensuring that the service subflows arrive at the receiving end in an orderly manner. Next, we will discuss data scheduling.
4 Bandwidth scheduling plan formulation
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5 Experimental comparison
In order to verify the effectiveness of the designed secure data transmission technology for multiple heterogeneous networks based on machine learning, experimental analysis was conducted, and the secure data transmission mechanism in heterogeneous networks in literature [1] and the secure data transmission mechanism in heterogeneous networks in literature [2] were used.
A data security model is compared to compare the effectiveness of the three systems. The experimental data set in this experiment is shown in Table 1. From the experimental data collected above, it can be seen that more and more data are selected for the experiment.
In order to better verify the effectiveness of the three methods, we mainly compare the transmission delays, data transmission interruptions and link packet loss rates of the three methods. The specific content is as follows.
5.1 Transmission delay comparison
The transmission delays of the three methods are compared respectively, and the comparison results are shown in Figure 5. By analyzing Figure 5, it is found that in the transmission of Google public data sets, the transmission delays of the three methods are all smaller. As the amount of transmitted data increases,
The data transmission delays of the three methods have increased, but after comparison, it can be found that the multi-heterogeneous network data secure transmission technology based on machine learning studied this time has the smallest transmission delay, which is less than the two traditional methods.
5.2 Comparison of data transmission interruption situations
Comparing the data transmission interruptions after applying the three transmission technologies respectively, the comparison results are shown in Figure 6. From Figure 6, it can be found that the transmission technology studied this time has the least data transmission interruptions, and it is less than that in several experiments.
Two traditional transmission technologies.
5.3 Link packet loss rate comparison
The multi-heterogeneous network data secure transmission technology based on machine learning and the two traditional transmission technologies studied in this study are respectively used for data transmission. The comparison results of the packet loss rates of the three methods are shown in Figure 7. By analyzing Figure 7, it can be found that,
The link packet loss rate of the secure data transmission mechanism in traditional heterogeneous networks is the highest, which is higher than that of a data security model based on heterogeneous networks and the transmission technology of this study. In summary, the machine learning-based
The multi-heterogeneous network data secure transmission technology has less transmission delay and packet loss rate than the two traditional transmission technologies. The reason is that the developed transmission technology pre-processes the multi-heterogeneous network data in advance and develops a bandwidth scheduling plan.
, established a secure transmission protocol, thus improving the secure transmission effect of multiple heterogeneous network data.
6Conclusion
This paper designs a multi-heterogeneous network data secure transmission technology based on machine learning, and verifies the effectiveness of this research technology through experiments. This technology can improve the efficiency of data transmission, and can also reduce the packet loss rate of data transmission. It has practical application significance.
Stronger. However, due to the limitation of research time, the multi-heterogeneous network data secure transmission technology studied this time still has certain shortcomings. Therefore, further optimization is needed in subsequent research.
Abstract: This article explains that virtualization technology ensures the stability and fluency of information use, cloud storage technology ensures the rationality of data distribution, and information security technology ensures the security of big data use and browsing.
Keywords: computer system, big data, cloud storage, virtualization.
0Introduction
Computer software technology can process large amounts of data in a short period of time, adopt certain logic for editing and analysis, propose relevant data information that users need, perform reprocessing, and determine relevant data content that is subject to data analysis that users need.
1Virtualization technology
Virtualization technology is an innovative technology in computer software technology. It can create a new virtual machine space for users to use in a short period of time. Virtualization technology truly makes rational use of information resources and effectively allocates software resources.
And mobilization, reasonable allocation and utilization of computer software resources can also prevent computer software from being stuck or slow due to uneven distribution of software resources during the running process. Flexible transformation is a significant feature of virtualization technology.
It can run and calculate on virtual computing components, realize cross-domain sharing and cooperation of computers, process and switch the resources required by users, and form a new resource chain. Virtualization technology mainly includes the following categories: Server virtualization
ization, Docker container technology, the focus of which is Docker container technology. Server virtualization is based on the multi-dimensional virtualization of computers, virtualizing an ontology computer into multiple virtual logically associated computers, and establishing a virtualization layer to combine the computer hardware
It is interconnected with logically related systems and realizes specific functions through decoupling and association. The so-called virtualization level means that multiple virtualized operating systems can be run on a physical computer and can be switched between each other, and the virtual computers at these levels
You can share one or more unique software and hardware resources, such as the memory, motherboard, graphics card, etc. in a conventional computer. On this basis, with the corresponding operating system support, users can download programs and
software for your own use (Figure 1).
Please remember the first domain name of this book:. Mobile version reading URL:
