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Abstract: To reduce the delay in secure data transmission for heterogeneous networks, a secure data transmission technology based on machine learning is designed. By selecting data sources and defining the importance of data attributes, preprocessing is performed for heterogeneous network data, and a multi-path parallel transmission architecture is established. On this basis, machine learning methods are used for efficient bandwidth estimation and parameter filtering, and finally, bandwidth scheduling and channel security protocol systems are established to complete secure data transmission in heterogeneous networks based on machine learning. Experimental results show that the secure data transmission method for heterogeneous networks based on machine learning effectively reduces data transmission delay and decreases data transmission interruption and packet loss rate, meeting the design requirements of data transmission technology.

Keywords: Machine Learning; Heterogeneous Networks; Secure Data Transmission; Network Data Preprocessing; Parallel Transmission Architecture

1 Introduction

Currently, communications technology is developing rapidly with distinct characteristics of various networks. After years of reform and innovation, the transmission rate of wireless access technology is gradually approaching the limit. Under this background, to meet various business needs, multi-network writing is necessary. However, the traditional writing mechanism is inefficient in utilizing network transmission resources simultaneously and effectively, failing to ensure efficient business transmission and increasing energy consumption issues during transmission, resulting in interference problems during transmission. Therefore, many scholars have conducted research on methods of multi-network data transmission. In literature [1], Shi Lingling and Li Jingzhao studied a secure data transmission mechanism in heterogeneous networks, which mainly employs an optimized AES-GCM authentication encryption algorithm combined with a SHA-based digital signature algorithm for data transmission; In literature [2], Zhou Jing and Chen Chen studied a data security model based on heterogenous networks, which pre-encrypts data and then establishes secure transmission channels for data transmission. Although both methods achieve certain results, there are still some deficiencies. To address these deficiencies, this paper applies machine learning methods in the secure data transmission of heterogeneous networks to solve existing problems. Experimental results show that the researched secure data transmission technology for heterogeneous networks effectively resolves current issues and possesses certain practical application significance.

2 Data Preprocessing for Heterogeneous Networks

In the secure data transmission of heterogeneous networks, much data is useless. Therefore, it is necessary to select relevant data sources from heterogeneous network data for transmission, thereby improving the accuracy and efficiency of data transmission. In the process of selecting effective data sources, the importance is used to measure the relationship between data attributes [3-4] and capture highly correlated data, with the calculation expression as follows: (1) In formula (1), T represents the comprehensive table index of all data sources, and (i, j) indicates the correlation between example source classes. Based on the judgment of the importance of data sources, the data table set with the highest correlation can be selected, reducing irrelevant tables. After completing the above selection of important data sources, data attributes are analyzed. A data source is composed of a set of data attributes, and these attribute characteristics reflect the basic information of the data to be transmitted. Mainly, the correlation of data tuples is used for measurement, analyzing the frequency of occurrence of tuple data, and defining based on tuple data density. A density map of data tuples is shown in Figure 1. In Figure 1, ε represents the radius of the specified neighborhood. Following this idea, the weight of each tuple data in the above data set is assigned [5-7], with its expression 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 the number of edge tuples.

3 Multi-path Parallel Transmission Architecture

After the above preprocessing is completed, a multi-path parallel transmission architecture is established, with the main content as follows: Traffic splitting in advance, where communication flow splitting involves the sender dividing large data blocks into data units of different or the same size [8], with its size determined by the granularity of the communication flow splitting, primarily divided into the following categories: First, in packet-level service splitting, a packet is the smallest component unit of a data stream and, thus, the granularity of the splitting method is the smallest, allowing independent packet probability, which can be sent to the sender; Second, flow-level traffic splitting [9], where specific target addresses are encapsulated in packet headers, and packets with the same target address are aggregated into a data flow, with each different data flow being independent and distinguished by a unique flow identifier. Flow-level splitting technology can effectively solve the impact of data distortion on multi-path transmission [10]. Third, sub-flow-level traffic splitting, where the data flow of the same destination header is divided into multiple sub-flows, and all packets within the sub-flows share the same destination address, partially solving the load imbalance issue in flow splitting algorithms. The multi-path parallel transmission architecture is shown in Figure 2. In addition, in the bandwidth aggregation architecture, the scheduling algorithm determines the business transmission method and business sub-flow scheduling sequence [11], ensuring the orderly arrival of business sub-flows at the receiving end. Next, we will discuss data scheduling.

4 Bandwidth Scheduling Scheme Formulation

For data transmission in heterogeneous networks, when the bandwidth of a certain path reaches a certain value, the network bandwidth will continue to increase, and the transmission performance will be relatively stable. To improve throughput, assigning excessive bandwidth will reduce spectrum utilization, resulting in wastage of spectrum resources. Under the current situation of increasingly tight spectrum resources, scheduling and management of bandwidth in multi-path parallel transmission can ensure the transmission performance of multi-path parallel transmission and effectively utilize resources. Therefore, processing is conducted, with the main implementation steps as follows: First, effective bandwidth estimation is performed using machine learning methods, and estimating the wireless bandwidth resources that each sub-flow can fully utilize and achieving high throughput requirements with fewer bandwidth resources is key to the bandwidth scheduling algorithm. Therefore, a coupled congestion control algorithm is employed to jointly control each sub-flow, with its expression as follows: (3) In formula (3), MSS represents the maximum segment size constant, as set by the protocol, while RTTi and PLRi respectively represent the round-trip delay and packet loss rate of the sub-flow's path. Second, parameter filtering process, given the diversity and time-varying characteristics of wireless channels, where link parameters and path effective bandwidth will undergo dynamic changes, also with errors. To eliminate errors, Kalman filter filtering is applied to network parameters to obtain accurate estimation values. Kalman filtering is a discrete-time recursive estimation algorithm that calculates a more accurate current time state as output by differential recursion at the current time, based on the measurement value of the current state, the last state, and the prediction error. In the study of discrete control systems, a linear stochastic differential equation is used as follows: (4) In formula (4), xk, xk-1 respectively represent the state parameters at time k and time k-1, Ak, Bk respectively represent system parameters, which, in multi-model systems, are matrices representing the state transition matrix and input matrix respectively, uk denotes the input parameter for control, and wk denotes noise during calculation. Third, bandwidth scheduling, assuming that a multi-path connection C contains n sub-flows, where each sub-flow is independent, occupying a path for data transmission, its scheduling process is illustrated in Figure 3. According to the above bandwidth scheduling process, a channel security protocol is finally established to ensure secure data transmission of heterogeneous data. The security protocol consists of the SSL protocol, rule establishment protocol, and tunnel information protocol. Among them, the SSL protocol primarily includes authentication algorithm and encryption algorithm, with all data packets on the server-side encrypted via the SSL protocol to ensure messaging communication security, while the rule establishment protocol involves connection information and message identification, and matching success in record tables generates a socket for forwarding data information and ensuring data forwarding and application on the VPN technology channel. OpenVPN programming is the primary method for implementing the tunnel message protocol. The client sends a request command message to establish a connection with the server. Following connection, the server writes the encrypted and validated data information into the tunnel information data area based on the SSL protocol, achieving data exchange and transmission with the client. The channel security protocol structure is shown in Figure 4. During the data transmission process, data is transmitted in accordance with the above channel security protocol to complete secure data transmission in heterogeneous networks based on machine learning.

5 Experimental Comparison

To verify the effectiveness of the designed data security transmission technology for heterogeneous networks based on Machine Learning, an experimental analysis was carried out. A comparison was made between the secure data transmission mechanism in heterogeneous networks as described in literature [1] and the data security model based on heterogeneous networks in literature [2], focusing on the effectiveness of the three systems. The experimental data set used in this experiment is shown in Table 1. It can be observed from the collected experimental data that the experimental data selection is increasing, thereby better verifying the effectiveness of the three methods, with a main focus on comparing the transmission delay, data transmission interruption situations, and packet loss rate. The specific content is as follows.

5.1 Transmission Delay Comparison

A comparison of the transmission delay of the three methods was conducted, with the results shown in Figure 5. Analyzing Figure 5 reveals that the transmission delays of the three methods are relatively small on Google's public dataset transmission. However, as the data volume increases, the transmission delay for all three methods increases. The comparison indicates that the data security transmission technology for heterogeneous networks based on Machine Learning has the smallest transmission delay, less than the traditional two methods.

5.2 Data Transmission Interruption Comparison

A comparison of the data transmission interruption situations after applying the three transmission technologies was conducted, with the results shown in Figure 6. Analyzing Figure 6 reveals that the data transmission technology used in this study resulted in the least number of interruptions in data transmission, and in several experiments, it performed better than the traditional two transmission technologies.

5.3 Packet Loss Rate Comparison

A comparison of the packet loss rate using the data security transmission technology for heterogeneous networks based on Machine Learning and the traditional two transmission technologies was conducted, with the results shown in Figure 7. Analyzing Figure 7 reveals that the secure data transmission mechanism in traditional heterogeneous networks has the highest packet loss rate, higher than both the data security model based on heterogeneous networks and the transmission technology used in this study. In summary, the data security transmission technology for heterogeneous networks based on Machine Learning has less transmission delay and a lower packet loss rate compared to the traditional two transmission technologies. This can be attributed to the pre-processing of data from heterogeneous networks and the formulation of bandwidth scheduling schemes, along with the establishment of secure transmission protocols to enhance the data transmission security effectiveness for heterogeneous networks.

6 Conclusion

This paper has designed a data security transmission technology for heterogeneous networks based on Machine Learning and verified the effectiveness of this technology through experiments. This technology improves the efficiency of data transmission and reduces the packet loss rate, demonstrating strong practical application significance. However, due to the limitations of research time, there are still some inadequacies in the data security transmission technology for heterogeneous networks, necessitating further optimization in subsequent research.

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