# Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

Authors: Kai Ji
Journal: Wireless Communications and Mobile Computing
 (2021)

---

## Abstract

Wireless personal communication network is easily affected by intrusion data in the communication process, resulting in the inability to ensure the security of personal information in wireless communication. Therefore, this paper proposes a malicious intrusion data mining algorithm based on legitimate big data in wireless personal communication networks. The clustering algorithm is used to iteratively obtain the central point of malicious intrusion data and determine its expected membership. The noise in malicious intrusion data is denoised by objective function, and the membership degree of communication data is calculated. The change factor of the neighborhood center of gravity of malicious intrusion data in wireless personal communication network is determined, the similarity between the characteristics of malicious intrusion data by using the Markov distance was determined, and the malicious intrusion data mining of wireless personal communication network supported by legal big data was completed. The experimental results show that the accuracy of mining malicious data is high and the mining time is short.


---

## Body

## 1. Introduction

With the rapid development of electronic information technology, wireless communication technology has become the most critical technology in current communication. As the rapid development of wireless communication network, personal rapid communication has become a more pillar industry [1]. Personal and external real-time communication and understanding of information need to be through wireless communication network. At present, with the explosive growth of many kinds of big data in the society, the pressure of wireless communication network is increasing. At the same time, a large number of uncertain data in the personal communication network have a certain virus, which has a great impact on personal wireless communication security [2]. Wireless communication network ensures the normal transmission and exchange of a large amount of data but also ensures the security of data. At this time, the deep mining of large quantities of data in wireless communication becomes an indispensable link. Among them, in personal communication, the interference of some unknown information leads to the intrusion of personal privacy communication data, resulting in the security of personal communication information cannot be guaranteed [3]. At present, China’s network security legal system is still in the process of step-by-step construction, but now, the network security has laws to follow [4]. Therefore, with the support of legal big data, researchers in this field have done a lot of mining for malicious intrusion data in wireless personal communication network to ensure the security of personal communication information.In reference [5], a new malicious intrusion detection data method is proposed. This method can effectively filter the malicious intrusion data according to the set communication data white list, and the operation process is relatively simple. However, the type of communication data in the white list set by this method is less, and the accuracy of malicious intrusion data mining is low. In reference [6], a distributed intrusion data detection model based on deep forest algorithm is proposed. This method has high detection accuracy for intrusion data, but it is easy to be interfered by high noise data in the process of classification, resulting in poor detection results. In reference [7], a data mining-based abnormal data detection method for optical fiber communication network is proposed. The operation process of this method is relatively simple, and the clustering accuracy of feature attributes in classification is high, but the amount of data after attribute fusion is less, so it cannot be widely used.In order to solve the shortcomings of the above methods, this paper proposes a malicious intrusion data mining algorithm for wireless personal communication networks supported by legal big data. With the help of clustering algorithm, the central point of malicious intrusion data is obtained iteratively, and the expected membership degree is determined to complete the malicious data acquisition in wireless personal communication. The noise in the malicious intrusion data is reduced by the objective function, and the membership degree of the communication data is calculated to complete the preprocessing of the intrusion data of the wireless personal communication network. The neighborhood center of gravity change factor of malicious intrusion data in wireless personal communication network is determined, the similarity degree between the features of malicious intrusion data is determined by the Markov distance, and the data features are normalized in the form of covariance matrix. The interference between malicious intrusion data was eliminated. With the help of sliding window calculation, the correlation between the features of malicious intrusion data is determined, and the nonlinear features of malicious data in different dimensions are calculated. The malicious intrusion data mining model of wireless personal communication network is constructed. The malicious intrusion data mining of wireless personal communication network supported by legal big data is completed.


## 2. Data Acquisition and Preprocessing of Malicious Intrusion in Wireless Personal Communication Network

### 2.1. Malicious Intrusion Data Collection in Wireless Personal Communication Network

China’s network industry continues to develop, and there are many types of data in the network, easy to affect the values of the masses. Accordingly, our country has carried on the legislation management to some behavior in the network, manages the network environment unceasingly, and strives to provide the more comfortable and secure network communication space for the people [8]. Therefore, before the advanced processing of malicious intrusion data in wireless personal communication network, it is necessary to collect malicious intrusion data in wireless personal communication network. In the process of personal wireless communication, the content data and virus data including communication are generated, which is malicious intrusion data. Therefore, this paper first designs a malicious intrusion data collection process, as shown in Figure 1.Figure 1
Acquisition process of malicious intrusion data.In the malicious intrusion data acquisition of the wireless personal communication network, all the personal communication data is first collected and extracted from the wireless personal communication network environment to provide the basis for the subsequent data intrusion processing. The reasonable communication data in communication data acquisition and the different data are detected accordingly, and the data determined as malicious invasion will be processed, so as to reduce the insecurity of personal communication. In order to process the malicious data, this paper first gathers the wireless personal communication network data with the help of the clustering algorithm. Malicious data acquisition was completed in wireless personal communication by continuously iterating acquiring the central point of malicious intrusion data and determining the expected relevant membership of the [9].Suppose the data set of wireless personal communication is(1)Q=q1,q2,⋯,qn.All the data in formula (1) are divided into clusters, and the central data of wireless personal communication data are set as follows:
(2)P=p1,p2,⋯,pn.The data point center in formula (2) is randomly generated in the wireless personal communication data, and because it cannot be strictly restricted, its membership should be calculated, namely
(3)W=P∑Ui.In the formula,W represents the degree of affiliation of data point center data, and Ui represents a cluster of wireless personal communication data.All communication data are classified according to the membership degree between the above wireless personal communication data. Because wireless personal communication data mainly includes two kinds of data, one is normal data and the other is malicious intrusion data [10], it is necessary to separate malicious intrusion data from all data. That is to become the focus of this study data. Through formula (4), the analysis is as follows:
(4)EX=∑ni=1UiWVj.In the formula,Vj represents fuzzy weights, and it is a constant greater than 0.According to the above analysis, the acquisition of malicious intrusion data in the wireless personal communication network is realized. Basic data support for subsequent mining is provided.


### 2.2. Preprocessing of Malicious Intrusion Data in Wireless Personal Communication Network

Because there is some noise and indistinguishable normal communication data in the malicious intrusion data of wireless personal communication network obtained above, it is necessary to preprocess the malicious intrusion data of wireless personal communication network. First, the noise in the malicious intrusion data is reduced by the objective function, that is(5)G=∑ni=1∑mj=1dijhijUi+β1−∑ni=1dij.In the formula,G represents the target function, dij represents the Gaussian noise data of the malicious intrusion data, hij represents the white noise data of the malicious intrusion data, and β represents the noise reduction factor.In the process of noise reduction of malicious intrusion data in wireless personal communication network, secondary noise reduction is needed to ensure the accurate extraction of malicious intrusion data, that is(6)Ut=μ1∑ni=1hij/dij.In the formula,μ describes the central point of malicious intrusion data.After noise reduction of the above wireless personal communication data, we should further distinguish the nonmalicious intrusion data doped in the malicious intrusion data and delete it effectively [11]. First, this type of data is set to
(7)Y=y1,y2,⋯,yn.If the membership degree is 0 or less than 0, this part of the data is deleted. If the communication data is greater than 0, the communication data is considered malicious intrusion data [12], that is
(8)R=∑ni=1dij>0∑ni=1dij<0.By judging the membership degree of nonmalicious data in malicious data, the set of malicious intrusion data is determined, and the preprocessing of wireless personal communication intrusion data is completed.


## 2.1. Malicious Intrusion Data Collection in Wireless Personal Communication Network

China’s network industry continues to develop, and there are many types of data in the network, easy to affect the values of the masses. Accordingly, our country has carried on the legislation management to some behavior in the network, manages the network environment unceasingly, and strives to provide the more comfortable and secure network communication space for the people [8]. Therefore, before the advanced processing of malicious intrusion data in wireless personal communication network, it is necessary to collect malicious intrusion data in wireless personal communication network. In the process of personal wireless communication, the content data and virus data including communication are generated, which is malicious intrusion data. Therefore, this paper first designs a malicious intrusion data collection process, as shown in Figure 1.Figure 1
Acquisition process of malicious intrusion data.In the malicious intrusion data acquisition of the wireless personal communication network, all the personal communication data is first collected and extracted from the wireless personal communication network environment to provide the basis for the subsequent data intrusion processing. The reasonable communication data in communication data acquisition and the different data are detected accordingly, and the data determined as malicious invasion will be processed, so as to reduce the insecurity of personal communication. In order to process the malicious data, this paper first gathers the wireless personal communication network data with the help of the clustering algorithm. Malicious data acquisition was completed in wireless personal communication by continuously iterating acquiring the central point of malicious intrusion data and determining the expected relevant membership of the [9].Suppose the data set of wireless personal communication is(1)Q=q1,q2,⋯,qn.All the data in formula (1) are divided into clusters, and the central data of wireless personal communication data are set as follows:
(2)P=p1,p2,⋯,pn.The data point center in formula (2) is randomly generated in the wireless personal communication data, and because it cannot be strictly restricted, its membership should be calculated, namely
(3)W=P∑Ui.In the formula,W represents the degree of affiliation of data point center data, and Ui represents a cluster of wireless personal communication data.All communication data are classified according to the membership degree between the above wireless personal communication data. Because wireless personal communication data mainly includes two kinds of data, one is normal data and the other is malicious intrusion data [10], it is necessary to separate malicious intrusion data from all data. That is to become the focus of this study data. Through formula (4), the analysis is as follows:
(4)EX=∑ni=1UiWVj.In the formula,Vj represents fuzzy weights, and it is a constant greater than 0.According to the above analysis, the acquisition of malicious intrusion data in the wireless personal communication network is realized. Basic data support for subsequent mining is provided.


## 2.2. Preprocessing of Malicious Intrusion Data in Wireless Personal Communication Network

Because there is some noise and indistinguishable normal communication data in the malicious intrusion data of wireless personal communication network obtained above, it is necessary to preprocess the malicious intrusion data of wireless personal communication network. First, the noise in the malicious intrusion data is reduced by the objective function, that is(5)G=∑ni=1∑mj=1dijhijUi+β1−∑ni=1dij.In the formula,G represents the target function, dij represents the Gaussian noise data of the malicious intrusion data, hij represents the white noise data of the malicious intrusion data, and β represents the noise reduction factor.In the process of noise reduction of malicious intrusion data in wireless personal communication network, secondary noise reduction is needed to ensure the accurate extraction of malicious intrusion data, that is(6)Ut=μ1∑ni=1hij/dij.In the formula,μ describes the central point of malicious intrusion data.After noise reduction of the above wireless personal communication data, we should further distinguish the nonmalicious intrusion data doped in the malicious intrusion data and delete it effectively [11]. First, this type of data is set to
(7)Y=y1,y2,⋯,yn.If the membership degree is 0 or less than 0, this part of the data is deleted. If the communication data is greater than 0, the communication data is considered malicious intrusion data [12], that is
(8)R=∑ni=1dij>0∑ni=1dij<0.By judging the membership degree of nonmalicious data in malicious data, the set of malicious intrusion data is determined, and the preprocessing of wireless personal communication intrusion data is completed.


## 3. Construction of Malicious Intrusion Data Mining Model for Wireless Personal Communication Network

According to the above obtained malicious intrusion data of wireless personal communication network, in order to ensure the security of wireless personal communication, the influence of malicious intrusion data is deeply processed, through the construction of wireless personal communication network.First, the neighborhood center of gravity change factor of the malicious intrusion data of the wireless personal communication network is determined to obtain the main feature of the malicious intrusion data [13].Supposeρi describes the length between the center of gravity aρ and a+1ρ of malicious intrusion data in a wireless personal communication network; then, there is
(9)ρi=Daρ+a−1ρ.At this time, the malicious data center of gravity in wireless personal communication is the largest, but because of the fluctuation of its peripheral data, the data center of gravity cannot always be kept as the maximum value [14], and the absolute difference between aρ and a+1ρ therefore needs to be calculated to obtain
(10)Δρi=Daρ−a+1ρ.At this time, it can guarantee the maximum value of malicious data center of gravity in wireless personal communication. The maximum value is quantized, and the change factor of its neighborhood center of gravity is as follows:(11)INρ=∑mi=1Δρi.The larger the value of formula (11), the more obvious the abnormal characteristics of malicious data in wireless personal communication.After determining the abnormal characteristics of malicious data in wireless personal communication, the Mahalanobis distance is used to determine the similarity between the features of malicious intrusion data, which provides more powerful evidence for its mining [15]. The Markov distance is normalized to the data features in the form of covariance matrix to eliminate the interference between malicious intrusion data [16].The time period of wireless personal communication was set ast. At this time, the feature vector of malicious intrusion data is h, its dimension is set to b, and the feature vector matrix of malicious intrusion data is determined in t−1 period as follows:
(12)Ht−1=ht−1,ht−2,⋯ht−nT.By calculating the covariance matrix, the following results are obtained:(13)KP=ht−1−hT−1.According to formula (13), the minimum value of the eigenvector is determined [17], namely
(14)minKP=disht−1−hT−1.Based on the above calculation of abnormal malicious intrusion data characteristic vector of personal wireless communication, we analyze the characteristics of malicious intrusion data feature vector distribution and set the amount of malicious intrusion data in a certain communication as follows:(15)Str=z1,z2,⋯,zi,⋯,zi+1.According to the amount of data, the sliding window is calculated to determine the correlation between the characteristics of malicious intrusion data [18], that is
(16)Zt=∑ma=1z−zi∑ma=1z−zi∑ma=1zi−zi+1.According to the characteristic correlation between the above malicious intrusion data, the malicious data is regarded as different characteristics in each dimension, and the final amount of each malicious data is determined according to the calculation of its high and low dimensions [19, 20].Based on the above analysis, the nonlinearity of malicious intrusion data is determined, and the nonlinear characteristics of malicious intrusion data are constructed by the kernel function, that is(17)s=1m∑σxθxxt.In the formula,σandθ represent the nonlinear feature of malicious data in different dimensions, respectively.On this basis, the construction of malicious intrusion data mining model of wireless personal communication network is completed, that is(18)ℵ=Ki∑mi=1Ki+s∑mj=1zt+1NKi.In the formula,Ki represents the K primary component of malicious intrusion data for wireless personal communications networks.The malicious intrusion data mining of the wireless personal communication network was completed by formula (18), and the implementation process is as shown in Figure 2.Figure 2
Flow chart of malicious intrusion data mining of wireless personal communication network.


## 4. Experimental Analysis

### 4.1. Experimental Environment

To verify the effectiveness of the mining algorithm, the experiment is realized by the simulation software, the Apple system, and the memory of 16 GB; experimental data are processed through the SPSS 13.0 software. The specific experimental environment is shown in Figure3.Figure 3
Experimental environment.


### 4.2. Design of Experimental Parameters

In order to ensure the effectiveness of the experiment, the relevant parameters used in the experiment are set up, as shown in Table1.Table 1
Experimental parameters.
ParameterDataSample communication data (GB)2Normal communication data (bar)1000Malicious communication data (bar)1000Data mining interval (s)2Data interference noise (dB)0-2Number of iterations (times)100


### 4.3. Experimental Index Design

On the basis of the above experimental environment and parameter design, the experiment is carried out by comparing the methods of this paper, reference [5], reference [6], and reference [7]. The experimental indexes are the precision of malicious intrusion data mining in wireless personal communication network, the time consumption of mining, and the error of malicious intrusion data extraction. In order to ensure the credibility of the experimental data, the experimental results are iterative 100 times, and the results are verified many times.


### 4.4. Result

#### 4.4.1. Accuracy Analysis of Different Methods of Malicious Intrusion Data Mining

The accuracy of malicious intrusion data mining in wireless personal communication network is the key index to measure the mining algorithm. The higher the value, the better the effect of mining. Therefore, results of the experimental comparison of this method and the methods from references [5], [6], and [7], to sample wireless personal communication network malicious intrusion data mining accuracy, are shown in Figure 4.Figure 4
Accuracy analysis of malicious intrusion data mining with different methods.The analysis of the experimental results in Figure4 shows that as the sample intrusion data increases, four methods have changed the accuracy of malicious intrusion data mining. When the malicious data is 800, the accuracy of this method is about 98%, about 79% of the accuracy of the method in reference [5], about 82% of the accuracy of the method in reference [6], and about 75% of the accuracy of the method in reference [7]. When the malicious data is 1600, the accuracy of this method is about 99%, about 71% of the accuracy of the method in reference [5], about 80% of the accuracy of the method in reference [6], and about 65% of the accuracy of the method in reference [7]. By comparing these four methods, we can see that the accuracy of this method is the highest. The practical effectiveness of this method is verified.


#### 4.4.2. Time-Consuming Analysis of Malicious Intrusion Data Mining by Different Methods

Based on the basis of the mining accuracy, the time consumption of malicious intrusion data mining of this method and the methods in references [5], [6], and [7] is analyzed. The results are shown in Table 2.Table 2
Time-consuming comparison of malicious intrusion data mining (s).
Sample data/barMethods of this paperMethod in reference [5]Method in reference [6]Method in reference [7]2002.32.82.52.34002.23.12.82.86002.33.23.33.18002.43.53.53.510002.53.73.83.712002.44.04.23.914002.25.14.54.216002.35.64.74.518002.45.75.14.820002.36.15.65.2As can be seen from the data in Table2, with the increase of sample data, the four methods have changed the mining time of malicious intrusion data. When the sample data is 600, about 2.3 s is consumed by the mining time in this method. The mining time of the method in reference [5] is about 3.2 s. The mining time of the method in reference [6] is about 3.3 s. The mining time of the method in reference [7] is about 3.1 s. When the sample data is 1200, about 2.4 s of the mining time is taken. The mining time of the method in reference [5] is about 4.0 s. The mining time of the method in reference [6] is about 4.2 s. The mining time of the method in reference [7] method is 3.9 s. When the sample data is 2000, about 2.3 s of mining time is consumed in this method. The mining time of the method in reference [5] is about 6.1 s. The mining time of the method in reference [6] is 5.6 s. The mining time of the method in reference [7] is about 5.2 s. The analysis of the data shows that in the four methods of sample data mining, the time-use change of this method is stable, and the other three methods of mining time are on the rise. The effectiveness of this method is verified.


#### 4.4.3. Error Analysis of Malicious Intrusion Data Extraction by Different Methods

In order to further reflect the reliability of the proposed method, the error analysis of the present method and the methods in references [5] and [6] and sample malicious intrusion data extraction in the method in reference [7] are analyzed. The results are shown in Figure 5.Figure 5
Error analysis of malicious intrusion data extraction by different methods.According to the experimental results in Figure5, there are some differences in the error of extracting malicious intrusion data by four methods. With the increase of sample data, the extraction errors of the four methods show a downward trend. And convergence is also increasing. Among them, the minimum error of this method is about 0.2% [5], the minimum error in extracting malicious intrusion data is about 1.6% [6], the minimum error in extracting malicious intrusion data is about 0.8% [7], and the minimum error in extracting malicious intrusion data is about 0.6%. Compared with the other four methods, this method has increased by about 1.4%, 0.6%, and 0.4%, respectively, The proposed method is more effective. It is verified that the effectiveness of this method is better in these four methods. The control of malicious intrusion data in personal communication will be better and more advantageous.


## 4.1. Experimental Environment

To verify the effectiveness of the mining algorithm, the experiment is realized by the simulation software, the Apple system, and the memory of 16 GB; experimental data are processed through the SPSS 13.0 software. The specific experimental environment is shown in Figure3.Figure 3
Experimental environment.


## 4.2. Design of Experimental Parameters

In order to ensure the effectiveness of the experiment, the relevant parameters used in the experiment are set up, as shown in Table1.Table 1
Experimental parameters.
ParameterDataSample communication data (GB)2Normal communication data (bar)1000Malicious communication data (bar)1000Data mining interval (s)2Data interference noise (dB)0-2Number of iterations (times)100


## 4.3. Experimental Index Design

On the basis of the above experimental environment and parameter design, the experiment is carried out by comparing the methods of this paper, reference [5], reference [6], and reference [7]. The experimental indexes are the precision of malicious intrusion data mining in wireless personal communication network, the time consumption of mining, and the error of malicious intrusion data extraction. In order to ensure the credibility of the experimental data, the experimental results are iterative 100 times, and the results are verified many times.


## 4.4. Result

### 4.4.1. Accuracy Analysis of Different Methods of Malicious Intrusion Data Mining

The accuracy of malicious intrusion data mining in wireless personal communication network is the key index to measure the mining algorithm. The higher the value, the better the effect of mining. Therefore, results of the experimental comparison of this method and the methods from references [5], [6], and [7], to sample wireless personal communication network malicious intrusion data mining accuracy, are shown in Figure 4.Figure 4
Accuracy analysis of malicious intrusion data mining with different methods.The analysis of the experimental results in Figure4 shows that as the sample intrusion data increases, four methods have changed the accuracy of malicious intrusion data mining. When the malicious data is 800, the accuracy of this method is about 98%, about 79% of the accuracy of the method in reference [5], about 82% of the accuracy of the method in reference [6], and about 75% of the accuracy of the method in reference [7]. When the malicious data is 1600, the accuracy of this method is about 99%, about 71% of the accuracy of the method in reference [5], about 80% of the accuracy of the method in reference [6], and about 65% of the accuracy of the method in reference [7]. By comparing these four methods, we can see that the accuracy of this method is the highest. The practical effectiveness of this method is verified.


### 4.4.2. Time-Consuming Analysis of Malicious Intrusion Data Mining by Different Methods

Based on the basis of the mining accuracy, the time consumption of malicious intrusion data mining of this method and the methods in references [5], [6], and [7] is analyzed. The results are shown in Table 2.Table 2
Time-consuming comparison of malicious intrusion data mining (s).
Sample data/barMethods of this paperMethod in reference [5]Method in reference [6]Method in reference [7]2002.32.82.52.34002.23.12.82.86002.33.23.33.18002.43.53.53.510002.53.73.83.712002.44.04.23.914002.25.14.54.216002.35.64.74.518002.45.75.14.820002.36.15.65.2As can be seen from the data in Table2, with the increase of sample data, the four methods have changed the mining time of malicious intrusion data. When the sample data is 600, about 2.3 s is consumed by the mining time in this method. The mining time of the method in reference [5] is about 3.2 s. The mining time of the method in reference [6] is about 3.3 s. The mining time of the method in reference [7] is about 3.1 s. When the sample data is 1200, about 2.4 s of the mining time is taken. The mining time of the method in reference [5] is about 4.0 s. The mining time of the method in reference [6] is about 4.2 s. The mining time of the method in reference [7] method is 3.9 s. When the sample data is 2000, about 2.3 s of mining time is consumed in this method. The mining time of the method in reference [5] is about 6.1 s. The mining time of the method in reference [6] is 5.6 s. The mining time of the method in reference [7] is about 5.2 s. The analysis of the data shows that in the four methods of sample data mining, the time-use change of this method is stable, and the other three methods of mining time are on the rise. The effectiveness of this method is verified.


### 4.4.3. Error Analysis of Malicious Intrusion Data Extraction by Different Methods

In order to further reflect the reliability of the proposed method, the error analysis of the present method and the methods in references [5] and [6] and sample malicious intrusion data extraction in the method in reference [7] are analyzed. The results are shown in Figure 5.Figure 5
Error analysis of malicious intrusion data extraction by different methods.According to the experimental results in Figure5, there are some differences in the error of extracting malicious intrusion data by four methods. With the increase of sample data, the extraction errors of the four methods show a downward trend. And convergence is also increasing. Among them, the minimum error of this method is about 0.2% [5], the minimum error in extracting malicious intrusion data is about 1.6% [6], the minimum error in extracting malicious intrusion data is about 0.8% [7], and the minimum error in extracting malicious intrusion data is about 0.6%. Compared with the other four methods, this method has increased by about 1.4%, 0.6%, and 0.4%, respectively, The proposed method is more effective. It is verified that the effectiveness of this method is better in these four methods. The control of malicious intrusion data in personal communication will be better and more advantageous.


## 4.4.1. Accuracy Analysis of Different Methods of Malicious Intrusion Data Mining

The accuracy of malicious intrusion data mining in wireless personal communication network is the key index to measure the mining algorithm. The higher the value, the better the effect of mining. Therefore, results of the experimental comparison of this method and the methods from references [5], [6], and [7], to sample wireless personal communication network malicious intrusion data mining accuracy, are shown in Figure 4.Figure 4
Accuracy analysis of malicious intrusion data mining with different methods.The analysis of the experimental results in Figure4 shows that as the sample intrusion data increases, four methods have changed the accuracy of malicious intrusion data mining. When the malicious data is 800, the accuracy of this method is about 98%, about 79% of the accuracy of the method in reference [5], about 82% of the accuracy of the method in reference [6], and about 75% of the accuracy of the method in reference [7]. When the malicious data is 1600, the accuracy of this method is about 99%, about 71% of the accuracy of the method in reference [5], about 80% of the accuracy of the method in reference [6], and about 65% of the accuracy of the method in reference [7]. By comparing these four methods, we can see that the accuracy of this method is the highest. The practical effectiveness of this method is verified.


## 4.4.2. Time-Consuming Analysis of Malicious Intrusion Data Mining by Different Methods

Based on the basis of the mining accuracy, the time consumption of malicious intrusion data mining of this method and the methods in references [5], [6], and [7] is analyzed. The results are shown in Table 2.Table 2
Time-consuming comparison of malicious intrusion data mining (s).
Sample data/barMethods of this paperMethod in reference [5]Method in reference [6]Method in reference [7]2002.32.82.52.34002.23.12.82.86002.33.23.33.18002.43.53.53.510002.53.73.83.712002.44.04.23.914002.25.14.54.216002.35.64.74.518002.45.75.14.820002.36.15.65.2As can be seen from the data in Table2, with the increase of sample data, the four methods have changed the mining time of malicious intrusion data. When the sample data is 600, about 2.3 s is consumed by the mining time in this method. The mining time of the method in reference [5] is about 3.2 s. The mining time of the method in reference [6] is about 3.3 s. The mining time of the method in reference [7] is about 3.1 s. When the sample data is 1200, about 2.4 s of the mining time is taken. The mining time of the method in reference [5] is about 4.0 s. The mining time of the method in reference [6] is about 4.2 s. The mining time of the method in reference [7] method is 3.9 s. When the sample data is 2000, about 2.3 s of mining time is consumed in this method. The mining time of the method in reference [5] is about 6.1 s. The mining time of the method in reference [6] is 5.6 s. The mining time of the method in reference [7] is about 5.2 s. The analysis of the data shows that in the four methods of sample data mining, the time-use change of this method is stable, and the other three methods of mining time are on the rise. The effectiveness of this method is verified.


## 4.4.3. Error Analysis of Malicious Intrusion Data Extraction by Different Methods

In order to further reflect the reliability of the proposed method, the error analysis of the present method and the methods in references [5] and [6] and sample malicious intrusion data extraction in the method in reference [7] are analyzed. The results are shown in Figure 5.Figure 5
Error analysis of malicious intrusion data extraction by different methods.According to the experimental results in Figure5, there are some differences in the error of extracting malicious intrusion data by four methods. With the increase of sample data, the extraction errors of the four methods show a downward trend. And convergence is also increasing. Among them, the minimum error of this method is about 0.2% [5], the minimum error in extracting malicious intrusion data is about 1.6% [6], the minimum error in extracting malicious intrusion data is about 0.8% [7], and the minimum error in extracting malicious intrusion data is about 0.6%. Compared with the other four methods, this method has increased by about 1.4%, 0.6%, and 0.4%, respectively, The proposed method is more effective. It is verified that the effectiveness of this method is better in these four methods. The control of malicious intrusion data in personal communication will be better and more advantageous.


## 5. Conclusion

This paper proposes a malicious intrusion data mining algorithm for wireless personal communication network supported by legal big data. With the help of the calculation of the sliding window to determine the correlation between the characteristics of malicious intrusion data and to calculate the nonlinear characteristics of malicious data in different dimensions, the malicious intrusion data mining model of wireless personal communication network was built, and the malicious intrusion data mining of wireless personal communication network supported by legal big data was competed. This method has the following advantages:(1)
The highest accuracy of mining malicious data using the proposed method is about 98%, with a certain reliability(2)
The proposed method is the shortest time-consuming to mining malicious data and improves the efficiency of the algorithm(3)
The proposed method shows the low extraction error of malicious data.



---

*Source: 8321636-2021-09-25.xml*