# The Application of Artificial Intelligence Oriented to the Internet of Things Technology in the Complexity of Digital Crossborder Trade

Authors: Hongqing Liu; Ting Liu
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

With the continuous update of technologies such as cloud computing, big data, AI, and 5G, various IoT services have also been developed. Various IoT terminals have emerged one after another, and the number of access terminals has also risen sharply. With the development and popularization of a new generation of artificial intelligence technology, the Internet of Things mainly connects various objects to each other in an agreed manner through various sensing devices and conducts wireless communication and data exchange to achieve object-to-object communication, positioning, traceability, intelligent identification, and management. The Internet of Things has broad application prospects in many aspects such as intelligent robots, transportation, and intelligent buildings. Accurate node positioning is considered to be the focus of the application of the Internet of Things in geographic and network location services. More and more experts and scholars have begun to worry about the future development of artificial intelligence technology. While artificial intelligence technology provides convenience to the society, it may also cause huge hidden dangers to the public safety of the entire society. Therefore, it is necessary to make decisions about the technical risks of artificial intelligence. Philosophical analysis has certain historical, practical significance, and theoretical value. Internet technology innovation and digital technology innovation continue to emerge. Digital trade has become a typical feature of the current economic structural transformation, and it is also a new driving force for countries to participate in the division of labor in the global value chain. At present, the scale of global digital trade continues to expand, but there are still many restrictions on digital trade rules. Digital trade tariff barriers and nontariff barriers in digital trade such as crossborder data flow restrictions, intellectual property infringements, and personal information protection are bound to have an impact on the global value chain division of labor system. IoT technology-oriented artificial intelligence accelerates the logistics speed and tracking efficiency of digital cross-border trade, improves consumers’ willingness to spend, and promotes the development of the entire e-commerce trade field.


---

## Body

## 1. Introduction

The existing access control algorithm of Internet of things is relatively single, and it cannot adaptively choose the most suitable access method according to the characteristics of access services. When facing the increasing types of services, the decline of access success rate and the rise of access delay cannot be avoided. In many application scenarios, it is of practical value to obtain the correct and effective location data information connected to a node and the effective location data related to other location service systems [1, 2]. Therefore, the research on the geographic location technology of Internet of things has not only strong economic and social application value but also great commercial value. It is of great significance to realize information service based on global precise location and improve the information quality of modern people’s daily life. It makes full use of the flexibility of terminal access in the overlapping of heterogeneous networks. A new access control algorithm based on dynamic load transmission is proposed. This algorithm uses the transmission advantage when the initial access fails. The first level evaluation algorithm selects the highest priority services for load transmission and gives access resources for the newly arrived access applications, so as to improve the access success rate and the total throughput of the system [3, 4]. As a new technology, artificial intelligence technology will have a great impact on the whole society because of its special potential. Firstly, the basic connotation and function characteristics of artificial intelligence are defined and distinguished by the definition and distinction of the two concepts. Secondly, the paper makes a preliminary study on the root of the risk of AI-related technology from the theory of technology ontology, cognitive view, and society and clarifies that the risk of AI-related technology is the internal attribute of it, and it is also the inevitable risk that we human beings suffer in the process of invention and popularization of related technology. In terms of digital trade barriers, the proposal of tariff exemption for electronic transmission under WTO has high instability in practical application; nontariff barriers such as crossborder data flow, infringement of intellectual property rights, and personal data protection still restrict the development of digital trade. In the aspect of digital trade rules construction, in order to maintain the core interests in the digital trade market, famous “American model” and “European model” have been constructed. Although there is a lack of experience in the formulation of digital trade rules, preliminary attempts have been made at present. The main model of digital economic trade is the agreement between multilateral and bilateral trade models. Firstly, the rules of the digital economic trade model among three different regions, namely, cptpp, TTIP, and HSA, are analyzed through case study, Then, the paper introduces the European bilateral economic and trade cooperation agreements that China needs to participate in Europe, the bilateral economic and trade cooperation agreements of Europe that the United States needs to participate in the whole Europe, and the European bilateral economic and trade cooperation agreements that the EU needs to participate in the whole Europe. Finally, the paper analyzes and compares all the rules and cases of digital economic and trade models. This paper first describes the background of the research and the current research status of predecessors and then analyzes the relevant theories of IoT technology, including the analysis of IoT terminal access scenarios, system models, and the MAC layer protocol based on the combination of random access and ACB mechanisms. Afterwards, the application of artificial intelligence in the complexity of digital crossborder trade is mainly based on the experimental analysis of artificial intelligence-based digital crossborder trade network anomaly detection, and finally, the complexity of digital cross-border trade is verified.


## 2. Related Work

This technology used to be limited to the exchange of information between people, broadening the exchange of information between things and people, events, and life, making the future network more intelligent, and realizing the interconnection of all things [5, 6]. As an important branch of mobile Internet technology, the Internet of Things technology closely combines various information sensor devices and mobile Internet related technologies and directly completes the transmission and sharing of data via the mobile Internet, using electronic computers and other technologies, means to directly realize the processing and application of relevant information and data. This technology has once been limited to the information exchange between people, has broadened the information exchange between things and people, events, and life, makes the future network more intelligent, and realizes the interconnection of everything. The Internet of Things technology has broad application prospects and a wide range of applications in the world, involving applications in multiple fields such as smart home, smart medical, urban construction, factory manufacturing, government office, environmental management, and food safety. Literature [7–9]. In recent years, there has been another upsurge in artificial intelligence technology research. Artificial intelligence technology has been widely used in more and more industries. Unmanned driving has guided the transportation industry and brought some new changes in travel. The use of database and algorithm recognition technology has helped the police quickly catch criminals. Intelligent robot technology is expected to completely solve the problem of human resource allocation in the medical field and industry. Artificial intelligence technology has entered every aspect of life and will. The future social development will bring about a huge change. Not only that, the United States, Japan, the United Kingdom, and China, and other world science and technology powers have all begun to join the wave of artificial intelligence research and have issued relevant national strategic plans, raising my country’s artificial intelligence construction to the national strategic level. As artificial intelligence is currently in a state of rapid progress and development, scientists want more to research and develop an intelligent system that can completely think independently, so as to realize the simulation of human intelligence. Literature [10–12] The digital revolution promoted by the Internet era has not only created a new way of communication and communication but also produced a model of information resource sharing. Business models and new labor have become the source of employment growth. However, the existing system lags behind the specific practical requirements, which has led us to a series of trade disputes in the international market. Although many countries in the world have been deeply aware of the significance and importance of digital trade for economic development and social growth, in order to protect their own national security and personal data information security, countries have begun to consider setting up trade barriers. Due to the uneven digital trade exchanges between countries, one after another has actively turned to signing bilateral and multilateral trade agreements in order to be able to more comprehensively solve these digital trade issues. Literature [13–15]. The United States has always been a major country in global digital trade. After the United States was seriously defeated in pursuing the liberalization of digital trade under WTO rules, it turned to bilateral economic and trade negotiations, such as the United States and South Korea. With its own advantages, it has signed negotiations on the content of digital trade in trade agreements with some countries. In order to promote the multilateral economic and trade negotiations led by other countries, the United States has set up a special e-commerce chapter to try to promote digital trade liquidity to satisfy its own interest requirements.


## 3. Related Theories of IoT Technology

### 3.1. Analysis of IoT Terminal Access Scenarios

#### 3.1.1. Scenario Introduction

This chapter considers the deployment scenario of a single eNB and a large number of terminals, there are only M2M users in the cell, and H2H users are not considered. The LTE standard clearly states that in a specific time slot, when two or more users select the same MTC preamble, they may conflict with it, but if two users transmit signals between them. If the delay difference is small, the eNB will treat multiple MTC devices as one MTC device and send them the same RAR signal and message, respectively [16–18]. In this case, the conflict problem can only be solved in the subsequent steps. In this chapter, it is assumed that the eNB can always detect whether there is a conflict between them when receiving a preamble, so as long as two or more MCT devices in the MCT have selected the same preamble, and they can be identified. It is a conflict, and the access operation in this time slot has failed.The access scenario described in this chapter is shown in Figure1. In this scenario, there is an eNB that covers a certain range. A total of N MTC devices arrive in the area according to the designated access flow model and initiate an access application within the specified access time, and the access algorithm runs on the eNB.Figure 1
Schematic diagram of access scenario.


#### 3.1.2. User Access Flow Model

In the Internet of Things environment, the types of services are numerous and complex, and the total amount of access and arrival rate of each type of service are also different.One-time arrival: in the daily access process, applications that arrive uniformly often do not cause congestion. Only the influx of a large number of access applications in a short period of time will cause serious congestion in the system [19, 20]. This method has relatively little usage in some documents but it is often widely used for qualitative analysis of system access algorithms, such as the impact of values obtained by different preamble codes on the performance of the entire system access algorithm.Arrival by access flow: this type of method is that a certain number of access applications are continuously reached within a period of time, and the specific arrival amount of each time slot can meet different distribution rates [21, 22]. This type of access flow model is more in line with the actual access situation and is more dynamic and continuous than the first type of access flow model.The method function of the Poisson distribution is a common random discrete probability distribution [23]. The Poisson distribution represents the average number of times that each random probability event occurs in a certain unit and specific time. The method of Poisson distribution is particularly suitable for describing the possible vibration frequency and number of occurrences of a random event within a certain period of time, and its probability function is as (1)
(1)PX=k=λkk!e−λ,k=0,1,2,⋯Because if the event arrival rate obeys the Poisson distribution with a parameter ofλ, then its arrival time interval obeys the exponential distribution with a parameter of 1/λ.The uniform probability distribution is also called the rectangular distribution. This flow model is often used to calculate and simulate the business of reporting information regularly over a period of time. The distribution probability function is defined as (2)
(2)fx=1b−a,a<x<b0,else.The beta distribution is the density function of the conjugate prior distribution correlation between the Bernoulli distribution and the binomial distribution. Its probability density function is as (3)
(3)fx,α,β=xα−1⋅1−xβ−1∫01uα−1⋅1−uβ−1=1Bα,β⋅xα−1⋅1−xβ−1.The random variableX obeys the beta distribution with parameters α and β and denoted as XBeα,β. With the difference of α and β, its probability density function is also very different.According to 3GPP, the commonly used parameters of beta distribution access flow areα=3 and β=4. The access flow distribution model when the number of time slots is 2000, and the total number of accesses is 30,000 using this parameter.The beta distribution access flow model is often used in some emergencies. In this scenario, a large number of access applications are flooded in a short period of time [24]. For example, when an emergency occurs or a large area of MTC fails, it accesses at a certain moment nearby. There has been an explosive growth in the number of applications, and when the incident has passed or the fault has been eliminated, the number of access applications has gradually stabilized. The beta distributed access flow model is currently the most widely used model in academic literature for research on access flow control algorithms.To sum up, in the large-scale access scenario of the Internet of Things, it is suitable to adopt the uniform distribution or Poisson distribution access flow model for periodic services such as the timing report of sensor data and for sudden services such as alarms. The business is suitable for adopting the beta distribution access flow model [25].


### 3.2. System Model

Aiming at the coexistence of large data volume services and small data volume services in M2M communication in the Internet of Things, an access control algorithm based on time slot Aloha and adaptive ACB is proposed. The algorithm has two stages [26]. The first stage is statistically first. The usage of the preamble of an access cycle and the idle rate of competing access physical resource blocks then use the prediction algorithm to predict the number of access applications for the next time slot; the second stage, according to the predicted value of the number of applications, and access to the next time slot, the maximum number of successes is the principle [27]. Adjust the preamble grouping threshold and ACB control parameters, then identify the newly arrived service type according to the QoS requirements and the size of the data volume, and use the parameters obtained above to use different types of services separately Aloha or adaptive ACB method for access. Finally, because the access algorithm involves two different access methods, the existing MAC protocol cannot carry the algorithm. In order to cooperate with the algorithm, a MAC layer protocol based on a mixture of random access and ACB mechanisms is also proposed.


### 3.3. MAC Layer Protocol Based on a Mixture of Random Access and ACB Mechanism

The ACB algorithm is a two-step access algorithm and requires additional signaling to select the preamble. This section simplifies the signaling for the access of small data volume services and adopts the adaptive ACB mechanism for large data volume services. In order to cooperate with this hybrid access algorithm, this section proposes a MAC layer protocol that combines random access and data bearer [28]. The protocol divides the access resources of each access cycle iT into 4 parts. The first part is used to broadcast the necessary access information such as the current time slot access resources and ACB control parameters; the second part is used for random access the preamble allocation; the third part is used for the one-step access of the time slot Aloha; the fourth part is the data bearer part, which is used for data transmission by the device that successfully obtains the preamble in random access, and the improved MAC layer. The agreement is shown in Figure 2.Figure 2
Schematic diagram of hybrid MAC protocol.The main difference between this protocol and the existing LTE access protocol is that the time slot Aloha access method is adopted for the services that are not sensitive to delay, and the effective data volume is small, while for the large data volume or delay-sensitive services, the access method is based on random access mode of ACB [29]. The protocol effectively avoids the problems of a sharp increase in signaling consumption and a decrease in overall system performance caused by the ACB mechanism for small data services, and it also ensures the needs of large data services and high-priority services.


#### 3.3.1. The First Stage: Application Volume Forecasting Algorithm Based on Time Series Forecasting

Aiming at the coexistence of large data volume services and small data volume services in M2M communication in the Internet of Things, this chapter proposes an access control algorithm based on the combination of time-slot Aloha and adaptive ACB and proposes an access application volume based on time series prediction Forecasting algorithm.For services that adopt the adaptive ACB random access method, the amount of applications is estimated according to the state of the preamble in the ACB mechanism [30].Usesi to represent the state of the i-th preamble, where si=0 indicates that the preamble is not selected; that is, it is in an idle state, and si=1 indicates that the preamble is just selected by an MTC device; that is, the device can be the fourth stage transmits data. This state is called the successful state. si>1 means that two or more devices have selected the preamble and are detected as a conflict state. The probability that the i-th preamble is in these three states for
(4)PxjNα=nα=1−1Npnα,si=0Cnα1⋅1Np1−1Npnα−1=nαNp⋅1−1Npnα−1,si=1,1−1−1Npnα−nαNp⋅1−1Npnα−1,si≥2.Then, use the maximum likelihood estimation formula:(5)P=Psi=0Nαu1⋅Psi=1Nαu2⋅Psi≥2Nαu3.Let this formula take the maximum value, which is(6)Nα=argmax∑jJlnPsjNα.For services that use the Aloha contention access method of time slot, estimation is made according to the state of the physical resource block in the current time slot. The specific steps are as follows (7):
(7)Ci=logPidke,ilogNi−1Ni.The predicted value of the number of access requests for thei+1th time slot is
(8)Ni+1=Ni−Si+Fi+1+newi+1,i≤IA,Ni−Si+Fi+1,i>IA.Because the amount of access requests belongs to a time series, the weighted sum of historical increments can be used as the increment of the next time slot, and then(9)newi+1=max0,0.6∗newi+0.3∗newi−1+0.1∗newi−1=max0,0.6Ni−0.3Ni−1−0.2Ni−2−0.1Ni−3−0.6Fi−0.3Fi−1−0.1Fi−2+0.6Si−1+0.3Si−2+0.1Si−3.Therefore, the predicted value of the number of access requests in the next time slot is expressed as(10)Ni+1=maxNi1.6Ni−0.3Ni−1−0.2Ni−2−0.1Ni−3−0.6Fi−0.3Fi−1−0.1Fi−2+0.6Si−1+0.3Si−2+0.1Si−3−Si,i≤IANi−Si+Fi+1,i>IA.This prediction algorithm is basically consistent with the actual number of applications for access.


#### 3.3.2. Second Stage: Parameter Selection Based on Maximum Expectations

After predicting the number of requests for access to the next time slot in the first stage, based on the principle of maximizing the number of successful access in the next time slot, the ACB control parameter (a) and the optimal solution of the preamble grouping threshold are discussed.Becausesi=1 indicates that the preamble transmission is successful, the expected value of the successful transmission preamble is calculated as (11)
(11)ENsNα=nα=∑i=1NρPsi=1Nα=nα=Np⋅Cnα1⋅1Np⋅1−1Npnα−1=nα⋅1−1Npnα−1.When there are a total ofN users, the probability of Na users passing the screening is
(12)PNα=nαN=n=Cnnα⋅anα⋅1−an−nα.In summary, for a total ofN users, the expected number of successful accesses is
(13)ENsN=n=EPNα=nαN=n⋅EN=n=∑na−1nNα=nαN=n⋅na⋅1−1Nna−1=∑na−1nCnnα⋅anα⋅1−an−nα⋅na⋅1−1Nna−1=n⋅a⋅1−aNpna−1.The optimal ACB control parameters derived froma are
(14)aopt=Jn.According to the conclusion in formula (14), it is easy to calculate that when the number of access applications is approximately equal to the number of available preambles for the current service, the expected number of successful access times is the highest, so 1S is selected as the current time slot high priority service. After imitating the number of preamble codes of 30, 54, and 70, respectively, this conclusion has also been verified.


## 3.1. Analysis of IoT Terminal Access Scenarios

### 3.1.1. Scenario Introduction

This chapter considers the deployment scenario of a single eNB and a large number of terminals, there are only M2M users in the cell, and H2H users are not considered. The LTE standard clearly states that in a specific time slot, when two or more users select the same MTC preamble, they may conflict with it, but if two users transmit signals between them. If the delay difference is small, the eNB will treat multiple MTC devices as one MTC device and send them the same RAR signal and message, respectively [16–18]. In this case, the conflict problem can only be solved in the subsequent steps. In this chapter, it is assumed that the eNB can always detect whether there is a conflict between them when receiving a preamble, so as long as two or more MCT devices in the MCT have selected the same preamble, and they can be identified. It is a conflict, and the access operation in this time slot has failed.The access scenario described in this chapter is shown in Figure1. In this scenario, there is an eNB that covers a certain range. A total of N MTC devices arrive in the area according to the designated access flow model and initiate an access application within the specified access time, and the access algorithm runs on the eNB.Figure 1
Schematic diagram of access scenario.


### 3.1.2. User Access Flow Model

In the Internet of Things environment, the types of services are numerous and complex, and the total amount of access and arrival rate of each type of service are also different.One-time arrival: in the daily access process, applications that arrive uniformly often do not cause congestion. Only the influx of a large number of access applications in a short period of time will cause serious congestion in the system [19, 20]. This method has relatively little usage in some documents but it is often widely used for qualitative analysis of system access algorithms, such as the impact of values obtained by different preamble codes on the performance of the entire system access algorithm.Arrival by access flow: this type of method is that a certain number of access applications are continuously reached within a period of time, and the specific arrival amount of each time slot can meet different distribution rates [21, 22]. This type of access flow model is more in line with the actual access situation and is more dynamic and continuous than the first type of access flow model.The method function of the Poisson distribution is a common random discrete probability distribution [23]. The Poisson distribution represents the average number of times that each random probability event occurs in a certain unit and specific time. The method of Poisson distribution is particularly suitable for describing the possible vibration frequency and number of occurrences of a random event within a certain period of time, and its probability function is as (1)
(1)PX=k=λkk!e−λ,k=0,1,2,⋯Because if the event arrival rate obeys the Poisson distribution with a parameter ofλ, then its arrival time interval obeys the exponential distribution with a parameter of 1/λ.The uniform probability distribution is also called the rectangular distribution. This flow model is often used to calculate and simulate the business of reporting information regularly over a period of time. The distribution probability function is defined as (2)
(2)fx=1b−a,a<x<b0,else.The beta distribution is the density function of the conjugate prior distribution correlation between the Bernoulli distribution and the binomial distribution. Its probability density function is as (3)
(3)fx,α,β=xα−1⋅1−xβ−1∫01uα−1⋅1−uβ−1=1Bα,β⋅xα−1⋅1−xβ−1.The random variableX obeys the beta distribution with parameters α and β and denoted as XBeα,β. With the difference of α and β, its probability density function is also very different.According to 3GPP, the commonly used parameters of beta distribution access flow areα=3 and β=4. The access flow distribution model when the number of time slots is 2000, and the total number of accesses is 30,000 using this parameter.The beta distribution access flow model is often used in some emergencies. In this scenario, a large number of access applications are flooded in a short period of time [24]. For example, when an emergency occurs or a large area of MTC fails, it accesses at a certain moment nearby. There has been an explosive growth in the number of applications, and when the incident has passed or the fault has been eliminated, the number of access applications has gradually stabilized. The beta distributed access flow model is currently the most widely used model in academic literature for research on access flow control algorithms.To sum up, in the large-scale access scenario of the Internet of Things, it is suitable to adopt the uniform distribution or Poisson distribution access flow model for periodic services such as the timing report of sensor data and for sudden services such as alarms. The business is suitable for adopting the beta distribution access flow model [25].


## 3.1.1. Scenario Introduction

This chapter considers the deployment scenario of a single eNB and a large number of terminals, there are only M2M users in the cell, and H2H users are not considered. The LTE standard clearly states that in a specific time slot, when two or more users select the same MTC preamble, they may conflict with it, but if two users transmit signals between them. If the delay difference is small, the eNB will treat multiple MTC devices as one MTC device and send them the same RAR signal and message, respectively [16–18]. In this case, the conflict problem can only be solved in the subsequent steps. In this chapter, it is assumed that the eNB can always detect whether there is a conflict between them when receiving a preamble, so as long as two or more MCT devices in the MCT have selected the same preamble, and they can be identified. It is a conflict, and the access operation in this time slot has failed.The access scenario described in this chapter is shown in Figure1. In this scenario, there is an eNB that covers a certain range. A total of N MTC devices arrive in the area according to the designated access flow model and initiate an access application within the specified access time, and the access algorithm runs on the eNB.Figure 1
Schematic diagram of access scenario.


## 3.1.2. User Access Flow Model

In the Internet of Things environment, the types of services are numerous and complex, and the total amount of access and arrival rate of each type of service are also different.One-time arrival: in the daily access process, applications that arrive uniformly often do not cause congestion. Only the influx of a large number of access applications in a short period of time will cause serious congestion in the system [19, 20]. This method has relatively little usage in some documents but it is often widely used for qualitative analysis of system access algorithms, such as the impact of values obtained by different preamble codes on the performance of the entire system access algorithm.Arrival by access flow: this type of method is that a certain number of access applications are continuously reached within a period of time, and the specific arrival amount of each time slot can meet different distribution rates [21, 22]. This type of access flow model is more in line with the actual access situation and is more dynamic and continuous than the first type of access flow model.The method function of the Poisson distribution is a common random discrete probability distribution [23]. The Poisson distribution represents the average number of times that each random probability event occurs in a certain unit and specific time. The method of Poisson distribution is particularly suitable for describing the possible vibration frequency and number of occurrences of a random event within a certain period of time, and its probability function is as (1)
(1)PX=k=λkk!e−λ,k=0,1,2,⋯Because if the event arrival rate obeys the Poisson distribution with a parameter ofλ, then its arrival time interval obeys the exponential distribution with a parameter of 1/λ.The uniform probability distribution is also called the rectangular distribution. This flow model is often used to calculate and simulate the business of reporting information regularly over a period of time. The distribution probability function is defined as (2)
(2)fx=1b−a,a<x<b0,else.The beta distribution is the density function of the conjugate prior distribution correlation between the Bernoulli distribution and the binomial distribution. Its probability density function is as (3)
(3)fx,α,β=xα−1⋅1−xβ−1∫01uα−1⋅1−uβ−1=1Bα,β⋅xα−1⋅1−xβ−1.The random variableX obeys the beta distribution with parameters α and β and denoted as XBeα,β. With the difference of α and β, its probability density function is also very different.According to 3GPP, the commonly used parameters of beta distribution access flow areα=3 and β=4. The access flow distribution model when the number of time slots is 2000, and the total number of accesses is 30,000 using this parameter.The beta distribution access flow model is often used in some emergencies. In this scenario, a large number of access applications are flooded in a short period of time [24]. For example, when an emergency occurs or a large area of MTC fails, it accesses at a certain moment nearby. There has been an explosive growth in the number of applications, and when the incident has passed or the fault has been eliminated, the number of access applications has gradually stabilized. The beta distributed access flow model is currently the most widely used model in academic literature for research on access flow control algorithms.To sum up, in the large-scale access scenario of the Internet of Things, it is suitable to adopt the uniform distribution or Poisson distribution access flow model for periodic services such as the timing report of sensor data and for sudden services such as alarms. The business is suitable for adopting the beta distribution access flow model [25].


## 3.2. System Model

Aiming at the coexistence of large data volume services and small data volume services in M2M communication in the Internet of Things, an access control algorithm based on time slot Aloha and adaptive ACB is proposed. The algorithm has two stages [26]. The first stage is statistically first. The usage of the preamble of an access cycle and the idle rate of competing access physical resource blocks then use the prediction algorithm to predict the number of access applications for the next time slot; the second stage, according to the predicted value of the number of applications, and access to the next time slot, the maximum number of successes is the principle [27]. Adjust the preamble grouping threshold and ACB control parameters, then identify the newly arrived service type according to the QoS requirements and the size of the data volume, and use the parameters obtained above to use different types of services separately Aloha or adaptive ACB method for access. Finally, because the access algorithm involves two different access methods, the existing MAC protocol cannot carry the algorithm. In order to cooperate with the algorithm, a MAC layer protocol based on a mixture of random access and ACB mechanisms is also proposed.


## 3.3. MAC Layer Protocol Based on a Mixture of Random Access and ACB Mechanism

The ACB algorithm is a two-step access algorithm and requires additional signaling to select the preamble. This section simplifies the signaling for the access of small data volume services and adopts the adaptive ACB mechanism for large data volume services. In order to cooperate with this hybrid access algorithm, this section proposes a MAC layer protocol that combines random access and data bearer [28]. The protocol divides the access resources of each access cycle iT into 4 parts. The first part is used to broadcast the necessary access information such as the current time slot access resources and ACB control parameters; the second part is used for random access the preamble allocation; the third part is used for the one-step access of the time slot Aloha; the fourth part is the data bearer part, which is used for data transmission by the device that successfully obtains the preamble in random access, and the improved MAC layer. The agreement is shown in Figure 2.Figure 2
Schematic diagram of hybrid MAC protocol.The main difference between this protocol and the existing LTE access protocol is that the time slot Aloha access method is adopted for the services that are not sensitive to delay, and the effective data volume is small, while for the large data volume or delay-sensitive services, the access method is based on random access mode of ACB [29]. The protocol effectively avoids the problems of a sharp increase in signaling consumption and a decrease in overall system performance caused by the ACB mechanism for small data services, and it also ensures the needs of large data services and high-priority services.


### 3.3.1. The First Stage: Application Volume Forecasting Algorithm Based on Time Series Forecasting

Aiming at the coexistence of large data volume services and small data volume services in M2M communication in the Internet of Things, this chapter proposes an access control algorithm based on the combination of time-slot Aloha and adaptive ACB and proposes an access application volume based on time series prediction Forecasting algorithm.For services that adopt the adaptive ACB random access method, the amount of applications is estimated according to the state of the preamble in the ACB mechanism [30].Usesi to represent the state of the i-th preamble, where si=0 indicates that the preamble is not selected; that is, it is in an idle state, and si=1 indicates that the preamble is just selected by an MTC device; that is, the device can be the fourth stage transmits data. This state is called the successful state. si>1 means that two or more devices have selected the preamble and are detected as a conflict state. The probability that the i-th preamble is in these three states for
(4)PxjNα=nα=1−1Npnα,si=0Cnα1⋅1Np1−1Npnα−1=nαNp⋅1−1Npnα−1,si=1,1−1−1Npnα−nαNp⋅1−1Npnα−1,si≥2.Then, use the maximum likelihood estimation formula:(5)P=Psi=0Nαu1⋅Psi=1Nαu2⋅Psi≥2Nαu3.Let this formula take the maximum value, which is(6)Nα=argmax∑jJlnPsjNα.For services that use the Aloha contention access method of time slot, estimation is made according to the state of the physical resource block in the current time slot. The specific steps are as follows (7):
(7)Ci=logPidke,ilogNi−1Ni.The predicted value of the number of access requests for thei+1th time slot is
(8)Ni+1=Ni−Si+Fi+1+newi+1,i≤IA,Ni−Si+Fi+1,i>IA.Because the amount of access requests belongs to a time series, the weighted sum of historical increments can be used as the increment of the next time slot, and then(9)newi+1=max0,0.6∗newi+0.3∗newi−1+0.1∗newi−1=max0,0.6Ni−0.3Ni−1−0.2Ni−2−0.1Ni−3−0.6Fi−0.3Fi−1−0.1Fi−2+0.6Si−1+0.3Si−2+0.1Si−3.Therefore, the predicted value of the number of access requests in the next time slot is expressed as(10)Ni+1=maxNi1.6Ni−0.3Ni−1−0.2Ni−2−0.1Ni−3−0.6Fi−0.3Fi−1−0.1Fi−2+0.6Si−1+0.3Si−2+0.1Si−3−Si,i≤IANi−Si+Fi+1,i>IA.This prediction algorithm is basically consistent with the actual number of applications for access.


### 3.3.2. Second Stage: Parameter Selection Based on Maximum Expectations

After predicting the number of requests for access to the next time slot in the first stage, based on the principle of maximizing the number of successful access in the next time slot, the ACB control parameter (a) and the optimal solution of the preamble grouping threshold are discussed.Becausesi=1 indicates that the preamble transmission is successful, the expected value of the successful transmission preamble is calculated as (11)
(11)ENsNα=nα=∑i=1NρPsi=1Nα=nα=Np⋅Cnα1⋅1Np⋅1−1Npnα−1=nα⋅1−1Npnα−1.When there are a total ofN users, the probability of Na users passing the screening is
(12)PNα=nαN=n=Cnnα⋅anα⋅1−an−nα.In summary, for a total ofN users, the expected number of successful accesses is
(13)ENsN=n=EPNα=nαN=n⋅EN=n=∑na−1nNα=nαN=n⋅na⋅1−1Nna−1=∑na−1nCnnα⋅anα⋅1−an−nα⋅na⋅1−1Nna−1=n⋅a⋅1−aNpna−1.The optimal ACB control parameters derived froma are
(14)aopt=Jn.According to the conclusion in formula (14), it is easy to calculate that when the number of access applications is approximately equal to the number of available preambles for the current service, the expected number of successful access times is the highest, so 1S is selected as the current time slot high priority service. After imitating the number of preamble codes of 30, 54, and 70, respectively, this conclusion has also been verified.


## 3.3.1. The First Stage: Application Volume Forecasting Algorithm Based on Time Series Forecasting

Aiming at the coexistence of large data volume services and small data volume services in M2M communication in the Internet of Things, this chapter proposes an access control algorithm based on the combination of time-slot Aloha and adaptive ACB and proposes an access application volume based on time series prediction Forecasting algorithm.For services that adopt the adaptive ACB random access method, the amount of applications is estimated according to the state of the preamble in the ACB mechanism [30].Usesi to represent the state of the i-th preamble, where si=0 indicates that the preamble is not selected; that is, it is in an idle state, and si=1 indicates that the preamble is just selected by an MTC device; that is, the device can be the fourth stage transmits data. This state is called the successful state. si>1 means that two or more devices have selected the preamble and are detected as a conflict state. The probability that the i-th preamble is in these three states for
(4)PxjNα=nα=1−1Npnα,si=0Cnα1⋅1Np1−1Npnα−1=nαNp⋅1−1Npnα−1,si=1,1−1−1Npnα−nαNp⋅1−1Npnα−1,si≥2.Then, use the maximum likelihood estimation formula:(5)P=Psi=0Nαu1⋅Psi=1Nαu2⋅Psi≥2Nαu3.Let this formula take the maximum value, which is(6)Nα=argmax∑jJlnPsjNα.For services that use the Aloha contention access method of time slot, estimation is made according to the state of the physical resource block in the current time slot. The specific steps are as follows (7):
(7)Ci=logPidke,ilogNi−1Ni.The predicted value of the number of access requests for thei+1th time slot is
(8)Ni+1=Ni−Si+Fi+1+newi+1,i≤IA,Ni−Si+Fi+1,i>IA.Because the amount of access requests belongs to a time series, the weighted sum of historical increments can be used as the increment of the next time slot, and then(9)newi+1=max0,0.6∗newi+0.3∗newi−1+0.1∗newi−1=max0,0.6Ni−0.3Ni−1−0.2Ni−2−0.1Ni−3−0.6Fi−0.3Fi−1−0.1Fi−2+0.6Si−1+0.3Si−2+0.1Si−3.Therefore, the predicted value of the number of access requests in the next time slot is expressed as(10)Ni+1=maxNi1.6Ni−0.3Ni−1−0.2Ni−2−0.1Ni−3−0.6Fi−0.3Fi−1−0.1Fi−2+0.6Si−1+0.3Si−2+0.1Si−3−Si,i≤IANi−Si+Fi+1,i>IA.This prediction algorithm is basically consistent with the actual number of applications for access.


## 3.3.2. Second Stage: Parameter Selection Based on Maximum Expectations

After predicting the number of requests for access to the next time slot in the first stage, based on the principle of maximizing the number of successful access in the next time slot, the ACB control parameter (a) and the optimal solution of the preamble grouping threshold are discussed.Becausesi=1 indicates that the preamble transmission is successful, the expected value of the successful transmission preamble is calculated as (11)
(11)ENsNα=nα=∑i=1NρPsi=1Nα=nα=Np⋅Cnα1⋅1Np⋅1−1Npnα−1=nα⋅1−1Npnα−1.When there are a total ofN users, the probability of Na users passing the screening is
(12)PNα=nαN=n=Cnnα⋅anα⋅1−an−nα.In summary, for a total ofN users, the expected number of successful accesses is
(13)ENsN=n=EPNα=nαN=n⋅EN=n=∑na−1nNα=nαN=n⋅na⋅1−1Nna−1=∑na−1nCnnα⋅anα⋅1−an−nα⋅na⋅1−1Nna−1=n⋅a⋅1−aNpna−1.The optimal ACB control parameters derived froma are
(14)aopt=Jn.According to the conclusion in formula (14), it is easy to calculate that when the number of access applications is approximately equal to the number of available preambles for the current service, the expected number of successful access times is the highest, so 1S is selected as the current time slot high priority service. After imitating the number of preamble codes of 30, 54, and 70, respectively, this conclusion has also been verified.


## 4. Application of Artificial Intelligence in the Complexity of Digital Crossborder Trade

### 4.1. Experimental Analysis of Anomaly Detection in Digital Crossborder Trade Networks Based on Artificial Intelligence

#### 4.1.1. Anomaly Detection Classification Algorithm

Based on abnormal network intrusion detection behavior, fundamentally, it can be considered as a classification problem; that is to say, it accurately distinguishes normal behavior and abnormal behavior from a large amount of abnormal network behavior data and in different behaviors. To accurately determine the specific attack methods and methods, it is even necessary to have the ability to analyze the type of unknown attack detected [31]. This article focuses on the different levels of behavior inspection classification methods based on machine learning and neural networks.First, introduce the linear regression algorithm, and the formula of the linear regression algorithm is as (15)
(15)z=θ0+θ1x1+θ2x2+θ3x3⋯+θnxn=θTx,wherex is the independent variable, and θ is the weight parameter. For generalized logistic regression, the model algorithm is to feed back the result of a linear function to the sigmoid function; so, they can also be considered as a generalized logistic regression model. The logistic regression formula is as (16)
(16)hθx=11+e−z=11+e−θTx.The result of the sigmoid function is directly mapped to between (0.1), they can be widely used to replace the probability of a specific category of data, and for those who need to make full use of this probability to make assistance, the complexity of decision-making tasks is also very meaningful. The sigmoid function can be widely used to deal with two classification problems. After classifying an input datax, the result is that the probabilities of type 1 and type 0 are (17) and (18), respectively.
(17)Py=1x;θ=hθx,(18)Py=0x;θ=1−hθx.Next, we use a method of maximum likelihood in probability theory to solve the loss function, thereby obtaining the optimal parameters, so that the effect of this classification can be optimal. First, we can get a probability function:(19)Pyx;θ=hθxy∗1−hθx1−y.Since the sample data are independent, their joint probability distribution is(20)Lθ=∏i=1mPyixi;θ=∏i=1mhθxiyi∗1−hθxi1−yi.Take the log likelihood function as(21)ligLθ=∑i=1mloghθxiyi+∑i=1m1−hθxi1−yi.The calculation method of the maximum likelihood value estimation is usually to decompose a maximum likelihood value at the value ofIθ according to the requirements. We can usually directly convert it into a method that uses the differential gradient method to determine the parameters of the algorithm. Take the value size to solve the likelihood:
(22)Jθ=−1mIθ.This algorithm avoids the problems caused by the inaccuracy of the assumed distribution, because its purpose is to model directly based on the classification possibility of the data, without making any assumptions based on the assumed data distribution.Assuming that the training sample set has a class, according to Bayes’ theorem, the posterior probability of belonging to classci is
(23)Pcix=Px,ciPx=PciPxciPx.Since the class conditional probabilityPx∣ci is the joint probability of all attributes, for the convenience of calculation, the naive Bayes classifier assumes that all attributes are independent of each other. The posterior probability of belonging to class ci can be written as
(24)Pcix=PciPxciPx=PciPx∏j=1dPxjci,where d is the number of attributes, and xj is the value of x on the j-th attribute.Px is the same for all new classifications and can be completely ignored; so, the naive Bayes classifier regards each input new classification sample as the main theoretical basis for X to be classified as
(25)c^=argmaxcPci∏j=1dPxjci.


#### 4.1.2. Anomaly Detection Experiment Analysis

This chapter is based on the original algorithm database generated by the Web on the server as an algorithm data set and does an experimental system design and research verification on the XGBoost algorithm problems involved in the article that need continuous improvement [32]. At the same time, some new system improvement algorithm XGBoost detection model proposed in the academic research of this article is combined with some traditional machine language learning detection algorithms, such as logistic function regression, naive Bayes, support for multiple vector machines, K nearest neighbors, and support for single layer. The XGBoost detection algorithm such as the machine, carried out linear comparison and system comprehensive application analysis, verified the accuracy of the results of the detection algorithm and the safety of the system by some models that need to be proposed in the academic research of this article.The experiments in Table1 show that the features constructed in this paper can effectively distinguish abnormal requests from normal requests. Subsequent experiments focus on using different models to classify abnormal HTTP requests and determine which attack method each abnormal HTTP request belongs to.Table 1
Recognition accuracy rate of normal samples and abnormal samples.
ModelTraining set resultsTest machine resultNaive Bayes0.91640.9178Logistic regression1.01.0K neighbors0.99750.9953Support vector machines1.01.0XGBoost1.01.0Table2 show that on the training set or the test set, no matter which n-gram model is used, the classification result is almost completely correct, and the recognition rate is 100%, It can also be found from Table 2 that the feature dimensions of the three different grams are quite different, gram 1 is only 129, while gram2 is close to 1800, while gram 3 has exceeded 6000. Test set prediction results of different gram models are shown in Table 3.Table 2
Training results of training set of different n-gram models.
N-gramAccuracyPrecisionRecallF-scoreFeature dimension1-gram1.01.01.01.01292-gram1.01.01.01.017983-gram1.01.01.01.06154Table 3
Test set prediction results of different gram models.
N-gramAccuracyPrecisionRecallF-measure1-gram0.98420.98530.98250.98392-gram0.99140.99521.00.997593-gram0.98251.00.98530.9926In the two-level classification model, the accuracy and its running time under different feature dimensions appear in Tables4 and 5, respectively. The experimental research results confirm that appropriately reducing or reducing the dimension of the feature will not only increase the calculation time to a large extent but also will not directly affect the effectiveness of the calculation of the two-level partition model.Table 4
Accuracy results of different feature dimensions of the first layer.
Feature dimensionTraining set accuracyTest set accuracyRunning time (s)5 important features0.84650.817131.0010 important features0.89310.872932.5620 important features0.89980.885649.0640 important features0.89990.886356.67All features0.90010.8870101.92Similarly, as shown in Table5, the feature extraction method is a very effective calculation method. We can clearly see that when the dimension of the feature is appropriately reduced or reduced, the calculation speed can be greatly improved, and it will not it directly affects the computational validity of the two-layer partition model.Table 5
Accuracy results of different feature dimensions of the second layer.
Top nImportant feature dimensionSet accuracyTest set accuracyRunning time (s)Top 250.93230.896313.53100.94460.900416.25200.95290.928123.43400.95550.938929.05600.95650.942033.81All0.95920.943690.24Top 350.94060.900220.85100.96080.905524.43200.97500.944133.40400.98270.963344.23600.98270.966753.21All0.98690.9694153.44Top 450.95030.901827.56100.96580.907031.13200.98450.954740.06400.99360.973356.24600.99370.977863.46All0.99990.9807210.82The experimental results show that the network intrusion detection algorithm based on improved XGBoost proposed in this paper based on theoretical research is better than other detection algorithms in terms of detection rate and false alarm rate.


### 4.2. Complexity of Digital Crossborder Trade

#### 4.2.1. Digital Trade Rules and the Complexity of Negotiations under the WTO Framework

Although our electronic transmission tariff-free policy is only temporary, such a decision is only a mutual commitment between WTO and member states around the world, without any legal constraints. However, many WTO members are now paying great attention to this preferential policy, advocating to change its nature from temporary to permanent, and to give it corresponding legal effect [33]. The preferential policies will be updated. Due to the low efficiency of WTO negotiations, some WTO members have initiated and established the “True Friends of the Service Industry Group” [34], hoping that the process of TISA negotiations can make new progress and effectively promote the international digital commodity trade in the new region. Sustained and healthy development. However, the intertwined effects of various factors have prevented the TISA negotiations from achieving more prominent results. At present, the WTO has not completely produced a special chapter on the rules of digital trade, has no legal binding force, and has not issued a relevant mechanism to solve the problem.


#### 4.2.2. Disputes about Trade Barriers

(1) Digital Trade Tariff Barriers. The barriers of crossborder electronic digital service trade not only include trade barriers from various tariffs but also include various nontariff trade barriers. Regarding crossborder tariff trade barriers, the specific policy of exempting online electronic data transmission services from crossborder tariffs, which has been officially announced at this year’s WTO meeting, has become an issue [35]. However, some consensus has been basically reached with many parliamentary members. In the subsequent practice, there is no policy, and the exemption of crossborder tariffs is only temporary. In addition to the developed countries, some developing countries can hardly think of using “electronic transmission duty-free” [36] to greatly relax the tariff standards of developed countries such as the United States for access to the international digital market. The exemption of digital tariffs in developed countries that are major commodity importers and exporters engaged in international digital market trade will greatly reduce the source of local tax revenue and impact their local existing sovereign tax revenue management system [37]. This also shows that for the developing countries whose domestic digital trade economy is in the early stage of development, it is necessary to quickly promote the domestic mobile digital trade economy by eliminating the technical barriers of digital trade import tariffs [38]. A large number of traditional digital products and digital information service products abroad can fill and help to a large extent fill in and help make up for the technical defects of a large number of traditional digital technologies and digital laws in China. However, the electronic transmission tax has not completely prevented us from taxing such digital electronic transactions.(2) Nontariff Barriers to Digital Trade Restrictions on Crossborder Data Flow. Nontariff barriers in digital trade mainly include the following: data crossborder flow restriction, intellectual property rights infringement, and personal information protection. These nontariff barriers are often used to spread discriminatory laws and policies and to customize different market access standards and market regulatory policies for digital service participants [39]. With the development of domestic digital trade, disputes in digital trade are gradually intensifying and competing, and countries are beginning to realize the imperfection of domestic digital trade rules and the serious lack of an internationally unified digital trade rule system [40]. On the grounds of maintaining data security, governments of various countries have specifically introduced a series of policies to prevent hindering the flow of crossborder data in their countries, including governments preventing foreign investment companies from participating in the domestic digital economy market and requiring their foreign business access to comply with local content requirements, the existence of local databases, etc. However, one of the direct results of these preferential policies is to significantly increase the operating expenses and costs of the enterprise. The enterprise puts more energy on investment or considers reducing the transaction cost of the Internet and reduces the enterprise’s independent research and development of digital technology. The importance of this is not conducive to fundamentally and comprehensively enhancing the competitiveness of enterprises.


#### 4.2.3. Disputes concerning the Protection of Personal Information

(1) Personal Information Protection Measures. The issue of personal privacy and security protection is undoubtedly an issue that we often need to pay special attention to when we obtain user personal information. With the rapid popularization of mobile phones and the Internet and the rapid development of the industry, the key issue of data information security has become increasingly prominent. The security protection of personal privacy has therefore received extensive attention. So far, our country has not officially promulgated a law specifically for the security protection of users’ personal information. Although we have put forward some constructive guidance on the management of the security protection of users’ personal information in recent years, we have not yet developed a complete set of laws and guidelines.(2) Differences in Personal Information Protection Systems in Various Countries. Regarding the protection of personal information, most countries have fully realized the right of Internet users to protect their personal information and have also actively improved the domestic rules for the protection of personal information. Therefore, in the context of advancing digital trade, countries should actively play the role of an international code builder for crossborder data flow instead of becoming a passive recipient of the code. In the process of studying and formulating domestic policies and actively participating in international standards, the government requires the government to fully consider the profits and social responsibilities of domestic enterprises in the process of crossborder data flow and actively cooperate with international organizations and the current legal system. Effectively enhance the completeness of the guidelines for crossborder data flow for domestic enterprises and safeguard the legitimate rights and interests of the country.


#### 4.2.4. Disputes concerning Intellectual Property Rights

(1) Types of Infringement of Intellectual Property Rights on the Internet. Online infringing digital content accounts for a large proportion of online intellectual property rights damage and infringement of digital content. Illegal and infringing use of digital content is widespread in developing countries, and it will become a huge resource gap that directly affects digital content trade and other economic interests. In order to better protect all of their due digital business interests, we will continue to work hard in the future and solve the illegal infringement of digital content. The infringer has the right to inform the provider of the harm of network services in a timely manner and take necessary protective measures. If the necessary risk prevention measures are not properly implemented in a timely manner, the infringer shall also bear direct and joint liability for the part that may cause economic losses.(2) Remedies for Intellectual Property Infringement. At present, the vast majority of China’s intellectual property legislation have exceeded the TRIPS standard and are also much higher than the level of intellectual property legislation in other developing countries. The Chinese Copyright Law separately stipulates various civil liabilities for infringement of its intellectual property rights and its criminal liabilities. We must update and develop information technology, build a complete public information service system, standardize and optimize reasonable routes and channels for digital information transmission, protect the safety and legitimate interests of all intellectual property rights holders, ensure the health of all intellectual property rights and systems, fast operation, and truly promote and promote the digital development of intellectual property rights.


## 4.1. Experimental Analysis of Anomaly Detection in Digital Crossborder Trade Networks Based on Artificial Intelligence

### 4.1.1. Anomaly Detection Classification Algorithm

Based on abnormal network intrusion detection behavior, fundamentally, it can be considered as a classification problem; that is to say, it accurately distinguishes normal behavior and abnormal behavior from a large amount of abnormal network behavior data and in different behaviors. To accurately determine the specific attack methods and methods, it is even necessary to have the ability to analyze the type of unknown attack detected [31]. This article focuses on the different levels of behavior inspection classification methods based on machine learning and neural networks.First, introduce the linear regression algorithm, and the formula of the linear regression algorithm is as (15)
(15)z=θ0+θ1x1+θ2x2+θ3x3⋯+θnxn=θTx,wherex is the independent variable, and θ is the weight parameter. For generalized logistic regression, the model algorithm is to feed back the result of a linear function to the sigmoid function; so, they can also be considered as a generalized logistic regression model. The logistic regression formula is as (16)
(16)hθx=11+e−z=11+e−θTx.The result of the sigmoid function is directly mapped to between (0.1), they can be widely used to replace the probability of a specific category of data, and for those who need to make full use of this probability to make assistance, the complexity of decision-making tasks is also very meaningful. The sigmoid function can be widely used to deal with two classification problems. After classifying an input datax, the result is that the probabilities of type 1 and type 0 are (17) and (18), respectively.
(17)Py=1x;θ=hθx,(18)Py=0x;θ=1−hθx.Next, we use a method of maximum likelihood in probability theory to solve the loss function, thereby obtaining the optimal parameters, so that the effect of this classification can be optimal. First, we can get a probability function:(19)Pyx;θ=hθxy∗1−hθx1−y.Since the sample data are independent, their joint probability distribution is(20)Lθ=∏i=1mPyixi;θ=∏i=1mhθxiyi∗1−hθxi1−yi.Take the log likelihood function as(21)ligLθ=∑i=1mloghθxiyi+∑i=1m1−hθxi1−yi.The calculation method of the maximum likelihood value estimation is usually to decompose a maximum likelihood value at the value ofIθ according to the requirements. We can usually directly convert it into a method that uses the differential gradient method to determine the parameters of the algorithm. Take the value size to solve the likelihood:
(22)Jθ=−1mIθ.This algorithm avoids the problems caused by the inaccuracy of the assumed distribution, because its purpose is to model directly based on the classification possibility of the data, without making any assumptions based on the assumed data distribution.Assuming that the training sample set has a class, according to Bayes’ theorem, the posterior probability of belonging to classci is
(23)Pcix=Px,ciPx=PciPxciPx.Since the class conditional probabilityPx∣ci is the joint probability of all attributes, for the convenience of calculation, the naive Bayes classifier assumes that all attributes are independent of each other. The posterior probability of belonging to class ci can be written as
(24)Pcix=PciPxciPx=PciPx∏j=1dPxjci,where d is the number of attributes, and xj is the value of x on the j-th attribute.Px is the same for all new classifications and can be completely ignored; so, the naive Bayes classifier regards each input new classification sample as the main theoretical basis for X to be classified as
(25)c^=argmaxcPci∏j=1dPxjci.


### 4.1.2. Anomaly Detection Experiment Analysis

This chapter is based on the original algorithm database generated by the Web on the server as an algorithm data set and does an experimental system design and research verification on the XGBoost algorithm problems involved in the article that need continuous improvement [32]. At the same time, some new system improvement algorithm XGBoost detection model proposed in the academic research of this article is combined with some traditional machine language learning detection algorithms, such as logistic function regression, naive Bayes, support for multiple vector machines, K nearest neighbors, and support for single layer. The XGBoost detection algorithm such as the machine, carried out linear comparison and system comprehensive application analysis, verified the accuracy of the results of the detection algorithm and the safety of the system by some models that need to be proposed in the academic research of this article.The experiments in Table1 show that the features constructed in this paper can effectively distinguish abnormal requests from normal requests. Subsequent experiments focus on using different models to classify abnormal HTTP requests and determine which attack method each abnormal HTTP request belongs to.Table 1
Recognition accuracy rate of normal samples and abnormal samples.
ModelTraining set resultsTest machine resultNaive Bayes0.91640.9178Logistic regression1.01.0K neighbors0.99750.9953Support vector machines1.01.0XGBoost1.01.0Table2 show that on the training set or the test set, no matter which n-gram model is used, the classification result is almost completely correct, and the recognition rate is 100%, It can also be found from Table 2 that the feature dimensions of the three different grams are quite different, gram 1 is only 129, while gram2 is close to 1800, while gram 3 has exceeded 6000. Test set prediction results of different gram models are shown in Table 3.Table 2
Training results of training set of different n-gram models.
N-gramAccuracyPrecisionRecallF-scoreFeature dimension1-gram1.01.01.01.01292-gram1.01.01.01.017983-gram1.01.01.01.06154Table 3
Test set prediction results of different gram models.
N-gramAccuracyPrecisionRecallF-measure1-gram0.98420.98530.98250.98392-gram0.99140.99521.00.997593-gram0.98251.00.98530.9926In the two-level classification model, the accuracy and its running time under different feature dimensions appear in Tables4 and 5, respectively. The experimental research results confirm that appropriately reducing or reducing the dimension of the feature will not only increase the calculation time to a large extent but also will not directly affect the effectiveness of the calculation of the two-level partition model.Table 4
Accuracy results of different feature dimensions of the first layer.
Feature dimensionTraining set accuracyTest set accuracyRunning time (s)5 important features0.84650.817131.0010 important features0.89310.872932.5620 important features0.89980.885649.0640 important features0.89990.886356.67All features0.90010.8870101.92Similarly, as shown in Table5, the feature extraction method is a very effective calculation method. We can clearly see that when the dimension of the feature is appropriately reduced or reduced, the calculation speed can be greatly improved, and it will not it directly affects the computational validity of the two-layer partition model.Table 5
Accuracy results of different feature dimensions of the second layer.
Top nImportant feature dimensionSet accuracyTest set accuracyRunning time (s)Top 250.93230.896313.53100.94460.900416.25200.95290.928123.43400.95550.938929.05600.95650.942033.81All0.95920.943690.24Top 350.94060.900220.85100.96080.905524.43200.97500.944133.40400.98270.963344.23600.98270.966753.21All0.98690.9694153.44Top 450.95030.901827.56100.96580.907031.13200.98450.954740.06400.99360.973356.24600.99370.977863.46All0.99990.9807210.82The experimental results show that the network intrusion detection algorithm based on improved XGBoost proposed in this paper based on theoretical research is better than other detection algorithms in terms of detection rate and false alarm rate.


## 4.1.1. Anomaly Detection Classification Algorithm

Based on abnormal network intrusion detection behavior, fundamentally, it can be considered as a classification problem; that is to say, it accurately distinguishes normal behavior and abnormal behavior from a large amount of abnormal network behavior data and in different behaviors. To accurately determine the specific attack methods and methods, it is even necessary to have the ability to analyze the type of unknown attack detected [31]. This article focuses on the different levels of behavior inspection classification methods based on machine learning and neural networks.First, introduce the linear regression algorithm, and the formula of the linear regression algorithm is as (15)
(15)z=θ0+θ1x1+θ2x2+θ3x3⋯+θnxn=θTx,wherex is the independent variable, and θ is the weight parameter. For generalized logistic regression, the model algorithm is to feed back the result of a linear function to the sigmoid function; so, they can also be considered as a generalized logistic regression model. The logistic regression formula is as (16)
(16)hθx=11+e−z=11+e−θTx.The result of the sigmoid function is directly mapped to between (0.1), they can be widely used to replace the probability of a specific category of data, and for those who need to make full use of this probability to make assistance, the complexity of decision-making tasks is also very meaningful. The sigmoid function can be widely used to deal with two classification problems. After classifying an input datax, the result is that the probabilities of type 1 and type 0 are (17) and (18), respectively.
(17)Py=1x;θ=hθx,(18)Py=0x;θ=1−hθx.Next, we use a method of maximum likelihood in probability theory to solve the loss function, thereby obtaining the optimal parameters, so that the effect of this classification can be optimal. First, we can get a probability function:(19)Pyx;θ=hθxy∗1−hθx1−y.Since the sample data are independent, their joint probability distribution is(20)Lθ=∏i=1mPyixi;θ=∏i=1mhθxiyi∗1−hθxi1−yi.Take the log likelihood function as(21)ligLθ=∑i=1mloghθxiyi+∑i=1m1−hθxi1−yi.The calculation method of the maximum likelihood value estimation is usually to decompose a maximum likelihood value at the value ofIθ according to the requirements. We can usually directly convert it into a method that uses the differential gradient method to determine the parameters of the algorithm. Take the value size to solve the likelihood:
(22)Jθ=−1mIθ.This algorithm avoids the problems caused by the inaccuracy of the assumed distribution, because its purpose is to model directly based on the classification possibility of the data, without making any assumptions based on the assumed data distribution.Assuming that the training sample set has a class, according to Bayes’ theorem, the posterior probability of belonging to classci is
(23)Pcix=Px,ciPx=PciPxciPx.Since the class conditional probabilityPx∣ci is the joint probability of all attributes, for the convenience of calculation, the naive Bayes classifier assumes that all attributes are independent of each other. The posterior probability of belonging to class ci can be written as
(24)Pcix=PciPxciPx=PciPx∏j=1dPxjci,where d is the number of attributes, and xj is the value of x on the j-th attribute.Px is the same for all new classifications and can be completely ignored; so, the naive Bayes classifier regards each input new classification sample as the main theoretical basis for X to be classified as
(25)c^=argmaxcPci∏j=1dPxjci.


## 4.1.2. Anomaly Detection Experiment Analysis

This chapter is based on the original algorithm database generated by the Web on the server as an algorithm data set and does an experimental system design and research verification on the XGBoost algorithm problems involved in the article that need continuous improvement [32]. At the same time, some new system improvement algorithm XGBoost detection model proposed in the academic research of this article is combined with some traditional machine language learning detection algorithms, such as logistic function regression, naive Bayes, support for multiple vector machines, K nearest neighbors, and support for single layer. The XGBoost detection algorithm such as the machine, carried out linear comparison and system comprehensive application analysis, verified the accuracy of the results of the detection algorithm and the safety of the system by some models that need to be proposed in the academic research of this article.The experiments in Table1 show that the features constructed in this paper can effectively distinguish abnormal requests from normal requests. Subsequent experiments focus on using different models to classify abnormal HTTP requests and determine which attack method each abnormal HTTP request belongs to.Table 1
Recognition accuracy rate of normal samples and abnormal samples.
ModelTraining set resultsTest machine resultNaive Bayes0.91640.9178Logistic regression1.01.0K neighbors0.99750.9953Support vector machines1.01.0XGBoost1.01.0Table2 show that on the training set or the test set, no matter which n-gram model is used, the classification result is almost completely correct, and the recognition rate is 100%, It can also be found from Table 2 that the feature dimensions of the three different grams are quite different, gram 1 is only 129, while gram2 is close to 1800, while gram 3 has exceeded 6000. Test set prediction results of different gram models are shown in Table 3.Table 2
Training results of training set of different n-gram models.
N-gramAccuracyPrecisionRecallF-scoreFeature dimension1-gram1.01.01.01.01292-gram1.01.01.01.017983-gram1.01.01.01.06154Table 3
Test set prediction results of different gram models.
N-gramAccuracyPrecisionRecallF-measure1-gram0.98420.98530.98250.98392-gram0.99140.99521.00.997593-gram0.98251.00.98530.9926In the two-level classification model, the accuracy and its running time under different feature dimensions appear in Tables4 and 5, respectively. The experimental research results confirm that appropriately reducing or reducing the dimension of the feature will not only increase the calculation time to a large extent but also will not directly affect the effectiveness of the calculation of the two-level partition model.Table 4
Accuracy results of different feature dimensions of the first layer.
Feature dimensionTraining set accuracyTest set accuracyRunning time (s)5 important features0.84650.817131.0010 important features0.89310.872932.5620 important features0.89980.885649.0640 important features0.89990.886356.67All features0.90010.8870101.92Similarly, as shown in Table5, the feature extraction method is a very effective calculation method. We can clearly see that when the dimension of the feature is appropriately reduced or reduced, the calculation speed can be greatly improved, and it will not it directly affects the computational validity of the two-layer partition model.Table 5
Accuracy results of different feature dimensions of the second layer.
Top nImportant feature dimensionSet accuracyTest set accuracyRunning time (s)Top 250.93230.896313.53100.94460.900416.25200.95290.928123.43400.95550.938929.05600.95650.942033.81All0.95920.943690.24Top 350.94060.900220.85100.96080.905524.43200.97500.944133.40400.98270.963344.23600.98270.966753.21All0.98690.9694153.44Top 450.95030.901827.56100.96580.907031.13200.98450.954740.06400.99360.973356.24600.99370.977863.46All0.99990.9807210.82The experimental results show that the network intrusion detection algorithm based on improved XGBoost proposed in this paper based on theoretical research is better than other detection algorithms in terms of detection rate and false alarm rate.


## 4.2. Complexity of Digital Crossborder Trade

### 4.2.1. Digital Trade Rules and the Complexity of Negotiations under the WTO Framework

Although our electronic transmission tariff-free policy is only temporary, such a decision is only a mutual commitment between WTO and member states around the world, without any legal constraints. However, many WTO members are now paying great attention to this preferential policy, advocating to change its nature from temporary to permanent, and to give it corresponding legal effect [33]. The preferential policies will be updated. Due to the low efficiency of WTO negotiations, some WTO members have initiated and established the “True Friends of the Service Industry Group” [34], hoping that the process of TISA negotiations can make new progress and effectively promote the international digital commodity trade in the new region. Sustained and healthy development. However, the intertwined effects of various factors have prevented the TISA negotiations from achieving more prominent results. At present, the WTO has not completely produced a special chapter on the rules of digital trade, has no legal binding force, and has not issued a relevant mechanism to solve the problem.


### 4.2.2. Disputes about Trade Barriers

(1) Digital Trade Tariff Barriers. The barriers of crossborder electronic digital service trade not only include trade barriers from various tariffs but also include various nontariff trade barriers. Regarding crossborder tariff trade barriers, the specific policy of exempting online electronic data transmission services from crossborder tariffs, which has been officially announced at this year’s WTO meeting, has become an issue [35]. However, some consensus has been basically reached with many parliamentary members. In the subsequent practice, there is no policy, and the exemption of crossborder tariffs is only temporary. In addition to the developed countries, some developing countries can hardly think of using “electronic transmission duty-free” [36] to greatly relax the tariff standards of developed countries such as the United States for access to the international digital market. The exemption of digital tariffs in developed countries that are major commodity importers and exporters engaged in international digital market trade will greatly reduce the source of local tax revenue and impact their local existing sovereign tax revenue management system [37]. This also shows that for the developing countries whose domestic digital trade economy is in the early stage of development, it is necessary to quickly promote the domestic mobile digital trade economy by eliminating the technical barriers of digital trade import tariffs [38]. A large number of traditional digital products and digital information service products abroad can fill and help to a large extent fill in and help make up for the technical defects of a large number of traditional digital technologies and digital laws in China. However, the electronic transmission tax has not completely prevented us from taxing such digital electronic transactions.(2) Nontariff Barriers to Digital Trade Restrictions on Crossborder Data Flow. Nontariff barriers in digital trade mainly include the following: data crossborder flow restriction, intellectual property rights infringement, and personal information protection. These nontariff barriers are often used to spread discriminatory laws and policies and to customize different market access standards and market regulatory policies for digital service participants [39]. With the development of domestic digital trade, disputes in digital trade are gradually intensifying and competing, and countries are beginning to realize the imperfection of domestic digital trade rules and the serious lack of an internationally unified digital trade rule system [40]. On the grounds of maintaining data security, governments of various countries have specifically introduced a series of policies to prevent hindering the flow of crossborder data in their countries, including governments preventing foreign investment companies from participating in the domestic digital economy market and requiring their foreign business access to comply with local content requirements, the existence of local databases, etc. However, one of the direct results of these preferential policies is to significantly increase the operating expenses and costs of the enterprise. The enterprise puts more energy on investment or considers reducing the transaction cost of the Internet and reduces the enterprise’s independent research and development of digital technology. The importance of this is not conducive to fundamentally and comprehensively enhancing the competitiveness of enterprises.


### 4.2.3. Disputes concerning the Protection of Personal Information

(1) Personal Information Protection Measures. The issue of personal privacy and security protection is undoubtedly an issue that we often need to pay special attention to when we obtain user personal information. With the rapid popularization of mobile phones and the Internet and the rapid development of the industry, the key issue of data information security has become increasingly prominent. The security protection of personal privacy has therefore received extensive attention. So far, our country has not officially promulgated a law specifically for the security protection of users’ personal information. Although we have put forward some constructive guidance on the management of the security protection of users’ personal information in recent years, we have not yet developed a complete set of laws and guidelines.(2) Differences in Personal Information Protection Systems in Various Countries. Regarding the protection of personal information, most countries have fully realized the right of Internet users to protect their personal information and have also actively improved the domestic rules for the protection of personal information. Therefore, in the context of advancing digital trade, countries should actively play the role of an international code builder for crossborder data flow instead of becoming a passive recipient of the code. In the process of studying and formulating domestic policies and actively participating in international standards, the government requires the government to fully consider the profits and social responsibilities of domestic enterprises in the process of crossborder data flow and actively cooperate with international organizations and the current legal system. Effectively enhance the completeness of the guidelines for crossborder data flow for domestic enterprises and safeguard the legitimate rights and interests of the country.


### 4.2.4. Disputes concerning Intellectual Property Rights

(1) Types of Infringement of Intellectual Property Rights on the Internet. Online infringing digital content accounts for a large proportion of online intellectual property rights damage and infringement of digital content. Illegal and infringing use of digital content is widespread in developing countries, and it will become a huge resource gap that directly affects digital content trade and other economic interests. In order to better protect all of their due digital business interests, we will continue to work hard in the future and solve the illegal infringement of digital content. The infringer has the right to inform the provider of the harm of network services in a timely manner and take necessary protective measures. If the necessary risk prevention measures are not properly implemented in a timely manner, the infringer shall also bear direct and joint liability for the part that may cause economic losses.(2) Remedies for Intellectual Property Infringement. At present, the vast majority of China’s intellectual property legislation have exceeded the TRIPS standard and are also much higher than the level of intellectual property legislation in other developing countries. The Chinese Copyright Law separately stipulates various civil liabilities for infringement of its intellectual property rights and its criminal liabilities. We must update and develop information technology, build a complete public information service system, standardize and optimize reasonable routes and channels for digital information transmission, protect the safety and legitimate interests of all intellectual property rights holders, ensure the health of all intellectual property rights and systems, fast operation, and truly promote and promote the digital development of intellectual property rights.


## 4.2.1. Digital Trade Rules and the Complexity of Negotiations under the WTO Framework

Although our electronic transmission tariff-free policy is only temporary, such a decision is only a mutual commitment between WTO and member states around the world, without any legal constraints. However, many WTO members are now paying great attention to this preferential policy, advocating to change its nature from temporary to permanent, and to give it corresponding legal effect [33]. The preferential policies will be updated. Due to the low efficiency of WTO negotiations, some WTO members have initiated and established the “True Friends of the Service Industry Group” [34], hoping that the process of TISA negotiations can make new progress and effectively promote the international digital commodity trade in the new region. Sustained and healthy development. However, the intertwined effects of various factors have prevented the TISA negotiations from achieving more prominent results. At present, the WTO has not completely produced a special chapter on the rules of digital trade, has no legal binding force, and has not issued a relevant mechanism to solve the problem.


## 4.2.2. Disputes about Trade Barriers

(1) Digital Trade Tariff Barriers. The barriers of crossborder electronic digital service trade not only include trade barriers from various tariffs but also include various nontariff trade barriers. Regarding crossborder tariff trade barriers, the specific policy of exempting online electronic data transmission services from crossborder tariffs, which has been officially announced at this year’s WTO meeting, has become an issue [35]. However, some consensus has been basically reached with many parliamentary members. In the subsequent practice, there is no policy, and the exemption of crossborder tariffs is only temporary. In addition to the developed countries, some developing countries can hardly think of using “electronic transmission duty-free” [36] to greatly relax the tariff standards of developed countries such as the United States for access to the international digital market. The exemption of digital tariffs in developed countries that are major commodity importers and exporters engaged in international digital market trade will greatly reduce the source of local tax revenue and impact their local existing sovereign tax revenue management system [37]. This also shows that for the developing countries whose domestic digital trade economy is in the early stage of development, it is necessary to quickly promote the domestic mobile digital trade economy by eliminating the technical barriers of digital trade import tariffs [38]. A large number of traditional digital products and digital information service products abroad can fill and help to a large extent fill in and help make up for the technical defects of a large number of traditional digital technologies and digital laws in China. However, the electronic transmission tax has not completely prevented us from taxing such digital electronic transactions.(2) Nontariff Barriers to Digital Trade Restrictions on Crossborder Data Flow. Nontariff barriers in digital trade mainly include the following: data crossborder flow restriction, intellectual property rights infringement, and personal information protection. These nontariff barriers are often used to spread discriminatory laws and policies and to customize different market access standards and market regulatory policies for digital service participants [39]. With the development of domestic digital trade, disputes in digital trade are gradually intensifying and competing, and countries are beginning to realize the imperfection of domestic digital trade rules and the serious lack of an internationally unified digital trade rule system [40]. On the grounds of maintaining data security, governments of various countries have specifically introduced a series of policies to prevent hindering the flow of crossborder data in their countries, including governments preventing foreign investment companies from participating in the domestic digital economy market and requiring their foreign business access to comply with local content requirements, the existence of local databases, etc. However, one of the direct results of these preferential policies is to significantly increase the operating expenses and costs of the enterprise. The enterprise puts more energy on investment or considers reducing the transaction cost of the Internet and reduces the enterprise’s independent research and development of digital technology. The importance of this is not conducive to fundamentally and comprehensively enhancing the competitiveness of enterprises.


## 4.2.3. Disputes concerning the Protection of Personal Information

(1) Personal Information Protection Measures. The issue of personal privacy and security protection is undoubtedly an issue that we often need to pay special attention to when we obtain user personal information. With the rapid popularization of mobile phones and the Internet and the rapid development of the industry, the key issue of data information security has become increasingly prominent. The security protection of personal privacy has therefore received extensive attention. So far, our country has not officially promulgated a law specifically for the security protection of users’ personal information. Although we have put forward some constructive guidance on the management of the security protection of users’ personal information in recent years, we have not yet developed a complete set of laws and guidelines.(2) Differences in Personal Information Protection Systems in Various Countries. Regarding the protection of personal information, most countries have fully realized the right of Internet users to protect their personal information and have also actively improved the domestic rules for the protection of personal information. Therefore, in the context of advancing digital trade, countries should actively play the role of an international code builder for crossborder data flow instead of becoming a passive recipient of the code. In the process of studying and formulating domestic policies and actively participating in international standards, the government requires the government to fully consider the profits and social responsibilities of domestic enterprises in the process of crossborder data flow and actively cooperate with international organizations and the current legal system. Effectively enhance the completeness of the guidelines for crossborder data flow for domestic enterprises and safeguard the legitimate rights and interests of the country.


## 4.2.4. Disputes concerning Intellectual Property Rights

(1) Types of Infringement of Intellectual Property Rights on the Internet. Online infringing digital content accounts for a large proportion of online intellectual property rights damage and infringement of digital content. Illegal and infringing use of digital content is widespread in developing countries, and it will become a huge resource gap that directly affects digital content trade and other economic interests. In order to better protect all of their due digital business interests, we will continue to work hard in the future and solve the illegal infringement of digital content. The infringer has the right to inform the provider of the harm of network services in a timely manner and take necessary protective measures. If the necessary risk prevention measures are not properly implemented in a timely manner, the infringer shall also bear direct and joint liability for the part that may cause economic losses.(2) Remedies for Intellectual Property Infringement. At present, the vast majority of China’s intellectual property legislation have exceeded the TRIPS standard and are also much higher than the level of intellectual property legislation in other developing countries. The Chinese Copyright Law separately stipulates various civil liabilities for infringement of its intellectual property rights and its criminal liabilities. We must update and develop information technology, build a complete public information service system, standardize and optimize reasonable routes and channels for digital information transmission, protect the safety and legitimate interests of all intellectual property rights holders, ensure the health of all intellectual property rights and systems, fast operation, and truly promote and promote the digital development of intellectual property rights.


## 5. Conclusion

This article focuses on the problem of large-scale terminal access in the Internet of Things environment, and, respectively, proposes the access control algorithm based on the time slot Aloha and adaptive ACB mixed in the single base station and multiple terminal scenarios, as well as multiple access points and heterogeneous network environments. Under the access control algorithm based on dynamic load transfer, implement this algorithm in the actual Internet of Things scenario. The access resources in a time slot are allocated to the time slot Aloha and adaptive ACB for simultaneous use, which ensures the QoS requirements of high-priority services, reduces the additional signaling consumption of the ACB mechanism, and reduces the average access of the entire system. Time delay improves the access success rate of the system; the access control algorithm based on dynamic load transfer is aimed at heterogeneous network access scenarios, and the transfer priority evaluation algorithm is used to select the highest priority service when the initial access fails. For load transfer, the access control algorithm based on the combination of time slot Aloha and adaptive ACB captures the coexistence of large data services and small data services in M2M communications. This article mainly discusses the nature of risks in artificial intelligence information technology, analyzes the main internal causes and external influences of risks in artificial intelligence information technology, and proposes specific strategies and solutions for information technology risk response and prevention. The Internet not only breaks geographical restrictions and brings prosperity to digital trade but also brings new opportunities to the economic levels and levels of various countries around the world. However, opportunities and challenges often exist at the same time, and the lack of international standards for digital trade dispute resolution has become the biggest obstacle in the current digital trade process. In order to take the lead in the distribution of economic benefits on a global scale, many countries in the world are actively formulating development strategies related to them.



---

*Source: 2339399-2022-04-28.xml*