Research on Enterprise Financial Accounting Information Security Model Based on Big Data

Zhong, Ran; Zhang, Qianying; Zhao, Yachao

doi:https://doi.org/10.1155/2022/7929846

Wireless Communications and Mobile Computing

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Artificial Intelligence and Computing on Industrial Applications

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Research Article | Open Access

Volume 2022 | Article ID 7929846 | https://doi.org/10.1155/2022/7929846

Research on Enterprise Financial Accounting Information Security Model Based on Big Data

Ran Zhong,¹Qianying Zhang,²and Yachao Zhao²

Academic Editor: Chia-Huei Wu

Received18 Feb 2022

Revised05 Mar 2022

Accepted11 Mar 2022

Published12 May 2022

Abstract

Powered by the rapid development of the information age, big data technology has had a great impact on China’s socialist economic development. Big data technology is integrated into all fields of nowadays society, especially in the accounting industry of enterprises. Accounting computerization quickly replaces the traditional manual bookkeeping, realizes the timeliness and accuracy of accounting information data processing, and improves the work efficiency and quality of accounting staff to a great extent, but at the same time, the security of financial accounting information is also very prominent. To tackle this problem, this paper proposes a hybrid encryption algorithm based on double chaotic system and improved AES encryption algorithm. The improved AES algorithm uses affine transformation pairs (A7 and 6F) to generate new S-boxes. The double four-dimensional hyperchaotic system is transformed from two three-dimensional chaotic systems, and then, the transformed hyperchaotic system is used to generate chaotic sequences, and a block encryption scheme is designed. On Hadoop big data platform, double hyperchaotic encryption scheme and improved AES algorithm are combined. Simulation test results show that this method can safely transmit enterprise financial accounting information data packets. With the increase of computing cluster nodes, its encryption transmission efficiency continues to improve. This scheme not only solves the problem of enterprise financial accounting data security encryption but also realizes the parallel transmission of encrypted data in the information age, forming a double guarantee of data transmission security and efficiency.

1. Introduction

Big data refers to data sets with large data scale that cannot be stored and processed by using existing technologies. User privacy and data security have always been the focus of research in the field of big data [1]. With the gradual maturity of big data technology, big data technology is widely used in various fields, and the research of big data encryption algorithm and scheme has attracted extensive attention.

The comprehensive embodiment of big data thinking and technology in accounting work is a new model with stronger insight, decision-making power, and information optimization processing ability [2]. It has six characteristics: high speed, diversity, large quantity, authenticity, value, and density. The era of enterprise big data accounting is coming with the rapid development of artificial intelligence [3]. In the enterprise accounting work, the work efficiency of accounting business is further improved by using big data thinking and technology [4]. If enterprises want to make better use of various big data accounting technologies, they need to take measures in many aspects to promote the comprehensive development of enterprises.

With the continuous development of information technology, the work of secure collection, real-time storage, and secure real-time collection and transmission of enterprise accounting data has become more and more rapid [5]. Between different types of accounting enterprises, various new information technologies can be used to realize the safe and real-time collection and transmission of enterprise accounting data. Therefore, the security and encryption protection of enterprise accounting data has become particularly important.

How to collect, transfer, and store the information related to enterprise accounting big data in real time and safely is discussed in this study. In the work of enterprise financial accounting, it is necessary to collect sensitive data information in time and safely through data, carry out security encryption protection, and avoid the leakage of important data information through data collection, which will have an adverse impact on the normal business development and business life of the whole enterprise [6]. The continuous popularization and wide application of basic technologies for the practical application of big data in Chinese enterprise accounting have improved the actual work efficiency of enterprise accounting managers [7]. It further demonstrates the practical applicability and value of enterprise accounting big data. With the gradual development, improvement, and popularization of enterprise information basic technology, there are still some basic technologies for big data enterprise application that need to be continuously updated and improved, so as to provide more help for the development and application of enterprise accounting management workers [8].

Advanced encryption standard (AES) encryption technology is widely used in the field of data and information security. In order to meet the needs of high-speed and safe use of massive information in the era of big data [9], a lot of research has been carried out in the field of data information security. In the existing research, literature [10] adopts hyperchaotic block encryption and AES hybrid encryption scheme. Although the execution efficiency of the algorithm is improved, the problem of short S-box iteration cycle of AES encryption algorithm is ignored. In literature [11], a data encryption model is designed by using double chaotic system. Although it can also improve the efficiency and security of data encryption, AES algorithm is not considered. Based on the above reasons, this study improves two groups of triple chaotic systems and AES algorithm, generates two new four-dimensional chaotic systems and new AES algorithm, uses two four-dimensional chaotic systems to design a packet encryption scheme, and finally integrates with the improved AES algorithm on Hadoop platform to form an encryption algorithm.

The main innovations of this paper are as follows. (1)The size of key space in data encryption will affect the security of encryption algorithm. In this study, two three-dimensional chaotic systems are transformed to generate two four-dimensional chaotic systems, which improves the key length of the hybrid encryption scheme(2)The iteration cycle of traditional AES encryption algorithm is short, and there may be the risk of being decoded. In this study, a new affine transformation pair is used to generate a new S-box sequence, which improves the iteration cycle of AES algorithm(3)Chaotic system has very complex dynamic characteristics, which can be used not only for data encryption but also for other types of file encryption. The higher the dimension of the system is, the more complex the dynamic characteristics are, and the better the encryption effect will be

This paper consists of five main parts: the first part is the Introduction. The second part is the Related Research. The third part is the Algorithm Design. The fourth part is the Experimental Analysis. The fifth part is the Conclusion, in addition to the summary and references.

2.1. Analysis of Factors Affecting the Security of Enterprise Financial Accounting Information

Combined with the current situation of enterprise financial accounting information security, this paper analyzes the security factors affecting data and classifies them into four categories: (1)Active aggressive factors. The use of high-tech or covert illegal means to steal the internal financial accounting information of enterprise computers, seriously hinder the normal use of data users, infringe on business secrets of enterprises, and improve the risk factor of personal and property safety of users. Common active attack factors are mainly hacking [12](2)Accounting information is intercepted in the process of communication, facing the threat of tampering and theft, and users finally get incomplete or invalid information. Such security problems are mainly caused by virus intrusion and computer configuration. The low configuration computer does not match the data transmission protocol and the antivirus software version is low, resulting in the interruption of information transmission [13]. The problem of virus invasion is more serious, with many kinds and fast spread. Once the virus invades, it will lead to the leakage and loss of enterprise financial and accounting information, resulting in irreparable losses(3)Unconsciously delete enterprise financial accounting information. Such problems are mainly caused by improper operation of users. First, the degree of computer use is not proficient enough and mistakenly enters unsafe websites carrying viruses. Second, it will not use firewall, antivirus software, and other tools [14], resulting in the loss of enterprise financial accounting(4)User identity information is stolen. It can be divided into two cases: one is hacker intrusion online steal user information; the other is that in reality, others maliciously obtain user identity information and freely log on to personal computers [15]. The two situations are serious threats to the security of enterprise financial accounting information, which requires the use of special encryption means and security common sense to strictly prevent

2.2. Hadoop Big Data Platform

Hadoop is a distributed big data platform, which is mainly composed of two core components: HDFS and MapReduce. HDFS is mainly responsible for the distributed storage of data. MapReduce is mainly responsible for distributed computing of data [16].

MapReduce will divide the data set transmitted from HDFS into several independent parts. Then, the map task performs parallel operations on these independent parts to complete their processing. The result of the operation will be transmitted to the reduce task [17]. Generally, the operation results of the intermediate process will be stored in the local disk, and only the final output results and inputs will be stored in HDFS. The HDFS structure is shown in Figure 1.

2.3. Two Improved Hyperchaotic Systems

Hyperchaotic system has higher application value in the field of encryption than chaotic system, because it has more complex dynamic behavior [18].

2.3.1. Improved Hyperchaotic System 1

A three-dimensional continuous autonomous chaotic system is proposed in literature [19], and its state equation is

In formula (1). , , , and are the actual parameters of the three-dimensional system, and , , and are the state variables of the three-dimensional system. The system will produce chaotic attractors when , , , and . At this time, the system will become hyperchaotic and generate three Lyapunov exponents, which are , , and , respectively.

By changing in the term in equation (1) to , a new three-dimensional continuous autonomous chaotic system can be obtained:

After calculation and analysis, the three Lyapunov exponents of the system when , , , and are , , and . Compared with the chaotic system proposed in literature [19], it is obvious that the three Lyapunov exponents of the chaotic system in this study are much larger than those in reference [19]. Therefore, the chaotic system in this study has more complex dynamic characteristics than the chaotic system in literature [19] and is more suitable for the research of big data encryption than the chaotic system in literature [19].

However, the system has some problems, such as high consumption of computing resources and complex structure. Therefore, the state feedback control method is adopted and a fourth dimensional state variable is introduced [20], and set the state variable . A new four-dimensional chaotic system can be obtained by introducing it into the second equation in equation (2):

Compared with the traditional hyperchaotic system, this system has only two nonlinear terms and its structure is simpler. In addition, under the same computing resources, chaotic sequences longer than those in literature [19] can be generated. Therefore, it is more suitable for big data encryption than the chaotic system in literature [19].

2.3.2. Improved Hyperchaotic System 2

A three-dimensional Bao chaotic system is proposed in literature [21], and its mathematical model is

The system consists of continuous autonomous differential equations with two nonlinear terms. In formula (4), , , and are the actual parameters of the three-dimensional system, and , , and are the state variables of the three-dimensional system. The system will produce chaotic attractors when , , and . At this time, the system will be in hyperchaotic state and produce three Lyapunov exponents, which are , , and , respectively.

Similarly, by changing the term in equation (4) to , a new three-dimensional chaotic system can be obtained:

After calculation and analysis, when , , , , and , the three Lyapunov exponents of the system are , , and . Compared with the chaotic system proposed in literature [21], it is obvious that the three Lyapunov exponents of the improved chaotic system are much larger than the Lyapunov exponent of the chaotic system in literature [21]. Therefore, the dynamic characteristics of the improved chaotic system are more complex than literature [21] and are more suitable for the research of big data encryption than literature [21].

Similarly, on the basis of equation (5), a fourth dimensional state variable is introduced and a control parameter , and then add the feedback control term to the first term of equation (5), so that a four-dimensional hyperchaotic system can be obtained:

The system will produce hyperchaotic attractors when , , , , and . It has more advantages in system structure and computing resource consumption than the chaotic system in literature [21].

2.4. Improved AES Encryption Algorithm

S-box is one of the important components of AES algorithm, while the traditional S-box has a short iteration cycle, which leads to the low security of encryption and the possibility of being cracked. Therefore, this study improves the S-box of AES [22]. Figure 2 shows the input/output principle of the AES algorithm.

2.5. Structure and Principle of S-Box

In AES algorithm, S-box operation is a reversible nonlinear transformation operation acting on the status byte, which is defined as

In equation (7), is , and the multiplication field in GF(2⁸) field is

Because the operation is performed in the GF(2⁸) field, the calculation result generated by the operation will also be in the GF(2⁸) field. The final S-box is made of matrix, and the final result is nonlinear, because the multiplication inverse [23] is used in the calculation process.

2.6. Improved S-Box Scheme

The methods to improve the iteration period of S-box include adopting different affine transformation pairs and changing the calculation order of S-box [24]. Therefore, this study uses the new affine transformation pair (A7 and 6F) in reference [25] to improve the traditional S-box. The improved method is to use the new affine transformation to make two affine changes to (A7 and 6F), and the multiplication inverse is required between the two transformations.

3. Algorithm Design

3.1. Two Hyperchaotic Encryption Schemes

In the process of designing chaotic cipher, when the encryption algorithm selects continuous time chaotic system, it is necessary to pay attention to the influence of discretization of continuous chaotic sequence and selection of key parameters on the performance of the algorithm [26]. In addition, the security and practicability of the algorithm should be guaranteed. Based on the above factors, the big data encryption scheme of this study is as follows. (1)Select key parameters. The premise of selecting key parameters is that the chaotic system is in hyperchaotic state. Through experiments and calculations, the parameters when the system reaches the hyperchaotic system are kept unchanged. The selected key parameters are the eight initial values of the above two hyperchaotic systems, which can ensure that the algorithm has a large enough key space [27](2)The chaotic sequence is preprocessed. The first step is to discretize the chaotic system. The fourth-order Runge-Kutta method is used in this study. The second step discards the first 100 values of the iteration sequence. The reason for discarding the first 100 values is to make the generated chaotic sequence more random [28]. The third step is to carry out correlation operation on the chaotic sequence, so that the chaotic sequence can adapt to byte encryption. The calculation method is as follows:

In equation (9), is the modulo de remainder operation, , is the downward rounding operation, and , , , and are the calculated 8 chaotic sequences. Their value range is [0,256](3)Confusion handling. The state variables generated by hyperchaotic system will have certain correlations. When attacked, these correlations will provide certain information for the attacker and improve the probability of being broken. In order to improve the performance of the algorithm and reduce the probability of being broken, this study confuses the generated two groups of chaotic sequences. The methods of confusion treatment are

In equation (10), is an XOR symbol. are hyperchaotic sequences obtained after operation and can be used for big data encryption. Finally, the correlation between these sequences is destroyed, which improves the security of the algorithm [29] (4)Packet encryption. Encrypt the data with the four sequences obtained in step (3).The encryption method is to group the data by bytes, and encrypt every 4 bytes as a group. The specific encryption process is as follows:

In equation (11), is the data plaintext to be encrypted. is the ciphertext obtained after hyperchaotic block encryption. The encryption scheme is shown in Figure 3

3.2. Hyperchaotic System Based on MapReduce and Hybrid Encryption Algorithm of Improved AES

The encryption algorithm of this research is implemented on Hadoop big data platform. The MapReduce programming module in Hadoop platform is used to program the encryption algorithm. Figure 4 shows the core logic for the MapReduce programming module to run.

MapReduce consists of map and reduce functions. The map function is responsible for the fusion of the above hyperchaotic data encryption and the improved AES algorithm [30]. The reduce function is responsible for merging all data after data encryption. The specific steps are as follows: (1)Slice large data sets. The large data set stored on HDFS is divided into blocks according to the default size of Hadoop 2.0, and each block is 128 MB(2)MEP mixes hyperchaotic system with improved AES algorithm. First, the MEP function reads the data set after slicing and reads it in the way of key value pair [31]. represents the input key value pair. represents the output key value pair. The first step is to select the initial parameters of the chaotic system. The second step is to encrypt according to the scheme in Figure 1. The third step is to use the improved AES algorithm for data encryption. The pseudocode of the hybrid encryption algorithm is as follows:

Input: Z_CHAOS, Z_AES,
Output:
call farctional−hyperchaos−I(Z_CHAOS)
callfarctional−hyperchaos−II(Z_CHAOS)
W(k)←

←C(z)
call AES−encrypt(ZAES)
return

Among them, Z_CHAOS is the key of hyperchaotic system. Z_AES is the key to improve AES algorithm. (3)Data consolidation. Data merging is realized by the Reduce function. The merged object is the data block output from Map encrypted by the encryption algorithm. Before encryption, data blocks need to be sorted by Shuffle [32]. is the input of the reduce function. is the output of the reduce function(4)After the data is merged, it will be stored on HDFS. After the storage, the whole encryption process will be completed

The design of the same decryption algorithm is basically the same as the encryption algorithm. The only difference is that the map function in the decryption algorithm performs decryption rather than encryption. Only when the decryption key and the encryption key match exactly can the original plaintext data be obtained. If there is no exact match, the plaintext data [33] cannot be obtained. Because the hyperchaotic system with more complex dynamics and AES algorithm with long iteration period are adopted, the security of the encryption algorithm in this study has been greatly improved.

4. Experimental Analysis

In order to verify the security and performance of the data encryption method proposed in this study, the data encryption test is carried out in the big data environment. The experimental environment is set as follows: in corei5-7500CPU@3.40GHz, the virtual machines are arranged with the help of virtual simulation platform in the computer environment with 64 GB memory. Hadoop2.7.3 as the operating environment builds Hadoop big data transmission platform environment. Give the virtual machine a single core CPU configuration with a memory size of 1 GB. In this test, five groups of 2 GB data packets are selected as the security test set of encryption algorithm. Set a 10 GB data packet as the data encryption test set in the big data environment to meet the needs of different test objectives.

4.1. Algorithm Security Analysis

Based on this method, five groups of 2 GB data are tested for encrypted transmission, and hackers are artificially set up to maliciously crack the encrypted data in transmission. When all data is encrypted and transmitted, all data packets are intact without malicious damage by hackers. Record the transmission delay of five groups of data packets encrypted by this method, as shown in Table 1.

Table 1 shows that with the increase of encrypted data transmission, the data transmission delay increases synchronously. But the growth rate is getting smaller and smaller. The average delay of 1.0 GB packet is 0.89 s. The average delay of 2.0 GB packet is 1.1 s. The delay time overhead is short, which is within the acceptable range of data encryption delay.

4.2. Algorithm Encryption Efficiency Analysis

The original intention of this method is to adapt to the encryption of enterprise financial accounting data in the big data environment. Therefore, the data encryption test is carried out in the MapReduce parallel computing framework of Hadoop platform. DES algorithm and ECC algorithm are used as comparative encryption test methods. The three methods take 10 GB data packet as the data encryption object and record the efficiency of the three methods in dealing with large-scale data encryption. The results are shown in Figure 5.

According to the analysis of Figure 5, when the number of cluster nodes is 1 or 2, the encryption time of this method is above DES algorithm and ECC algorithm, but the time overhead is not much different. With the increase of cluster data, the encryption time of this method for large-scale data encryption gradually decreases, while the reduction trend of encryption time of the other two methods is not obvious. In contrast, the encryption efficiency of this method is improved quickly.

Here, this method gives full play to its advantage of implementing data encryption in the MapReduce parallel computing framework of big data Hadoop platform. In the whole encryption process, this method stores encrypted data based on HDFS file system, uses Map function [34] to perform AES algorithm encryption operation, and uses Reduce function [35] to merge the encrypted data information. This method gives full play to its high-performance data encryption function under the framework of parallel computing. Therefore, large-scale data encryption can be completed in a short time. Compared with the general DES data encryption algorithm and ECC data encryption algorithm, it has significant efficiency advantages and can meet the needs of people in modern information society for high-speed data encryption.

During the test, taking five groups of 2 GB data packets as the object, the memory occupancy of three methods of encryption was tested. The results are shown in Table 2.

Table 2 shows that the memory usage of the three data encryption methods varies greatly. Among them, the proportion of memory occupied by the method in this paper is the most stable and the consumption is small, which is about 5.38% of the overall operating environment. The small footprint of the proposed method is also attributed to the design of its parallel computing framework. In the MapReduce computing framework model, data encryption processing is accelerated at the same time, which saves the amount of data encryption computation to a certain extent, thus reducing the memory occupancy rate of the proposed method.

The memory occupation interval of DES data encryption algorithm is [12.7%,18.6%]. When encrypting packets of the same size, the memory occupation is unstable and fluctuates greatly. The memory occupation interval of ECC data encryption algorithm is [15.3%,22.7%], which has the same problem as DES data encryption algorithm, and the memory occupied by this algorithm is up to 22.7%, which shows that this method has large amount of calculation and high complexity of encryption process.

5. Conclusions

Maintaining the security of enterprise financial accounting information is a long-term work. Users should not only have common sense of accounting data use and correct computer operation ability but also actively use advanced data encryption technology to improve the safety factor of enterprise financial accounting data use. AES symmetric encryption algorithm has acquired good encryption results all over the world. This research puts forward a new encryption scheme of enterprise financial accounting information in the big data environment. The scheme uses MapReduce to combine the double hyperchaotic encryption algorithm with the improved AES algorithm to generate a new encryption algorithm. It provides security and convenience guarantee for the transmission of enterprise financial accounting information in the information age.

The performance improvement of AES symmetric encryption algorithm in the future depends on the design of hybrid algorithm. For example, the mixing of AES algorithm and ECC algorithm, RSA algorithm, and hyperchaotic encryption technology can meet the performance requirements of AES algorithm from different angles. In addition, the research on the combination of AES algorithm and mathematical calculation is gradually increasing, trying to break through the encryption strength of AES algorithm again from the perspective of mathematical calculation. As the smart mobile terminal technology upgrades, AES encryption algorithm gradually transitions from the field of computer data encryption to diversified fields such as smart phone encryption and smart card device encryption. More potential capabilities of AES encryption technology need to be deeply explored.

Data Availability

The labelled data sets used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no competing interests.

Acknowledgments

This work is supported by the Hebei Oriental University and Langfang Normal University.

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Copyright © 2022 Ran Zhong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Wireless Communications and Mobile Computing

Artificial Intelligence and Computing on Industrial Applications

Research on Enterprise Financial Accounting Information Security Model Based on Big Data

Abstract

1. Introduction

2. Related Research

2.1. Analysis of Factors Affecting the Security of Enterprise Financial Accounting Information

2.2. Hadoop Big Data Platform

2.3. Two Improved Hyperchaotic Systems

2.3.1. Improved Hyperchaotic System 1

2.3.2. Improved Hyperchaotic System 2

2.4. Improved AES Encryption Algorithm

2.5. Structure and Principle of S-Box

2.6. Improved S-Box Scheme

3. Algorithm Design

3.1. Two Hyperchaotic Encryption Schemes

3.2. Hyperchaotic System Based on MapReduce and Hybrid Encryption Algorithm of Improved AES

4. Experimental Analysis

4.1. Algorithm Security Analysis

4.2. Algorithm Encryption Efficiency Analysis

5. Conclusions

Data Availability

Conflicts of Interest

Acknowledgments

References

Copyright