Supply chain management with secured data transmission via improved DNA cryptosystem

Abstract

Supply chain management (SCM) is most significant place of concentration in various corporate circumstances. SCM has both designed and monitored numerous tasks with the following phases such as allocation, creation, product sourcing, and warehousing. Based on this perspective, the privacy of data flow is more important among producers, suppliers, and customers to ensure the responsibility of the market. This work aims to develop a novel Improved Digital Navigator Assessment (DNA)-based Self Improved Pelican Optimization Algorithm (IDNA-based SIPOA model) for secured data transmission in SCM via blockchain. An improved DNA cryptosystem is done for the process of preservation for data. The original message is encrypted by Improved Advanced Encryption Standard (IAES). The optimal key generation is done by the proposed SIPOA algorithm. The efficiency of the adopted model has been analyzed with conventional methods with regard to security for secured data exchange in SCM. The proposed IDNA-based SIPOA obtained the lowest value for the 40% cypher text is 0.71, while the BWO is 0.79, DOA is 0.77, TWOA is 0.84, BOA is 0.83, POA is 0.86, SDSM is 0.88, DNASF is 0.82 and FSA-SLnO is 0.78, respectively.

Keywords

Blockchain supply chain management DNA cryptosystem data preservation IAES encryption

Nomenclature

SCM

Supply chain management

SOC

Service-On-Chain

OCBS

Off-Chain Blockchain Systems

WOA

Whale Optimization Algorithm

WNU

Whale with New Crosspoint-based Update

IAES

Improved Advanced Encryption Standard DNA Digital Navigator Assessment

SIPOA

Self-Improved Pelican Optimization Algorithm

BWO

Black Widow Optimization

DOA

Dingo Optimization Algorithm

TWOA

Team Work Optimization Algorithm

BOA

Butterfly Optimization Algorithm

POA

Pelican Optimization Algorithm

SDSM

Secure Data Sharing Scheme

DNASF

DNA-based Secure and Fast

FSA

SLnO Fitness-based Self-adaptive Sea Lion Optimization

PANDA

Pose Aligned Networks for Deep Attribute Modeling

SOC

System-On-Chips

1. Introduction

Supply chain technology is a category of business processes that includes specific networks, organizations, and technological operations that are concerned with getting the finished goods from the vendor to the consumer [9,15,16,29]. According to the definition of SCM [2,17,18], it is “a variety of strategies used to effectively integrate suppliers, transporters, manufacturers and clients, so that goods are manufactured and shipped at the correct amounts, to the correct places, and at the correct time, to minimize system-wide costs while accomplishing service level needs”. Generally, these supply chains [27] have information, resources, and financial flows between different organizational sectors, such as from a supplier to the producer and lastly to the client.

Moreover, SCM [14,31,36] involves both the coordination and integration of various flows (resources, finances, and information flows) both within and across the companies in order to guarantee efficient performance with fewer risks. Nevertheless, the level of dedication and participation among partners has a significant impact on the effectiveness of a collaborative effort. The primary reason for the SCM’s failure is directly related to the lack of openness and trust. For this SCM problem in secured information sharing, blockchain technology [13,33] is a solution. Blockchain technology is described as “the fundamental technology underlying bitcoin, in which computers belonging to various independently owned entities adhere to a cryptographic protocol to continuously validate updates to a publicly accessible ledger.”Every transaction that occurs on the network is recorded in a digital ledger, and a block is a collection of several transactions. Each block includes transactions that include data, a hash, as well a preceding hash [5,8]. All parties involved may benefit from the smart contract’s definition of the fundamental functions for security and integrity [30]. However, blockchain technology research and adoption in SCM are still in their development and face many obstacles before they can provide a high level of security. To address difficult issues, a variety of modifications and enhancements have been made to optimization methods.

A message is converted into a cipher text by encryption methods, which may then be decrypted with a key and read by an authorized user [23]. To maintain a high level of security, a finite number of transactions can be handled in a brief amount of time. Hence, it has become difficult to balance security and block size. Providers are permitted to develop, arrange, and route applications that have low failure rates, scalability, and easy propagation in terms of performance [7]. Intermediation in blockchain-based SCM [12] can also result in less visible information, which raises costs and lowers performance. Consequently, it is clear that a well-developed infrastructure must be created in order for SCM to be effective, and blockchain may do this by offering optimization techniques [11,20]. Yet, there is still a problem with the level of security and privacy protection. Because there is no centralized authority, each owner has a copy of the full blockchain, giving everyone access to it, including other people’s sensitive information. Wired and wireless technologies are frequently used in SCM for data transmission, both of which have dependability problems, particularly the wireless network when transferring huge volumes of data. Disruption such as electromagnetic signals, signal superposition, and metal reflection, during communication channels easily affect signals like radio waves, microwaves, electrical voltages, and infrared signals. Under enormous data transmission, minor changes may cause a butterfly effect. This means that a small change in a signal can have a significant impact on a subsequent state. In addition, security concerns are a top priority in the majority of service sectors, including banking, insurance, and healthcare. To overcome these limitations, a novel IDNA-based SIPOA algorithm is proposed in this work for optimal key generation for secure data transmission.

The major contributions of the work are as follows:

Introduces improved DNA (IDNA) based encryption model to secure the transmission of data by blockchain.

Deploys IAES for the encryption and decryption of data for more security.

Proposes SIPOA algorithm for optimal key generation during IAES process.

The research organization is as follows: Section 1 is the introduction. Section 2 is an overview of the state-of-the-art techniques. Section 3 describes the IDNA-based SIPOA model. Section 4 offers the results part of the proposed work. Section 5 offers a conclusion.

2. Literature review

In 2020, Mustufa Haider Abidiet al. [1] utilized three basic processes, namely data sanitization, key generation, as well as restoration, and created a novel privacy preservation paradigm in the area of supply chain systems depending on blockchain technology. Also, the crucial fields in the source data are selected throughout the data sanitization and key generation phases; the optimal key was then created to hide the selected critical fields. The encrypted key and secret data were transported from the source to the destination across the supply chain network. With the aid of the same key, the restoration procedure was carried out on the receiver side. Of all of these data flow techniques, the most important problem that must be resolved for the data transmission to be secure was the best key selection strategy. Here, a unique optimization technique called Whale with New Crosspoint-based Update (WNU), the more sophisticated variant of WOA was created to choose the optimal key.

In 2021, Jingkuang Liu et al. [19]suggested a hybrid framework combining X-Alliance (a consensus method-assisted alliance chain) as well as PANDA (a consensus method-assisted public chain). In addition to processing each user’s transaction in parallel, this proposed hybrid chain architecture may also synchronize with unrelated network records, offer more dependable data storage and authority management, and guarantee the ownership of updated tracking data. It improved performance and reduced protection costs in addition to protecting data and privacy security. The experimental findings indicate that when 4 nodes were established, throughput can exceed 1200tps as per network crawling statistics.

In 2020, Zhijun Xu et al. [35] suggested a design plan for an integrated platform-assisted Ethereum blockchain for information services provided by supply chain participants. Blockchain security improvements could be completely utilized due to system design and smart contracts. A data-driven credit assessment scheme that could be implemented on the blockchain was proposed, and a cross-chain infrastructure was created to increase the system’s security, intelligence, and scalability. Several common and important issues with the blockchain-based method were also examined and solved.

In 2019, Kyungyong Chung &Hoill Jung [6] presented a knowledge-assisted blockchain system for a mobile service to manage health record data. In order to keep a significant amount of constantly accumulated user log data and context data inside a block within the knowledge base by using a side supply chain that holds data through the setup of a knowledge-based transaction process, the knowledge-based health framework applies blockchain technology, which was hard to fake and falsify. This makes it possible to provide high expandability as well as protection in mobile environments as well.

In 2021, Lennart Bader et al. [4] introduced PrivAccIChain, a private, secure framework for enhancing multi-hop retrieval of information with stakeholder responsibility along supply chains. This research specifically provides a flexible configuration of visibility and data security within the architecture to satisfy use case-specific requirements. With the help of this study, supply chain participants with a history of mistrust can nevertheless benefit from information exchange and multi-hop monitoring and tracing. Based on data from a purchasable automobile, this article assesses PrivAccIChain’s performance and demonstrates its viability in the real world.

In 2021, Limei Wang and Yun Wang [34] created a supply chain to target medium-sized and small companies in an innovative effort by banks servicing the real economy to support changes in the business development models. The term edge computing refers to a platform that combines network, data analysis, storage, and application core functions in order to address real-time, application knowledge, security, and privacy concerns. It might have provided the end-of-page service that was most convenient for the object data source. Developing an optimum approach that could be effectively controlled was the major objective of SCM.

In 2021, Chunhui Piao et al. [26] suggested a SOC strategy which may efficiently share government data with trustworthy data content and controllable data ownership while properly identifying the data retrieval needs of various departments. To construct data-sharing agreements across government agencies, the researcher used smart contracts in order to offer an on-chain service that could identify unclear data-retrieving requests and formalize the process logic. This research utilized the Service-On-Chain (SOC) method in a real-world scenario and shows that it offered a workable solution for safe and effective data sharing between government agencies.

In 2021, Ken Miyachi and Tim K. Mackey [21] presented an Off-Chain Blockchain Systems (OCBS) paradigm that makes use of off-chain resources and distributed software architecture to facilitate information processing and administration. As a result, OCBS were a crucial part of data governance when designing enterprise blockchain solutions. This has led to brief research and development into how on-chain as well as off storage and computation interact, as well as efforts to assess how well they perform in comparison to other systems for managing data. The capacity of OCBS was to increase scalability, decrease the amount of data storage needed, and enhance information privacy are some of their key qualities. These were all very important concerns to enable widespread blockchain usage.

In 2022, Yu et al. [37] developed a novel SDSM technique for IoT by consolidating the blockchain and cipher text-based attribute cryptography. Here, the ranking of partnerships is computed via the smart contacts to identify partnerships based on the historical transaction facts of block chain. By enabling fine-grained access control, personalized attributes of participants in the ciphertext-based attribute encryption algorithm are produced in SDSM to support the partnership based on the construction access restrictions.

In 2021, SuyelNamasudra [22] introduced a novel DNA computing-based secure and Fast Access Control model to diminish the challenges faced in data security and access control. Initially, the list of fast data access is kept in the cloud service provider. A random key which is based on 1024-bit DNA computing is created via the user’s secret information and data encryption is carried out via the same random key. At last, the effectiveness of the developed model is verified with the conventional methods.

In 2023, Aljabhan et al. [3] developed the novel perceptive craving game search (PCGS) optimization technique by optimally generating the key for data sanitization. By employing an optimal key generated by the PCGS optimization algorithm to sanitize the original logistics data obtained from the manufacturer, the risk of unauthorized access and data swarms that impede system performance is removed. Moreover, the authorized parties obtain the sanitized data that was gathered from the manufacturer via a number of sub-chains. On the receiving client side, the sanitized data is utilized to recreate the original information using the same created key.

In 2023, A. H. Sadeghi et al. [28] established the chance-constrained technique to handle the uncertainty of the restrictions. The sequential quadratic programming (SQP) exact algorithm is used to assess the performance of the novel metaheuristics, whale optimization algorithm (WOA) and which are provided as solution techniques for the nonlinearity of the model. The Taguchi approach for experiment design is used to calibrate the parameters of algorithms. By using many comparison measures and solving multiple numerical findings of varying sizes, extensive analysis is established. Additionally, appropriate parametric and non-parametric tests are used to statistically compare the outcomes.

In 2023, Pavithran et al. [25] developed a novel cryptosystem that uses finite-state machines and DNA cryptography. Here, the system is strengthened through the use of finite-state machines to substitute the DNA sequence. In addition, this research suggests a DNA character conversion table to make the ciphertext more random. The effectiveness of the suggested plan is evaluated in relation to the ciphertext’s unpredictability. The security of a cryptosystem is dependent on the ciphertext’s randomness, which is evaluated using randomness tests found in the National Institute of Standards and Technology (NIST) test suite.

In 2022, Khasim et al. [10] established a system that protects patients’ private, sensitive healthcare data from adversaries and the Authorization Service while maintaining their anonymity. In our developed work, we added extra security to the network layer and anonymized health records using a rotating panel signature method based on camels. Theoretical analysis was used to evaluate the programs’ efficacy, and the results showed that the program has a variety of security features and is resilient to numerous attacks.

Features and difficulties of the conventional technique are listed in Table 1.

Table 1
List of features and challenges in the traditional methods

Author [citation] Methods Features Challenges

Mustufa Haider Abidi et al. [1] WNU model High-level security Low convergence

Jingkuang Liu et al. [19] PANDA, X-Alliance model ∙ Improved transaction speed
∙ Improved privacy protection
∙ Less transaction cost
It could be challenging to figure out a balance between consensus efficiency, security, as well as cost.

Zhijun Xu et al. [35] Scalable cross-chain model ∙ More secure intelligent
∙ More scalable
It was difficult to implement a full blockchain cross-chain platform according to the high-level paradigm.

Kyungyong Chung & Hoill Jung [6] Knowledge-based blockchain network ∙ Higher accuracy
∙ Higher reproducibility Blockchain transactions require a lot of time.

Lennart Bader et al. [4] PrivAccI Chain model ∙ Reliable accountability
∙ Reliable verifiability Designing policies was difficult.

Limei Wang and Yun Wang [34] IoT data management system It has major practical and theoretical implications for promoting commercial banks’ and businesses’ expansion. Slow consensus speed

Chunhui Piao et al. [26] SOC model Highly efficient and secure in sharing data Realizing data accountability through government data sharing is challenging.

Ken Miyachi and Tim K. Mackey [21] OCBS model ∙ Improved data portability
∙ Improved transparency
∙ Improved patient-centred data stewardship The lack of off-chain data, which must be acquired and verified prior to the computing phase, causes OCBS, which slows down the blockchain’s output.

Yu et al. [37] SDSM ∙ Trusted data sharing in a single supply chain
∙ Avoids arbitrariness in determining partnership level
∙ Less computational effort Focus on secure data sharing approach across various supply chains

Suyel Namasudra [22] DNASF ∙ Improved data security
∙ Reduced overhead ∙ Identity management technique

Aljabhan et al. [3] GSO ∙ Better efficiency ∙ Validity of trust is needed

A. H. Sadeghi et al. [28] WOA ∙ Less transaction cost ∙ Security threats may occur during the transmission

Pavithran et al. [25] DNA ∙ Sensitive and private client data must be shielded from hackers and attackers. ∙ Both user authentication and fine-grained access control are not taken into consideration

Khasim et al. [10] Smart healthcare systems ∙ The recipient has the ability to confirm receipt of communications. ∙ It is not always easy to determine the statistical significance in machine learning scenarios.

Author [citation]	Methods	Features	Challenges
Mustufa Haider Abidi et al. [1]	WNU model	High-level security	Low convergence
Jingkuang Liu et al. [19]	PANDA, X-Alliance model	∙ Improved transaction speed ∙ Improved privacy protection ∙ Less transaction cost	It could be challenging to figure out a balance between consensus efficiency, security, as well as cost.
Zhijun Xu et al. [35]	Scalable cross-chain model	∙ More secure intelligent ∙ More scalable	It was difficult to implement a full blockchain cross-chain platform according to the high-level paradigm.
Kyungyong Chung & Hoill Jung [6]	Knowledge-based blockchain network	∙ Higher accuracy ∙ Higher reproducibility	Blockchain transactions require a lot of time.
Lennart Bader et al. [4]	PrivAccI Chain model	∙ Reliable accountability ∙ Reliable verifiability	Designing policies was difficult.
Limei Wang and Yun Wang [34]	IoT data management system	It has major practical and theoretical implications for promoting commercial banks’ and businesses’ expansion.	Slow consensus speed
Chunhui Piao et al. [26]	SOC model	Highly efficient and secure in sharing data	Realizing data accountability through government data sharing is challenging.
Ken Miyachi and Tim K. Mackey [21]	OCBS model	∙ Improved data portability ∙ Improved transparency ∙ Improved patient-centred data stewardship	The lack of off-chain data, which must be acquired and verified prior to the computing phase, causes OCBS, which slows down the blockchain’s output.
Yu et al. [37]	SDSM	∙ Trusted data sharing in a single supply chain ∙ Avoids arbitrariness in determining partnership level ∙ Less computational effort	Focus on secure data sharing approach across various supply chains
Suyel Namasudra [22]	DNASF	∙ Improved data security ∙ Reduced overhead	∙ Identity management technique
Aljabhan et al. [3]	GSO	∙ Better efficiency	∙ Validity of trust is needed
A. H. Sadeghi et al. [28]	WOA	∙ Less transaction cost	∙ Security threats may occur during the transmission
Pavithran et al. [25]	DNA	∙ Sensitive and private client data must be shielded from hackers and attackers.	∙ Both user authentication and fine-grained access control are not taken into consideration
Khasim et al. [10]	Smart healthcare systems	∙ The recipient has the ability to confirm receipt of communications.	∙ It is not always easy to determine the statistical significance in machine learning scenarios.

2.1. Review

Some of the challenges observed from the literature review. WNU model [1] has a low convergence rate as well as low accuracy. In the PANDA model [19], finding a balance between consensus efficiency, security, and cost can be challenging. In the Scalable cross-chain model [35], it was difficult to implement a full blockchain cross-chain platform according to the high-level paradigm. In a knowledge-based blockchain network [6], Blockchain transactions require a lot of time. Designing policies was difficult in the PrivAccIChain model [4]. IoT data management system [34] has slow consensus speed. Realizing data accountability through government data sharing is challenging while using the SOC model [26]. Due to the absence of off-chain data that must be obtained and confirmed before the computing phase, OCBS [21] slows down the output of the blockchain. GSO [3] requires more validity of trust in data transmission. WOA [28] has security threats that may occur during transmission. Both user authentication and fine-grained access control are not taken into consideration in DNA cryptoplasm [25]. Smart healthcare system is not always easy to determine the statistical significance in machine learning scenarios [10].

3. Proposed supply chain management with secured data transmission via improved DNA cryptosystem

From the acquisition of raw materials, until the shipment arrives at its intended location, SCM is the supervision of the flow of information, material, and financial resources associated with a good or service. By defending the coordination of processes as well as communication among involved parties against potential security threats, privacy may be archived. Blockchain has been applied to the supply chain in several ways recently to address security and privacy concerns. Significantly, blockchain-combined supply chain systems are good at ensuring data security and privacy, which prevents data records from being changed or used inappropriately.

This paper aims to develop a new secure data transmission in SCM, where blockchain-based data storage is considered. Also, the privacy of data sharing among the manufacturers, suppliers and customers is really important. Hence the data encryption process is a need in this way. An improved DNA (IDNA) based encryption is proposed in this paper, where the key generation is made optimal via a self-improved Pelican Optimization Algorithm (SIPOA). Thereby, the proposed model ensures secured transmission of data from one end to another. Finally, the decryption process is performed (i.e., the reverse process) on the receiving end. Figure 1 illustrates the suggested privacy preservation plan for blockchain technology.

Fig. 1.

An optimization-based block diagram of the suggested SCM in a blockchain.

3.1. Suggested supply chain framework and data preservation method: An overview

3.1.1. Suggested supply chain

Figure 2 describes the suggested supply chain framework. It has four levels: Level 1 is for the manufacturer, Level 2 is for the manager, Level 3 is for delivery, and Level 4 is for the vendor. The key steps taken in the secured blockchain technology are demonstrated as follows: Manufacturers from various companies typically construct their own databases containing various entries, like Item Description, Item Quantity, Brand, Price, Managed By (Manager), Weight (Kilograms), Shipping Method (Delivery), Vendor, etc. With a series of blocks encapsulating the records, the blockchain appears to be an unending chain. The data has only been sanitized because it does not differ and is not sensitive, with the exception of the manufacturer, manager, delivery, and vendor details. The concerned manager has the opportunity to retrieve the data block assigned to them once the manufacturers’ blockchain reaches the managers, where they may then establish their own blockchain. The suppliers and delivery construct the blockchain in a manner akin to that of the management and delivery. Managers, delivery personnel, and vendors in particular can only evaluate the data that has been assigned to them; the sensitive information of others is opaque. After then, the blockchain is delivered to the vendor, which is where data restoration happens. The vendor with the best key can get to the original data and retrieve the private information.

Fig. 2.

Suggested SCM.

In the recent findings, the total amount of blocks in each level and the average time required for information sharing at each level are shown in Table 2.

Table 2

Block count and the average amount of time needed for information transfer at each level

Level	Block count	Average time (in seconds)
1	88	13.516
2	4	27.48
3	2	41.726
4	73	16.32

Table 3 displays a live illustration of the suggested supply chain for an e-commerce platform. It includes five distinct manufacturers producing a variety of products, such as A (Mobile), B (vegetables), C (Accessories), D (Stationary), and E (Books). Manufacturers (A, B, C, D, and E) form the database and it contains data on the item’s brand, quantity, price, weight (in kilogrammes), manager, shipment type (delivery), and vendor. The fields that are sensitive in this case are the Brand, Item Description, Item Quantity, and Price. According to concern information (delivery, manager, vendor, item description, brand/authors, item quantity and shipment type), the manufacturers A, B, C, D, and E each construct their own blockchain, as illustrated in Fig. 3. A, B, C, D, and E were indicated as $B^{(1)}$ , $B^{(2)}$ , $B^{(3)}$ , $B^{(4)}$ , and $B^{(5)}$ . Together, they create a blockchain B. Every block $B_{1}$ is then transmitted to the manager with the identifier $B_{1}^{(1)}$ , $B_{1}^{(2)}$ , $B_{1}^{(3)}$ , $B_{1}^{(4)}$ , $B_{1}^{(5)}$ . The blockchain of the manufacturers A, B, C, D, and E is described in Fig. 3. In order to construct a new blockchain $B_{2}$ containing the vendor, item description, delivery, quality, brand, and shipping type information, the concern manager (M1, M2, M3, M4, and M5) accesses their data block. The block on the blockchain that consists of M1, M2, M3, M4, and M5 is indicated as $B_{2}^{(1)}$ , $B_{2}^{(2)}$ , $B_{2}^{(3)}$ , $B_{2}^{(4)}$ , $B_{2}^{(5)}$ .

Table 3

Illustration of supply chain

Non-Sensitive Fields				Sensitive Fields

Manufacturer	Manager (M)	Delivery (D)	Vendor (V)	Item Description (ID)	Item Quantity (Q)	Brand/authors (B)	Shipment type (S)
A	1	1	1	Honor 8X (ID1)	5(Q1)	Huawei (B1)	Air (S1)
B	2	2	2	Brinjal (ID2)	2(Q2)	Nil (B2)	Truck (S2)
C	5	4	3	Sound Peats smartwatch (ID3)	1(Q3)	Sound Peats (B3)	Air Charter (S3)
D	4	2	4	Camlin notebook (ID4)	20(Q4)	Kokuyo Camlin (B4)	Drones (S4)
E	2	1	5	Operational Research Book (ID5)	3(Q5)	Dr. R.K. Gupta (B5)	Drones (S5)
B	4	4	1	Carrot (ID6)	7(Q6)	Nil (B6)	Truck (S6)
C	2	3	2	Boat wireless Bluetooth headset (ID7)	5(Q7)	Boat Rockerz (B7)	Air Charter (S7)
D	5	1	3	Apsara pencil (Multicolor) (ID8)	10(Q8)	Hindustan Pencils (B8)	Air charter (S8)
A	3	5	4	Realme 5 Pro (ID9)	2(Q9)	Realme (B9)	Air charter (S9)
C	1	3	5	Ray-Ban Sunglass (ID10)	6(Q10)	Ray-Ban (B10)	Air charter (S10)
B	2	5	1	Cauliflower (ID11)	10(Q11)	Nil (B11)	Truck (S11)
E	3	3	4	Data Structures and Algorithms Made Easy: Data Structures and Algorithmic Puzzles (ID12)	2(Q12)	Narasimha Karumanchi (B12)	Drones (S12)

When the vendor and the connected vendors V1, V2, V3, V4, and V5 receive the delivery-related data, they access it and build a novel blockchain $B_{3}$ . Moreover, blockchain $B_{3}$ includes the blocks $B_{3}^{(1)}$ , $B_{3}^{(2)}$ , $B_{3}^{(3)}$ , $B_{3}^{(4)}$ and $B_{3}^{(5)}$ which, in turn, belong to vendors V1, V2, V3, V4, as well as V5. Specifically, the manager and delivery can review their own data without knowing about other people’s sensitive areas. On the other hand, the vendor can read all manufacturers’ non-sensitive and sensitive data with the ideal key that was established.

Fig. 3.

A sample blockchain of five manufacturers.

3.2. Data privacy preservation using IDNA-based SIPOA model:

IDNA encryption [24] begins with data that includes numbers, alphabets and some other special characters. The data is converted into an improved DNA-coded sequence, and this sequence is used as the encryption process key. In their scenario, IAES is used for encryption. The key $(k)$ generated in the IAES encryption and decryption procedure is optimally generated by the suggested SIPOA algorithm. Figure 4 shows the simplified layout for encryption and decryption.

Fig. 4.

IAES encryption using optimized DNA key.

IAES encryption process IAES is a symmetric encryption algorithm method that uses blocks and chunks of 128 bits for encryption. We transform these discrete blocks with keys of lengths of 128, 192, and 256 bits. After encrypting each block independently, it then joins them to form the cipher text. IAES working principle is represented in Fig. 5.

Fig. 5.

Working principle of IAES.

IAES encryption procedure is given below steps:

Step 1: Initially, the XOR operation is performed for the input plain text and round key represented in Fig. 6.

Fig. 6.

XOR of input plain text and round key.

Step 2: This phase carries out the substitution shown in Fig. 7. Each byte is replaced with another byte in this phase. This substitution is done in a method that guarantees that no single byte is ever replaced both independently and by a byte that completes the current byte. In improved sub-bytes (represented in Fig. 8), the sub-byte output is multiplied by 2 and the even rows of improved sub-byte are interchanged.

Fig. 7.

Sub-byte operation.

Fig. 8.

Improved Sub-byte operation.

Step 3: This phase involves shifting each row of the improved sub-byte a specific number of times, as seen in Fig. 9. The top row is not shifted in this instance. Shift the second row once to the left. Shift the third twice to the left. Shift the fourth row thrice to the left.

Fig. 9.

Row shift operation.

Step 4: Matrix multiplication is done in this step. The positioning of each byte in each column is changed because of multiplying each column by a constant matrix, as shown in Fig. 10.

Fig. 10.

Mix column operation.

Step 5: The final encryption text, shown in Fig. 11, is obtained in this phase by XOR-ing the output of the preceding stage with the associated round key.

Fig. 11.

Adding round key operation.

IAES decryption process The decryption method is done by performing the inverse operation of the IAES encryption procedure by giving the encrypted text as input to the decryption.

3.2.1. Objective function

(Obj)

The IDNA-based SIPOA method is being used with the goal of achieving the objective function outlined for data preservation as stated in Eq. (1), where $f_{1}$ is the hiding failure rate, $f_{2}$ is the modification degree, $f_{3}$ is the preservation ratio, and $f_{4}$ is the relying rate of false data generation. $\begin{matrix} (1) & Obj = Min (fit) = Min {f_{1} + f_{2} + f_{3} + f_{4}} \end{matrix}$

Hiding failure rate $(f_{1})$ The rate of sensitive data that are successfully hidden is referred to as the hiding failure rate and is computed mathematically using Eq. (2), where $N_{d}$ depicts hidden data index count, and $L_{n z}$ depicts non-zero index length. $\begin{matrix} (2) & f_{1} = \frac{N_{d}}{L_{n z}} \end{matrix}$

Modification degree $(f_{2})$ Modification degree is calculated in Eq. (3) by taking the Euclidean distance between the original data D and encrypted data $D^{'}$ . $\begin{matrix} (3) & f_{2} = D - D^{'} \end{matrix}$

Preservation ratio $(f_{3})$ The inverse of data loss, as specified in Eq. (4), is known as the data preservation ratio, which $N_{d}$ depicts hidden data index count and $N_{z}$ depicts zero index count. $\begin{matrix} (4) & f_{3} = \frac{N_{z}}{N_{d}} \end{matrix}$

Relying rate of false data generation $(f_{4})$ The amount of false data produced is known as the relying rate of false data generation, and it is quantified mathematically using Eq. (5). Here, the falsely generated ratio is calculated by dividing the number of encrypted data crossed the lower bound and upper bound $D_{UL}$ by the total number of actual values D. $\begin{matrix} (5) & f_{4} = \frac{D_{UL}}{D} \end{matrix}$

3.3. SIPOA algorithm for optimal key generation [32]:

The SIPOA meta-heuristic optimization technique was developed in response to the hunting strategies of pelicans. Its advantages include few adjustment factors, rapid convergence, and simple calculation. Pelicans are warm-water birds that live largely in lakes, rivers, beaches, and marshes. Pelicans are often sociable and have exceptional flying and swimming skills. They consume fish mostly, are skilled observers, and have excellent vision while flying. When the pelicans find their food, they leap from a height of 10 to 20 meters to sprint towards it, and then they dive directly into the ocean to start hunting. Pelicans will make a line or a U-shape to help them swoop down from the sky and land among a school of fish. The fish will subsequently be forced to swim upward as they use their feathers to flap the water. After that, the fish will be captured and put inside the neck pouch. The foregoing explanation served as the basis for developing the mathematical formalism of the SIPOA algorithm.

Improved initialization In the case of N pelicans in an M-dimensional space, then the location of the ith pelican are $P_{i} = [p_{i 1}, p_{i 2}, \dots, p_{i m}, \dots, p_{i M}]$ and the location of the N pelicans is given by the expression in Eq. (6). Initially, pelicans are dispersed randomly within a certain region, and their location updates are represented by the equation (7), which $u_{m}$ , $l_{m}$ indicates the pelican’s search range, and r is the random integer between (0, 1) which is replaced by the Dyadic transformation map function $(T)$ as per the proposed logic defined in Eq. (8). The dyadic transformation gives an example of how chaos could arise from a simple 1-dimensional map. $\begin{array}{c} (6) & P = [\begin{array}{c} P_{1} \\ ⋮ \\ P_{i} \\ ⋮ \\ P_{N} \end{array}] = [\begin{array}{c} p_{11} \dots p_{12} \dots p_{1 M} \\ ⋮ \\ p_{i 1} \dots p_{i 2} \dots p_{i, M} \\ ⋮ \\ p_{N 1} \dots p_{N 2} \dots p_{N M} \end{array}], i = 1, 2, \dots, N \\ (7) & P_{i m} = l_{m} + r \times (u_{m} - l_{m}); i = 1, 2, \dots, N; m = 1, 2, \dots, M \\ (8) & P_{i m} = l_{m} + T \times (u_{m} - l_{m}); i = 1, 2, \dots, N; m = 1, 2, \dots, M \end{array}$ Here, $\begin{matrix} T = \{\begin{array}{ll} 2 x, & 0 ⩽ x ⩽ \frac{1}{2} \\ 2 x - 1, & \frac{1}{2} ⩽ x ≺ 1 \end{array} \end{matrix}$

Improved exploration During this stage, the pelican finds its prey and dives down to it from a considerable height. The potential of pelican to explore the search space is improved by the prey’s random distribution, and each repetition of the search process updates the pelican’s location as shown in Eq. (9), where t is the current iteration, T is the maximum iteration, $S_{m}^{t}$ is the prey position at mth dimension, λ is random which is equivalent to (1, 2), $F (P_{S})$ is the objective function, and $F (P_{i})$ is the fitness function at mth dimension. As per the proposed logic, pelican’s location update is defined as in Eq. (10), where θ depicts a dynamic weighting vector. $\begin{array}{c} (9) & P_{i m}^{t + 1} = \{\begin{array}{ll} P_{i m}^{t} + rand \times (S_{m}^{t} - λ \times P_{i m}^{t}), & if F (P_{S}) ≺ F (P_{i}) \\ P_{i m}^{t} + rand \times (P_{i m}^{t} - S_{m}^{t}), & if F (P_{S}) ⩾ F (P_{i}) \end{array} \\ (10) & P_{i m}^{t + 1} = \{\begin{array}{ll} θ = \frac{e^{2} (1 - \frac{t}{T}) - e^{- 2} (1 - \frac{t}{T})}{e^{2} (1 - \frac{t}{T}) + e^{- 2} (1 - \frac{t}{T})} \\ P_{i m}^{t} + rand \times (S_{m}^{t} - [λ (E_{i m}^{t} + δ \cdot E_{i m}^{t} \times E_{i m}^{t} + ψ)] / 3 \times (P_{i m}^{t}) \cdot θ), & if F (P_{S}) ≺ F (P_{i}) \\ P_{i m}^{t} + rand \times (P_{i m}^{t} - S_{m}^{t}) \cdot θ, & if F (P_{S}) ⩾ F (P_{i}) \end{array} \end{array}$ Here, $\begin{matrix} E_{i m}^{t} = \frac{T - t}{T}, ψ = \{\begin{array}{ll} 1, & fit (t) ≺ best_fit, \\ 0, & otherwise, \end{array} δ = \frac{{fit}_{min}}{{fit}_{E_{i m}^{t}}} \end{matrix}$

Fig. 12.

Flowchart of the proposed SIPOA algorithm.

Exploitation Once reaching the water’s surface, pelicans spread their wings to hoist fish into the air, then scoop the meal into their throat pouches. Equation (11) provides a mathematical simulation of this pelican hunting behavior. where λ is the random number which is equivalent to $(1, 2)$ . $\begin{matrix} (11) & P_{i m}^{t + 1} = P_{i m}^{t} + γ \times (\frac{T - t}{T}) \times (2 \times rand - 1) \times P_{i m}^{t} \end{matrix}$

Algorithm 1

Pseudocode of IDNA-based SIPOA for optimal key generation

Figure 12 displays the flowchart of the suggested SIPOA algorithm. Algorithm 1 shows the pseudo-code of the proposed model.

4. Result and discussion

4.1. Simulation procedure

The suggested secure data transmission in supply chain management was executed in MATLAB and the findings are described below. The outcomes of the proposed are computed over the standard methodologies like Black Widow Optimization (BWO), Dingo Optimization Algorithm (DOA), Team Work Optimization Algorithm (TWOA), Butterfly Optimization Algorithm (BOA), Pelican Optimization Algorithm (POA), Game search optimization (GSO) [3], whale optimization algorithm (WOA) [28], Secure Data Sharing Scheme (SDSM)[37], DNA based Secure and Fast ACM (DNASF) [22], Fitness based Self-Adaptive Sea lion Optimization (FSA-SLnO) [13] and LSTM+MDNBO [15], respectively. Furthermore, the assessment was carried out in terms of Key Sensitivity, Euclidean Distance, Convergence, attack analysis and so on. Table 4 shows the system configuration.

Table 4
System configuration

Device Specifications

Processor AMD Ryzen 5 3450U with Random Vega Mobile Gfx 2.10 GHz

Installed RAM 16.0 GB (13.9 GB usable)

System type 64-bit operating system, x64-based processor

Windows Specifications

Edition Windows 11 Pro

Version 22H2

Device Specifications
Processor	AMD Ryzen 5 3450U with Random Vega Mobile Gfx 2.10 GHz
Installed RAM	16.0 GB (13.9 GB usable)
System type	64-bit operating system, x64-based processor
Windows Specifications
Edition	Windows 11 Pro
Version	22H2

4.2. Assessment of proposed IDNA-based SIPOA regarding CCA, CPA, KCA and KPA attack analysis for secure data transmission in supply chain management

The attack analysis on proposed IDNA-based SIPOA is contrasted over the GSO [3], WOA [28], BWO, DOA, TWOA, BOA, POA, SDSM [37], DNASF [22], FSA-SLnO and LSTM+MDNBO for secure data transmission in supply chain management is represented in Fig. 13. Also, the attack analysis was evaluated with respect to CCA, CPA, KCA and KPA attacks by altering the cipher text from 10%, 20%, 30%, 40% and 50%, respectively. The CCA attack is known as “An attack strategy on cryptanalysis known as a chosen-ciphertext attack (CCA) allows the cryptanalyst to collect data via acquiring the decryptions of specified ciphertexts.” While analyzing Fig. 13(a), the proposed IDNA-based SIPOA model affords high security to the information during transmission in the supply chain management and it protects the data from attackers. More particularly, the proposed IDNA-based SIPOA obtained the lowest value for the 40% cipher text is 0.71, whilst the GSO [3] is 0.77, WOA [28] is 0.82, BWO is 0.79, DOA is 0.77, TWOA is 0.84, BOA is 0.83, POA is 0.86, SDSM [37] is 0.88, DNASF [22] is 0.82 and FSA-SLnO is 0.78, respectively. The CPA is said to as “A chosen-plaintext attack (CPA) is indeed a type of cryptanalysis attack which assumes the attacker has access to the plaintext ciphers for every given plaintext. The attack’s objective is to obtain data that will render the encryption system less secure.” According to the description, a more secure strategy is one that has a lower value. Likewise, the proposed IDNA-based SIPOA recorded the minimal CPA attack rating in all the ciphertext. Mainly, in the 10th ciphertext, the proposed IDNA-based SIPOA gained the reduced ratings of 0.27, which is extremely lower than GSO, WOA, BWO, DOA, TWOA, BOA, POA, SDSM [37], DNASF [22], FSA-SLnO and LSTM+MDNBO, respectively.

The KPA attack is described as “The known ciphertext assault is a cryptanalysis attack type in which the attacker is presumptively limited to a particular set of ciphertexts.” In accordance with the Fig. 13(c), the proposed IDNA-based SIPOA protect the data from the attackers and it acquired the lowest attack ratings of 0.7 in the 4000 records, meanwhile the traditional methods generated the highest ratings, notably, GSO = 0.76, WOA = 0.77, FSA-SLnO = 0.73, LSTM+MDNBO = 0.75, DNASF [37] = 0.77 and POA = 0.91, respectively. The KPA attack analysis is described as “The known-plaintext attack (KPA) is a type of cryptanalysis assault in which the attacker obtains accessibility to both the plaintext (also known as a crib) and its encrypted version (ciphertext). They are able to be utilized to reveal further hidden information, including secret keys and code books.” For the record 6000, the proposed IDNA-based SIPOA accomplished reduced attack ratings of 0.23, this is superior to GSO, WOA, BWO, DOA, TWOA, BOA, POA, SDSM [37], DNASF [22], FSA-SLnO and LSTM+MDNBO. Here, it is demonstrated that the proposed IDNA-based SIPOA must provide maximum security to the information during transmission described in the Supply Chain Management.

Fig. 13.

Attack analysis on proposed IDNA-based SIPOAversus conventional methods for secure data transmission in SCM in terms of (a) CCA (b) CPA (c) KCA and (d) KPA.

4.3. Analysis of Euclidean distance, arithmetic mean and harmonic mean of the proposed IDNA based SIPOA and the extant methods for secure data transmission for supply chain management

The arithmetic mean (AM) is the total number of observations divided by the sum of all observations. The harmonic mean is found by dividing the total number of observations, or entries in the series, by the inverse of each value in the series. As a result, the reciprocal of the arithmetic mean of the reciprocals is the harmonic mean. The evaluation of Euclidean distance, arithmetic and harmonic mean of the proposed IDNA-based SIPOA is contrasted with the GSO, WOA, POA, DOA, TWOA, BWO, BOA, FSA-SLnO and LSTM+MDNBO is represented in Fig. 14 and Fig. 15. In particular, the Euclidean distance with regards to the harmonic mean of the proposed IDNA-based SIPOA has accomplished minimal values over the GSO, WOA, BWO, BOA, TWOA, DOA, POA, FSA-SLnO and LSTM+MDNBO. Regarding the Fig. 15(a), the Pearson correlation with regards to arithmetic mean, the proposed IDNA-based SIPOA generated the lowest value of 0.05, whereas the GSO is 0.35, WOA is 0.34, BWO is 0.39, DOA is 0.23, TWOA is 0.42, BOA is 0.37, POA is 0.24, FSA-SLnO is 0.17 and LSTM+MDNBO is 0.15, respectively. Furthermore, the Spearman correlation with regards to arithmetic and harmonic mean of the proposed IDNA-based SIPOA has acquired less value than the traditional methods.

Fig. 14.

Analysis of Euclidean distance on proposed IDNA-based SIPOAversus conventional approaches for secure data transmission in SCM.

Fig. 15.

Pearson correlation and Spearman correlation analysis on proposed IDNA-based SIPOA and the traditional methods for secure data transmission in supply chain management in terms of (a) arithmetic mean and (b) harmonic mean.

4.4. Analysis of key sensitivity of the proposed IDNA-based SIPOA and the traditional approaches for secure data transmission in supply chain management

The key sensitivity evaluation on proposed IDNA-based SIPOA is contrasted to the GSO, WOA, BWO, DOA, TWOA, BOA, POA, SDSM [37], DNASF [22], FSA-SLnO and LSTM+MDNBO is described in Table 5. Furthermore, the evaluation involved changing the sensitivity % from 10 to 50. The model should yield the lowest correlation for supply chain management’s secure data transmission. Furthermore, the proposed IDNA-based SIPOA offered minimal correlation ratings over the other methodologies. Similarly, the proposed IDNA-based SIPOA obtained a reduced rate for the 10th sensitivity percentage is 0.862, whereas in the 50th sensitivity percentage, the suggested IDNA-based SIPOA achieved a diminished rate of 0.826. More particularly, the proposed IDNA-based SIPOA accomplished the lowest value for the 40th sensitivity percentage is 0.852, meanwhile the GSO = 0.92909, WOA = 0.89685, BWO = 0.966, DOA = 0.877, TWOA = 0.893, BOA = 0.893, POA = 0.901, SDSM [37] = 0.885, DNASF [22] = 0.893, FSA-SLnO = 0.865 and LSTM+MDNBO = 0.859, respectively. Therefore, the suggested IDNA-based SIPOA is said to ensure the preservation of protected data because it can be difficult to identify the original data without the right key.

Table 5
Key sensitivity analysis on proposed IDNA-based SIPOA versus traditional methods for secure data transmission in SCM

Sensitivity percentage 10 20 30 40 50

GSO 0.916796 0.93205 0.94325 0.92909 0.937893

WOA 0.908 0.90515 0.92675 0.89685 0.885066

BWO 0.945992 0.9726 0.949599 0.96548 0.985599

DOA 0.9249 0.9659 0.9155 0.877155 0.9716

TWOA 0.8876 0.8915 0.9369 0.8927 0.890187

BOA 0.906599 0.875452 0.935527 0.893266 0.899925

POA 0.9284 0.9188 0.9166 0.901 0.879946

SDSM 0.90625 0.9287 0.9262 0.884928 0.930893

DNASF 0.897099 0.883476 0.936213 0.892983 0.895056

FSA-SLnO 0.937239 0.928465 0.903797 0.865101 0.972215

LSTM+MDNBO 0.87372 0.879285 0.849038 0.858651 0.869972

IDNA based SIPOA 0.8618 0.8466 0.8348 0.8515 0.825486

Sensitivity percentage	10	20	30	40	50
GSO	0.916796	0.93205	0.94325	0.92909	0.937893
WOA	0.908	0.90515	0.92675	0.89685	0.885066
BWO	0.945992	0.9726	0.949599	0.96548	0.985599
DOA	0.9249	0.9659	0.9155	0.877155	0.9716
TWOA	0.8876	0.8915	0.9369	0.8927	0.890187
BOA	0.906599	0.875452	0.935527	0.893266	0.899925
POA	0.9284	0.9188	0.9166	0.901	0.879946
SDSM	0.90625	0.9287	0.9262	0.884928	0.930893
DNASF	0.897099	0.883476	0.936213	0.892983	0.895056
FSA-SLnO	0.937239	0.928465	0.903797	0.865101	0.972215
LSTM+MDNBO	0.87372	0.879285	0.849038	0.858651	0.869972
IDNA based SIPOA	0.8618	0.8466	0.8348	0.8515	0.825486

4.5. Analysis of confidence interval of the proposed IDNA-based SIPOA and the conventional methods for secure data transmission in SCM

The evaluation of the confidence interval of the proposed IDNA-based SIPOA over the GSO, WOA, TWOA, BWO, POA, BOA, DOA, FSA-SLnO and LSTM+MDNBO for secure data transmission is shown in Table 6. For supply chain management, secure data transmission should have a low confidence interval since it provides greater data protection. Based on the confidence interval 1, the conventional models such as GSO, WOA, BWO, DOA, TWOA, BOA, POA, FSA-SLnO and LSTM+MDNBO have gained greater confidence intervals of 5.76149, 6.184562, 6.557, 4.661, 5.170, 6.0790, 5.115 and 4.552, whereas the proposed IDNA based SIPOA obtained the lowest confidence interval of 4.2219. Moreover, based on confidence interval 2, the proposed IDNA-based SIPOA attained a minimal confidence interval over the other methodologies for transferring the information for the supply chain management network.

Table 6
Analysis of confidence interval on suggested IDNA-based SIPOA over the traditional methodologies for secure data transmission in supply chain management

Confidence Interval_1 Confidence Interval_2

GSO 5.76149 5.248645

WOA 6.184562 6.012556

BWO 6.556645 6.275073

DOA 4.660868 4.292739

TWOA 5.170004 4.925181

BOA 6.078953 5.145356

POA 5.821737 5.508361

FSA-SLnO 5.1146 5.005952

LSTM+MDNBO 4.551548 4.346596

IDNA based SIPOA 4.221948 3.960517

	Confidence Interval_1	Confidence Interval_2
GSO	5.76149	5.248645
WOA	6.184562	6.012556
BWO	6.556645	6.275073
DOA	4.660868	4.292739
TWOA	5.170004	4.925181
BOA	6.078953	5.145356
POA	5.821737	5.508361
FSA-SLnO	5.1146	5.005952
LSTM+MDNBO	4.551548	4.346596
IDNA based SIPOA	4.221948	3.960517

4.6. Analysis of encrypted and decrypted data of the proposed IDNA-based SIPOA and the existing models for secure data transmission in supply chain management

By keeping such sensitive information hidden from unauthorized users, the data sanitization approach guarantees the security of sensitive data stored in big databases. Data restoration is the process of retrieving or restoring data that has been cleaned up on the sender side. The evaluation of encrypted and decrypted data of the proposed IDNA-based SIPOA over the GSO, WOA, BWO, TWOA, DOA, POA, BOA, FSA-SLnO and LSTM+MDNBO with regard to Sanitization and Restoration effectiveness is tabulated in Table 7. Here, for secured data transmission, in the sanitization effectiveness, the model should attain a lower correlation value and it acquired the highest correlation rate in terms of restoration effectiveness. Based on the encrypted data with regards to sanitization effectiveness, the proposed IDNA-based SIPOA obtained the greatest correlation of 0.130034, although the GSO is 0.289656, WOA is 0.314536, BWO is 0.39115, DOA is 0.3067, TWOA is 0.2559, BOA is 0.2668, POA is 0.3495, FSA-SLnO is 0.1407 and LSTM+MDNBO is 0.1354, respectively. Moreover, the decrypted data with regards to restoration effectiveness, the techniques like GSO, WOA, BWO, DOA, TWOA, BOA, POA, FSA-SLnO and LSTM+MDNBO have obtained minimized correlation value, meanwhile, the proposed IDNA-based SIPOA scored the greatest correlation rate of 0.9919. This determined that the proposed IDNA-based SIPOA can precisely recover the original data without any loss.

Table 7
Analysis of encrypted and decrypted data of the proposed IDNA-based SIPOA and the traditional models for secure data transmission in supply chain management with respect to sanitization and restoration effectiveness

Santization Effectiveness Restoration Effectivenesss

GSO 0.289656 0.941633

WOA 0.314536 0.823488

BWO 0.391157 0.815237

DOA 0.306703 0.879125

TWOA 0.255961 0.885656

BOA 0.266826 0.833315

POA 0.349553 0.861448

FSA-SLnO 0.14071 0.98929

LSTM+MDNBO 0.135478 0.990001

IDNA based SIPOA 0.130034 0.991929

	Santization Effectiveness	Restoration Effectivenesss
GSO	0.289656	0.941633
WOA	0.314536	0.823488
BWO	0.391157	0.815237
DOA	0.306703	0.879125
TWOA	0.255961	0.885656
BOA	0.266826	0.833315
POA	0.349553	0.861448
FSA-SLnO	0.14071	0.98929
LSTM+MDNBO	0.135478	0.990001
IDNA based SIPOA	0.130034	0.991929

Table 8

Statistical analysis on IDNA-based SIPOA versus traditional approaches for secure data transmission in SCM under distinctive case scenarios

	Best	Worst	Mean	Median	STD
GSO	3.789015	6.442522	4.570926	4.298658	0.86136
WOA	3.914925	6.141379	4.829394	4.85416	0.681873
BWO	5.449747	7.333656	6.415859	6.657718	0.740978
DOA	3.810253	6.122265	4.476803	3.810253	0.968759
TWOA	4.040836	5.840236	5.047592	5.409662	0.657031
BOA	3.582706	5.583898	4.621544	4.397706	0.884136
POA	3.767776	6.762779	4.665049	4.787063	0.824675
IDNA based SIPOA	3.4166	5.100799	4.091233	3.679985	0.687975

4.7. Statistical assessment on IDNA based SIPOA and the standard models with regards to correlation for secure data transmission in supply chain management

A statistical study on the proposed IDNA-based SIPOA and the extant methodologies for secure data transmission in supply chain management is shown in Table 8. Statistical evaluation to examine the fluctuation of optimization algorithm in solving the given objective. Also, it is assessed via distinctive case (Mean, Best, Median, Worst and Standard Deviation). While analyzing the statistical report, the IDNA-based SIPOA obtained reduced correlation rates over the established methods. Therefore, the proposed IDNA-based SIPOA is a more suitable approach to secure the data during transmission in supply chain management. More particularly, the proposed IDNA-based SIPOA for the worst-case scenario is 5.101, whilst the GSO, WOA, BWO, DOA, TWOA, BOA and POA have maintained the correlation value of 6.442522, 6.141379, 7.334, 6.122, 5.840, 5.583 and 6.763, respectively. Furthermore, in the standard deviation case scenario, the proposed IDNA-based SIPOA generated a correlation rate of 0.688, though the GSO, WOA, BWO, DOA, TWOA, BOA and POA have acquired very low correlation values. In accordance with the statistical analysis report, the proposed IDNA-based SIPOA has achieved a reduced correlation value in all the case and it safeguards the data from the attackers and the proposed IDNA-based SIPOA is appropriate for secure transmission of data in supply chain management.

4.8. Convergence evaluation on proposed IDNA-based SIPOA and the conventional methods for secure data transmission in SCM

Figure 16 explains the convergence analysis of the proposed IDNA-based SIPOA over the GSO, WOA, BWO, DOA, TWOA, BOA and POA for secure transferring of data in SCM. Further, the examination was done by altering the iteration from 0 to 30. In accordance with this Fig. 16, the maximum correlation value was reached by the suggested IDNA-based SIPOA and other conventional approaches, the correlation rate decreased for nearly all algorithms as the number of iterations grew. Nonetheless, the proposed IDNA-based SIPOA achieved the minimal correlation value than the existing models and the proposed IDNA-based SIPOA is preferable for secure data transmission. During the 25th iteration, the proposed IDNA-based SIPOA generated a correlation value of 3.5, though the GSO, WOA, BWO, DOA, TWOA, BOA and POA recorded the greatest correlation rate. As a result, the plan outperformed other solutions in terms of data protection in supply chain management, as evidenced by the excellent results it obtained.

Fig. 16.

Convergence study on proposed IDNA-based SIPOA vs. established method for secure data transmission in supply chain management.

4.9. Performance assessment on proposed IDNA-based SIPOA and the existing models in terms of false generation ratio, data preservation ratio and modification degree for secure data transmission

The performance analysis on the proposed IDNA-based SIPOA is computed with the GSO, WOA, BWO, DOA, POA, BOA, TWOA, FSA-SLnO and LSTM+MDNBO in terms of False Generation Ratio, Data Preservation Ratio and Modification Degree shown in Fig. 17(a), 17(b) and 17(c). Analyzing the Fig. 17, the false generation ratio, data preservation ratio and modification degree must be minimal for secure transmission of the data in the supply chain management network model. The proposed IDNA-based SIPOA based on data security performs better than the GSO, WOA, BWO, BOA, DOA, POA, TWOA, FSA-SLnO and LSTM+MDNBO as well as the proposed IDNA-based SIPOA achieves minimal false generation ratio. More particularly, for the 25th iteration, the proposed IDNA-based SIPOA obtained the lowest false generation ratio of 0.234, meanwhile the GSO = 0.256, WOA = 0.264, POA = 0.248, LSTM+MDNBO = 0.251, DOA = 0.352 and FSA-SLnO = 0.324, respectively. Additionally, evaluating the outcomes of the data preservation ratio, in the 15th iteration the proposed IDNA-based SIPOA generated a data preservation ratio of 0.25, whereas it attained a reduced data preservation ratio of 0.2.

Finally, analyzing the modification degree and the findings shown in Fig. 17(c). In the first iteration, the suggested IDNA-based SIPOA and the traditional approaches produced the highest degree of modification; however, as the number of iterations rose, the degree of modification decreased for all of the strategies. Nonetheless, the proposed IDNA-based SIPOA scored the lowest modification degree in almost all the iterations. In particular, the proposed IDNA-based SIPOA obtained the modification degree for the 20th iteration is 3.2, even though the GSO is 4.5, WOA is 4.8, LSTM+MDNBO is 3.4, POA is 3.7, BWO is 3.6, BOA is 4.1 and POA is 4.9, respectively. Altogether, the proposed IDNA-based SIPOA supremacy is manifested. In conclusion, the proposed IDNA-based SIPOA based on data security generated superior results with improved performance as if contrasted with extant methodologies.

Fig. 17.

Analysis of performance on suggested IDNA-based SIPOA vs conventional models for secure transferring of data in SCM network (a) false generation ratio (b) data preservation ratio and (c) modification degree.

4.10. Discussion

In this work, a new Improved DNA-based Self Improved Pelican Optimization Algorithm (IDNA-based SIPOA model) is developed for data preservation and security in SCM. An improved DNA cryptosystem is done for the data preservation process. The plain text is encrypted with the help of IAES. The optimal key generation is employed by the suggested SIPOA algorithm. The attack analysis on suggested IDNA-based SIPOA is contrasted over the GSO, WOA, BWO, LSTM+MDNBO, DOA, FSA-SLnO, BOA, POA, and TWOA for secure transferring of data in SCM The attack analysis was evaluated with regards to CCA, KCA CPA, and KPA attacks by altering the cypher text from 10%, 20%, 30%, 40% and 50%, respectively. The proposed IDNA-based SIPOA protect the data from the attackers and it acquired the lowest attack ratings of 0.7 in the 4000 records, meanwhile, the traditional methods generated the highest ratings, notably, FSA-SLnO = 0.73, LSTM+MDNBO = 0.75, DNASF [22] = 0.77 and POA = 0.91, respectively. In the confidence interval 1, the conventional models like GSO, WOA, DOA, FSA-SLnO, BOA, BWO, LSTM+MDNBO, POA, and TWOA have gained greater confidence intervals of 5.76149, 6.184562, 6.557, 4.661, 5.170, 6.0790, 5.115 and 4.552, whereas the proposed IDNA based SIPOA obtained the lowest confidence interval of 4.2219. Thus the superiority of the proposed IDNA-based SIPOA has been demonstrated over existing methods for secured transmission of data in SCM.

4.11. Practical implications

The proposed scheme has efficient supply chain management leading to streamlined operations, reduced lead times, and minimized costs. Businesses can boost their competitiveness in the market and increase operational efficiency by streamlining their processes from sourcing to delivery. A factory may be visited by outside inspectors or auditors, and companies may do background checks on their employees. To prevent theft or manipulation, shipments could also be tracked, guarded, and examined both before and after transportation. Here, supply chain security mostly refers to reducing the risks associated with utilizing software created by another company and protecting company data that is accessed by a different company within your supply chain. It is imperative for organizations to ensure the security of the software they acquire or use. Supply chains can be shielded from cyber and physical dangers with the use of security management systems.

5. Conclusion

Due to the fierce rivalry in global markets and the rapid progress in information technology, product lifespans are getting shorter, transportation capacity is getting smaller, and demand is rising. In the majority of corporate circumstances, one of the most crucial areas of focus was becoming the supply chain system. An intriguing alternative for secure information sharing in supply chain networks is provided by blockchain technology. Public-private-key cryptography was more frequently adopted since it was somewhat crucial to ensuring security at every stage of the blockchain. This paper developed a novel Improved DNA-based Self Improved Pelican Optimization Algorithm (IDNA-based SIPOA model) for secured data transmission. An improved DNA cryptosystem with AES encryption and decryption was used for the data preservation process as well and the key generated in the AES process was optimally generated by the proposed SIPOA algorithm. The effectiveness of the proposed secured transfer of data in SCM with a blockchain-based model is proved by comparing it to more traditional methods with regards to security. The proposed IDNA based SIPOA accomplished the lowest value for the 40th sensitivity percentage is 0.852, meanwhile the GSO = 0.92909, WOA = 0.89685, BWO = 0.966, DOA = 0.877, TWOA = 0.893, BOA = 0.893, POA = 0.901, SDSM [37] = 0.885, DNASF [22] = 0.893, FSA-SLnO = 0.865 and LSTM+MDNBO = 0.859, respectively. The suggested method provides protection to SCM from cyber attacks. The security of the supply chain can function with more preservation and more efficient rather than the disruptions, while the attacks cannot be overall reduced supply chain management focuses on identifying and eliminating inefficiencies, waste, and unnecessary costs. However, it has a number of problems, from cyberattacks and access dependability to an uncertain return on investment. Multi-dimensional visualization tools will be necessary in the future to display the data status, modifies, and trends of various smart objects that are equipped with smart identification devices. Future plans call for a smart world with a smart services models, smart network, smart control principle, and smart logistics and manufacturing SCM.

References

M.H.

Abidi ,

Alkhalefah ,

Umer and

M.K.

Mohammed , Blockchain-based secure information sharing for supply chain management: Optimization assisted data sanitization process, International journal of intelligent systems 36(1) (2021), 260–290. doi:10.1002/int.22299.

N.N.

Ahamed and

Karthikeyan , A reinforcement learning integrated in heuristic search method for self-driving vehicle using blockchain in supply chain management, International Journal of Intelligent Networks 1 (2020), 92–101. doi:10.1016/j.ijin.2020.09.001.

Aljabhan and

M.A.

Obaidat , Privacy-preserving blockchain framework for supply chain management: Perceptive Craving Game Search Optimization (PCGSO), Sustainability 15(8) (2023), 6905. doi:10.3390/su15086905.

Bader ,

Pennekamp ,

Matzutt ,

Hedderich ,

Kowalski ,

Lücken and

Wehrle , Blockchain-based privacy preservation for supply chains supporting lightweight multi-hop information accountability, Information Processing & Management 58(3) (2021), 102529. doi:10.1016/j.ipm.2021.102529.

A.C.

Cagliano ,

Grimaldi ,

Rafele and

Campanale , An enhanced framework for blood supply chain risk management, Sustainable Futures 4 (2022), 100091. doi:10.1016/j.sftr.2022.100091.

Chung and

Jung , Knowledge-based block chain networks for health log data management mobile service, Personal and Ubiquitous Computing (2022), 1–9.

Dashti ,

Qureshi ,

Jahangeer and

Zafar , Security challenges over cloud environment from service provider prospective, Cloud computing and data science (2020), 12–20. doi:10.37256/ccds.112020318.

S.K.

Dwivedi ,

Amin and

Vollala , Blockchain based secured information sharing protocol in supply chain management system with key distribution mechanism, Journal of Information Security and Applications 54 (2020), 102554. doi:10.1016/j.jisa.2020.102554.

Hu ,

Xu ,

Liu and

M.K.

Lim , Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning, Journal of Business Research 156 (2023), 113480. doi:10.1016/j.jbusres.2022.113480.

10.

Khasim and

S.S.

Basha , An improved fast and secure CAMEL based authenticated key in smart health care system, Cloud Computing and Data Science. 11 (2022), 77–91. doi:10.1186/s13677-022-00346-x.

11.

Kousiouris ,

Tsarsitalidis ,

Psomakelis ,

Koloniaris ,

Bardaki ,

Tserpes and

Anagnostopoulos , A microservice-based framework for integrating IoT management platforms, semantic and AI services for supply chain management, ICT Express 5(2) (2019), 141–145. doi:10.1016/j.icte.2019.04.002.

12.

Kshetri , Blockchain and sustainable supply chain management in developing countries, International Journal of Information Management 60 (2021), 102376. doi:10.1016/j.ijinfomgt.2021.102376.

13.

P.S.

Lahane and

Sharma , Secured information sharing for supply chain management based on blockchain technology and optimal key generation process, Concurrency and Computation: Practice and Experience 35 (2022).

14.

Lale ,

M.K.

Gupta and

Sharma , Optimal key generation for privacy preservation using blockchain technology, Indian Journal of Computer Science and Engineering 12(5) (2021), 2231–3850.

15.

Lale and

Purohit , Secured information sharing in supply chain management, Literature Review. Test Engineering and Mangement (2020), 8859–8862.

16.

Lale and

Sharma , Secured information sharing using data sanitization and restoration, International Journals of Advanced Trends in Computer Science and Engineering 9(5) (2020).

17.

Lale and

Sharma , Block chain technology in supply chain management using key generation, Turkish Journal of Computer and Mathematics Education 12(1) (2021), 252–256.

18.

Lale and

Sharma , A novel approach for information sharing in supply chain management, Journal of Engineering and Technology 7 (2021), 17–22.

19.

Liu ,

Yan and

Wang , A hybrid blockchain model for trusted data of supply chain finance, Wireless personal communications (2021), 1–25.

20.

Mehta ,

Tanwar ,

Bodkhe ,

Shukla and

Kumar , Blockchain-based royalty contract transactions scheme for industry 4.0 supply-chain management, Information processing & management 58(4) (2021), 102586. doi:10.1016/j.ipm.2021.102586.

21.

Miyachi and

T.K.

Mackey , hOCBS: A privacy-preserving blockchain framework for healthcare data leveraging an on-chain and off-chain system design, Information processing & management 58(3) (2021), 102535. doi:10.1016/j.ipm.2021.102535.

22.

Namasudra , Fast and secure data accessing by using DNA computing for the cloud environment, IEEE Transactions on Services Computing 15(4) (2020), 2289–2300. doi:10.1109/TSC.2020.3046471.

23.

Namasudra ,

Devi ,

Choudhary ,

Patan and

Kallam , Security, privacy, trust, and anonymity, Advances of DNA computing in cryptography 1 (2018), 138–150. doi:10.1201/9781351011419-7.

24.

Omer , DNA Cryptography. Research Gate, 2015.

25.

Pavithran ,

Mathew ,

Namasudra and

Singh , Enhancing randomness of the ciphertext generated by DNA-based cryptosystem and finite state machine, Cluster Computing 26(2) (2023), 1035–1051. doi:10.1007/s10586-022-03653-9.

26.

Piao ,

Hao ,

Yan and

Jiang , Privacy preserving in blockchain-based government data sharing: A Service-On-Chain (SOC) approach, Information Processing & Management 58(5) (2021), 102651. doi:10.1016/j.ipm.2021.102651.

27.

Ramkumar ,

Kasat ,

N.M.

PK ,

Raghu and

Chhabra , Quality enhanced framework through integration of blockchain with supply chain management, Measurement: Sensors 24 (2022), 100462.

28.

A.H.

Sadeghi ,

E.A.

Bani ,

Fallahi and

Handfield , Grey wolf optimizer and whale optimization algorithm for stochastic inventory management of reusable products in a two-level supply chain, IEEE Access. (2023).

29.

Saini ,

Malik and

Sharma , Transformation of supply chain management to green supply chain management: Certain investigations for research and applications. Cleaner Materials (2023), 100172.

30.

Sharma ,

N.R.

Moparthi ,

Namasudra ,

Shanmuganathan and

C.H.

Hsu , Blockchain-based IoT architecture to secure healthcare system using identity-based encryption, Expert Systems 39(10) (2022), e12915. doi:10.1111/exsy.12915.

31.

Sun ,

Ozawa ,

Zhang and

Takahashi , Electricity supply chain management considering environmental evaluation: A multi-period optimization stochastic programming model, Cleaner and Responsible Consumption 7 (2022), 100086. doi:10.1016/j.clrc.2022.100086.

32.

Tuerxun ,

Xu ,

Haderbieke ,

Guo and

Cheng , A wind turbine fault classification model using broad learning system optimized by improved pelican optimization algorithm, Machines 10(5) (2022), 407. doi:10.3390/machines10050407.

33.

Wang ,

Luo ,

Zhang ,

Tian and

Li , Blockchain-enabled circular supply chain management: A system architecture for fast fashion, Computers in Industry 123 (2020), 103324. doi:10.1016/j.compind.2020.103324.

34.

Wang and

Wang , Supply chain financial service management system based on block chain IoT data sharing and edge computing, Alexandria Engineering Journal 61(1) (2022), 147–158. doi:10.1016/j.aej.2021.04.079.

35.

Xu ,

Zhang ,

Song ,

Liu ,

Li and

Zhou , A scheme for intelligent blockchain-based manufacturing industry supply chain management, Computing 103 (2021), 1771–1790. doi:10.1007/s00607-020-00880-z.

36.

Yang ,

Li ,

Liu ,

Li ,

Zhang and

Wang , Research on logistics supply chain of iron and steel enterprises based on block chain technology, Future Generation Computer Systems 101 (2019), 635–645. doi:10.1016/j.future.2019.07.008.

37.

Yu ,

Zhan and

Sohail , SDSM: Secure data sharing for multilevel partnerships in IoT based supply chain, Symmetry 14(12) (2022), 2656. doi:10.3390/sym14122656.

Supply chain management with secured data transmission via improved DNA cryptosystem

Abstract

Keywords

Nomenclature

1. Introduction

2. Literature review

3. Proposed supply chain management with secured data transmission via improved DNA cryptosystem

3.1.1. Suggested supply chain

3.3. SIPOA algorithm for optimal key generation [32]:

4.1. Simulation procedure

Table 4 System configuration Device Specifications Processor AMD Ryzen 5 3450U with Random Vega Mobile Gfx 2.10 GHz Installed RAM 16.0 GB (13.9 GB usable) System type 64-bit operating system, x64-based processor Windows Specifications Edition Windows 11 Pro Version 22H2

4.8. Convergence evaluation on proposed IDNA-based SIPOA and the conventional methods for secure data transmission in SCM

4.11. Practical implications

5. Conclusion

References

Table 4
System configuration

Device Specifications

Processor AMD Ryzen 5 3450U with Random Vega Mobile Gfx 2.10 GHz

Installed RAM 16.0 GB (13.9 GB usable)

System type 64-bit operating system, x64-based processor

Windows Specifications

Edition Windows 11 Pro

Version 22H2