Assessing the Cybersecurity of Smart Warehouse Systems Using IoT Technologies

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Data may be preliminary. 16 May 2025 V1 Latest version Share on Assessing the Cybersecurity of Smart Warehouse Systems Using IoT Technologies Author : Wang Wei-Ming 0009-0005-7354-7749 Authors Info & Affiliations https://doi.org/10.22541/au.174740595.53338287/v1 367 views 104 downloads Contents Abstract | Introduction | Research methodology | Research Results | System Setup | Conclusions Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In the digital age, the logistics industry is also moving towards a mobile environment. For example, cloud servers are software used by most companies. Cloud servers can significantly increase system support in the logistics industry. Product information no longer needs to be stored in traditional paper form, but rather in a core technology framework that synchronizes information. This also carries certain risks, as the risk of information security increases. When the network environment is complex or the Internet of Things is interconnected, there is a possibility that the firewall system will be invaded due to information security loopholes, and there is a threat of leakage of logistics company product information. This article discusses the problems brought about by mobile logistics of logistics enterprises from a practical perspective, with a focus on the information security issues of mobile cloud. When the platform’s defense mechanisms can effectively protect the system and cloud platform, evaluation is conducted for application scenarios (such as instant delivery, warehouse automation, etc.). This study designs a network security assessment framework for logistics company systems in the mobile cloud by strengthening the network security configuration of the cloud system and maintaining system security through encrypted communications. RESEARCH ARTICLE Assessing the Cybersecurity of Smart Warehouse Systems Using IoT Technologies Wang Wei-Ming 1 1 Department of Information Engineering, National Taitung University, Corresponding author: Wang WeiMing( [email protected] ) Keywords: L o g i s t i c s p l a t f o r m | W a r e h o u s e M a n a g e m e n t S y s t e m | C a r g o T r a c k i n g Sy s t e m T r a c k i n g | P e n e t r a t i o n T e s t i n g |N e t w o r k S e c u r i t y | M o b i l e c l o u d c o m p u t i n g | Introduction In today’s widespread digitalization, logistics organizations are rapidly moving towards mobility, such as cloud computing, which will particularly improve the efficiency of system support and information integration, meaning that the products of logistics organizations no longer rely on paper to retain documents or data. However, especially in the context of the widespread application of the Internet of Things, if there are security vulnerabilities in the system, it may lead to risks such as firewall hacking and corporate information leakage. [1]. With the increasing popularity of big data, cloud computing and the Internet of Things, information security issues in logistics organizations are increasing. This means that the threats that need to be faced include data theft, system interruptions, and supply chain data loss. To address these network security issues, logistics organizations should first establish an information security strategy centered on risk management, and strengthen information security defenses through encryption technology, abnormal monitoring systems, etc., to cope with highly interconnected digital logistics scenarios. From some studies, it can be found that, for example, edge computing and fog computing can be used to analyze potential security threats in mobile cloud environments and establish effective protection mechanisms [5]. At the same time, in-depth discussions have been conducted on the security and privacy issues of cloud computing in order to provide guidance for logistics organizations to select cloud service providers [6]. In terms of data storage, there is also a way to protect privacy, such as attribute-based encryption schemes used to solve data storage problems in mobile clouds [7].A proof-of-authority mechanism has been proposed for mobile devices with restricted resources in order to prevent the possibility of data loss, tampering, and leakage. [8]. In terms of technical protection, many studies have proposed a variety of effective methods to improve data security. For example, deep learning can effectively detect suspected cyber attacks in mobile cloud environments with an accuracy rate of up to 97.11%[2]. Deep learning models can detect whether IoT devices are behaving abnormally and can be used to identify potential security threats in smart logistics systems[3]. Some studies have proposed an intrusion detection and prevention system that uses machine learning technology to dynamically analyze device resources and increase network traffic, which is an effective way to improve security in mobile cloud environments[4]. In addition, the related threats to network security have been studied in relation to the problem of cloud storage in smartphones. In particular, a network security data storage mechanism has been proposed to effectively prevent data leakage and unauthorized access [9]. In the data security framework of mobile cloud computing, multi- level network security measures have been proposed to enhance data protection capabilities [10]. Regarding the data storage problem based on blockchain, some studies have shown that if blockchain technology can effectively prevent data leakage and unauthorized access, it can ensure the data security of logistics organizations [11]. Regarding the incremental encryption method, some studies have shown that this method can achieve effective data encryption and decryption operations in mobile clouds, with the purpose of improving data security and efficient processing[12]. In addition, through the research on lightweight storage outsourcing solutions, it is proposed to realize data storage and access control in mobile cloud and network security[13]. In terms of data service mechanism, it is also proposed to ensure the secure storage and access control of data in the mobile cloud environment in order to ensure that the data of logistics organizations is safe in the cloud environment[14]. Regarding the complete access control framework, some studies have proposed implementing fine-grained access control through the Internet in mobile clouds to prevent unauthorized access and data leakage [15]. In addition, regarding the security issues of edge computing in the transportation sector, research shows that the application of edge computing technology can reduce latency and improve data security[16], which is particularly important for smart logistics systems that need to process large amounts of data in real time. In order to protect against Distributed Denial of Service (DDoS) attacks, protection solutions are mainly defense recommendations provided by logistics organizations [17]. Edge computing technology can significantly improve data protection capabilities in the logistics supply chain[18]. Some studies have investigated the application of IoT and 5G technologies in the network security of logistics organizations, proposing that network security protection measures can be improved by enhancing the use of IoT devices in the 5G environment[19]. In cold chain logistics systems, data integrity can be ensured through encryption and identity authentication mechanisms[20]. For IoT technology, a monitoring system is essential, especially transportation monitoring. Research has pointed out that IoT technology can effectively protect the security and privacy of data during transportation[21]. It also proposes that cloud computing data can be used to protect and improve the transparency and traceability of the logistics process[22]. In smart cities, cloud-based systems are the foundation for the IoT trust management system. They can also make IoT devices trustworthy and enhance the security of the overall system from an evaluation perspective [23]. Research has proposed that the strengthening of network security technology in the Internet of Things requires a network security framework between devices and cloud services[24]. This application is applied in intelligent transportation. In addition, research has also proposed data security protection measures to improve data processing and security in transportation and logistics systems[25]. The data integrity of blockchain technology is to prevent data from being tampered with and improperly traded during the logistics process, and to protect the authenticity and reliability of logistics data[26]. This is mainly done through encrypted query processing in IoT devices. This is done to encrypt and protect privacy data[27], further improving the security of the IoT. However, the application of blockchain technology through the IoT has proposed an intelligent logistics system architecture to ensure data integrity and improve operational efficiency[28]. It also prevents data tampering and unauthorized access[29]. The identity authentication and privacy protection issues in IoT logistics are analyzed through identity mechanism[30]. Such protective measures can improve data security[31], so that the application of IoT technology in intelligent logistics can also be comprehensively analyzed, and security solutions for IoT application scenarios are proposed[32]. However, AI technology issues in recent years have also become an important means to improve the security and reliability of logistics systems. Through AI technology, the intelligent logistics framework can be used to improve the security of the system[33]. Encryption technology is used to protect data in IoT logistics, especially in a logistics environment with multiple parties involved. Confidentiality is relatively important[34]. Regarding network security issues in cloud flows, some scholars have proposed that AI technology can enhance network security solutions and help organizations strengthen their protection capabilities[35]. In the face of complex network attacks and data theft risks, it can provide comprehensive protection for data security in the IoT system[36]. However, in terms of network security protection of the IoT and cloud logistics systems, some studies have proposed solutions based on zero-trust architecture. This approach aims to effectively respond to increasingly complex network attacks to reduce the risk of harm and provide comprehensive data security protection, especially in the IoT environment. Strict authentication and authorization are required for data, thereby enhancing the overall security of the system[37]. When it comes to mobile cloud data, network security is a huge challenge. As mentioned above, the application of zero-trust architecture is to ensure that logistics organizations can provide effective measures in the face of rapid changes in digitalization. By strengthening identity authentication and data encryption, the architecture responds to the growing network security threats[38]. This can not only effectively prevent network attacks, but also ensure the data integrity and privacy protection of logistics organizations. From the above, the problems faced by logistics organizations appear to be more complex. From data storage to each link in the transmission process, organizations should rely on advanced technologies, including blockchain, encryption technology, deep learning and other means to enhance network security protection capabilities and ensure that they can improve data processing and analysis capabilities. This is an effective method for logistics organizations to face network security risks On the basis of ensuring data security, it lays a solid foundation for the digital transformation of the global logistics system. | Research methodology Overview This study is based on cybersecurity issues, with a particular focus on threats posed by automation and the Internet of Things. Taking logistics organizations as an example, today’s logistics organizations are gradually integrating cloud computing and Internet of Things (IOT) technologies. The main reason is that exploiting vulnerabilities in the firewall’s basic system has become the key to network attacks. Based on this research, we worked with logistics organizations to replace traditional warehouse inventory capabilities with digital methods as the research basis and detect the possibility of cyber threats based on experimental methods. The study emphasizes that the current digital environment has indeed changed logistics organizations, but the corresponding cybersecurity threats will also increase. Special emphasis is placed on system risk assessment as a necessary condition for taking security measures to address it. Construction of Smart Logistics System Environment As described in the above overview, the purpose of this study on building a smart logistics system is to improve the operational efficiency of logistics organizations, reduce labor costs, and improve service quality. We further understand the network security challenges that the system will face. Based on the existing infrastructure of logistics organizations, we designed a simulated logistics system scenario and analyzed the potential network security risks through test results. The core of the smart logistics system includes: 1. Warehouse Management System : The main smart logistics system plays the most in portant role in the ware house management system. It is responsible for processing logistics, inventory and monitoring data. Through system integration, logistics operations can achieve fully automated efficiency. 2. Sensors and equipment : Temperature and humidity sensors, barcode scanners, and other equipment deployed in the warehouse transmit the information to the central processing system. 3. Terminal equipment: Logistics managers and operators use smartphones and tablet computers to perform on-site operations and input data, thus achieving real-time updates of the logistics system. 4. Cloud Server System: The data based on the terminal device comes from the cloud system, which ensures the management of the data. 5. IoT System Protocol: used to ensure data transmission between devices and cloud servers to minimize communication interruptions. Through the close collaboration of the above five environments, end-to-end automation and digital management from goods warehousing, transportation, storage to final delivery can be achieved. However, this does effectively integrate operational operations, but in evaluating the network security issues of the smart logistics system, according to the following testing and simulation procedures: 1. Penetration testing :Use vulnerability scanning mechanisms such as Nessus tools to perform regular vulnerability scans as shown in Figure 1. Figure 1 shows that if no other colors appear, it means that the IP is executing. If other colors appear, it means that the IP may be associated with potential network security threats. Figure 2 shows and this is normally done to simulate various types of cyber attacks to identify and analyze system vulnerabilities. 2. Risk assessment: Monitor abnormal behavior by analyzing data streams from various devices. Identify and evaluate potential security vulnerabilities or attack patterns to support the development of future defense strategies. 3. Data encryption: Data needs to be encrypted and firewall configurations must be strengthened through access control policies to prevent unauthorized access and data leakage. Fig.1. Distribution of vulnerability scan sample categories.( Server shielding) Figure 2. Vulnerability scanning sample category distribution, yellow indicates unau theorized websites(Server Shielding) Assessment of Smart Warehouse Management Systems The smart warehouse management system mainly executes the warehouse management system module through a digital model. There is a lot of room for improvement in the traditional warehouse management for the operation and management of logistics organizations. For automatic decision-making and real-time data analysis, real-time data can be executed through IoT devices. By predicting replenishment needs, the layout of warehouse shelves can be optimized and even the parameters of the warehouse system can be adjusted. For example, basic temperature and humidity control is used to adapt to different commodities. The smart warehouse management system can be introduced through intelligent equipment, such as automatic guidance devices, robotic arms and other intelligent equipment, and combined with the Internet of Things to achieve high automation, reduce labor costs and achieve unmanned warehouse operations, thereby improving the efficiency of logistics organizations, as shown in Figure 3.Although the smart warehouse management system has high efficiency, the complexity of the system will bring potential or more harmful network security risks.Frequent data exchange between the cloud platform and the system design is likely to lead to any network security vulnerabilities, which may become attack vectors. In the simulation test, the common error messages of the mobile cloud interface in mobile logistics were only detected by the vulnerability scan of the Internet of Things, which showed that insufficient processing was caused by encryption protection errors, which may lead to the exposure of sensitive information or allow unauthorized remote control of devices. If the artificial intelligence model in the system is not adequately protected or verified, it will damage the accuracy of warehouse operations and decision- making.For risk management, we implement a series of multi-layered cyber security measures, including: 1. Use encryption protocols to ensure the security of data transmission between devices and the cloud. 2. Use anomaly detection systems to monitor internal behavior and issue warnings for unusual activities. 3. By enforcing strict identity authentication. 4. Prevent malicious data from damaging machine learning models. The four projects aim to help logistics organizations automate their operations, but as mentioned above, cyber security must develop in tandem with the core driving force of this digital transformation. Cyber security and autonomous decision-making should be achieved to ensure reliable performance in an increasingly complex and dynamic supply chain environment. Figure 3. Architecture diagram of smart warehouse management system System operation and evaluation The system operation mainly designs a hierarchical smart warehouse system. For mobile logistics, the role of the smart warehouse system is absolutely necessary. The system can be used as a key role in distributed interface operation through the terminal. It is mainly aimed at the warehouse management system through the cloud orhybrid basis. It can be used to coordinate the logistics process, including goods inbound, outbound, warehouse tracking, management and scheduling. The terminal of the warehouse system can be used as a system interface through a smart phone or tablet computer. The above-mentioned devices allow users to use RESTful API, MQTT or WebSocket network transmission protocols, etc., to interact with the warehouse management system to ensure data exchange as shown in Figure 4. From Figure 4, it can be seen that the terminal has a variety of key functions, including allowing warehouse operators to execute barcodes and scans, access real-time order status, confirm whether the goods are delivered, and send control instructions to automatic guided vehicles, smart conveyor belts and other smart devices in the warehouse system, and through sensors or camera modules, it is used to identify whether the goods are damaged or monitor the shelf status.Based on real-time exception reporting, the overall flexibility and responsiveness of warehouse operations are enhanced. From the above, we can understand that the terminal needs to be routed through network security, especially wireless base facilities such as Wi-Fi, 5G or LPWAN in the process of collecting data. Then it is processed by the middleware layer or edge computing gateway, and finally transmitted to the cloud system. Through this type of architecture, latency can be reduced to ensure uninterrupted operation under fluctuating network conditions. A key to the terminal architecture lies in the layered network security framework, such as identity verification and other related issues. Mobile devices use TLS 1.3 or equivalent versions for encryption for all communications. When anomaly detection occurs, the system will monitor whether the threat behavior of the device has been tampered with and other risks.In summary, the combination of terminals and smart warehouses is effective in achieving decentralized operations because the benefits it can bring are relatively high. It can not only change the existing ecology of the logistics industry, but also lay the foundation for a flexible warehouse environment. Figure 4. Warehouse management system and terminal integration system architecture Internet Security The structural diagram designed from the above content is used as the core element of mobile logistics for the effectiveness of operational management, including Io T management and management platform, etc. Through the integrated process, the processing, tracking, scheduling and other distribution functions of goods entering and leaving can be realized. For terminals such as smartphones and tablets, the connection function of the IoT can be used. The order status can be known in real time by using barcode scanning. It is particularly emphasized that this study adopts an abnormal reporting mechanism. Through the transmission of data including Wi-Fi, 5G or LPWAN, all data must be pre-processed by multiple edge gateway nodes or localized processing layers before being uploaded to cloud management and analysis. This can effectively reduce communication delays and ensure system stability in unstable network environments, as shown in Figure 5. From Figure 5, we can understand the network security strategy oflogistics organizations.The network security strategies of logistics organizations are as follows: 1. Network security defense strategy: The system emphasizes that it enhances network security through a layered network security architecture, protects data privacy and access control during transmission, and prevents unauthorized interception or access. 2. Penetration Testing: Identify potential cyber security risks and improve system resilience by regularly conducting penetration tests and simulated cyber attack drills, remediating through denial of service, and optimizing security policies to protect against complex threats. 3. Risk assessment: The system detects abnormal behaviors and predicts potential threat events, triggering early response m e a s u r e s t o s u p p o r t s e c u r i t y o p e r a t i o n s management. Figure 5. The architecture diagram shows the importance of network security to warehouse systems Flowchart Through the above, we can understand the structure of the six modules of the smart warehouse system in mobile logistics, the technical chain for the analysis of terminals and the cloud, including performance, network security, and real-time control performance of the Internet of Things. If the user (such as the operator) uses a smartphone or tablet to perform barcode/QR code scanning on the terminal, order inquiry, goods receipt confirmation, and key tasks such as abnormal image upload that may occur on site, the terminal can be equipped with high- precision sensors and transmitted to the computing layer through standardized communication protocols (such as API, MQTT or WebSocket) for preliminary processing. These are all the descriptions of the above-mentioned research method experiments. However, potential risks include that once the detection system detects an abnormal situation (such as damage to the goods, abnormal temperature/humidity or incorrect storage location), the system will immediately activate the alarm mechanism.From the above table, we can understand that the coordinated operation of IoT sensing devices mainly handles logistics operations (such as incoming and outgoing goods), and the relevant automated equipment (such as automatic guided vehicles (AGV)) will send control commands. The management system will dispatch based on the sent instructions and optimize the resource allocation of the warehouse management system to ensure the continuous and efficient workflow.The cloud platform is used to store data and updates of the warehouse system, which can be analyzed through artificial intelligence and machine learning algorithms to identify potential attack vectors or cybersecurity threats, generate predictive alerts, and initiate preventive measures before damage occurs.From this study, we can understand that there may be network security threats in the network security architecture of the application layer, communication layer and transport layer of the warehousemanagement system. Although the system will enforce access control such as data encryption or identity verification, network threats and other issues need to be simulated through conventionalpenetration testing (such as SQL injection, distributed denial of service (DDoS)) to ensure the network security of the warehouse system. Through encryption control, it is used to ensure the integrity and confidentiality of data throughout the life cycle. The purpose of doing so is to provide the warehouse management of the logistics organization with system intelligence and the performance of the technical foundation. The relevant flow chart is shown in Figure 6. Figure 6. Flowchart | Research Results The experimental design of this study uses a practical test experiment. Through real-time interaction and data integration operations, it is used in warehouse management systems combined with intelligent dynamic devices. The simulated deployment of the system can be verified through network security. Through a modular model, users can improve operational efficiency, system accuracy and other decision-making capabilities, while detecting the risk level of network security, as shown in the following table. The specific research experimental module analysis and evaluation are as follows: System Module Evaluation In relation to the above description, the warehouse management system needs to include a credential-based secure login interface, which is suitable for verifying whether the logged-in user has a network security login account. The use of HTTPS secure transmission mainly reduces the risk of credentials being stolen and attacked. Based on the identity verification of users including warehouse staff and administrators, suspicious network attacks or privilege abuse issues can be eliminated. As shown in Figure 7, users can use smart phones or tablets to operate the smart warehouse system. The interface will present five modules including warehouse operations, transportation operations, inventory operations, exception handling, and report inquiries. Figure 7 shows that the warehouse management system is classified in a modular way, and the click rate of operators clicking on these five modules when using the warehouse management system can be detected. The purpose of this is to detect the legitimacy of the operator in executing the warehouse management system. Figure 7. Module operation frequency analysis System Module Evaluation From the above evaluation, we calculated the average value of the system modules from January to April. The average value mainly emphasizes the click rate of the warehouse management system. It can be found from Figure 8 that the dark blue part is the warehouse management system, which means that most operators will click on the warehouse management system. The system modules are analyzed based on the warehouse management system from January to April. Figure 8. Module Analysis (January and April combined) Network Security Analysis Through the module description of the warehouse management system above, the next step is to evaluate the network security as described above. Through the certificate authentication method, the HTTPS secure communication protocol is encrypted and authenticated to reduce the risk of network attacks. Then, according to the different identities of the warehouse management system administrators, the system uses role-based permission control to limit the access scope of the module.The use of research results related to network security. Analyze network security for the following purposes: Event detection : The purpose of event detection is mainly to detect abnormal logins, which means that the system will record the IP login time of all users. This is done to detect who logs into the warehouse management system during non-working hours. If it happens, an immediate notification mechanism will be established. From Figure 9, we can see that the detection rate of abnormal behavior of the system each month was only 62% in the first month and reached 80% in April. This means that the event detection of the warehouse management system through vulnerability scanning is increasing every month, which means that the logistics organization attaches great importance to the event detection part of the organization. Network Security Analysis: Regarding the network security assessment of the warehouse system, the above discussion has explored the method of using a terminal in combination with a warehouse management system. However, from the statistical results of the experiment, it can be found that, for example, the warehouse management system may cause network problems after logging into the system through the authentication credentials. The more common problem is the password strength problem. For example, setting a ”strong” password means that the user has a higher sense of account security. If a password with a ”weak” or ”other” level is set, it may affect the account security. From Figure 10, it can be found that we have detected that the general warehouse system administrators tend to set passwords with a ”medium” strength, which may cause account credential security problems in the warehouse management system, such as being very likely to be attacked by phishing or hacked. 𝑣 𝑣 Figure 9. Network security incident detection Figure 10. Network security detection intensity distribution System Risk Assessment Based on the statistical analysis of the warehouse management system using password strength as the network security of the warehouse management system, we can find that 50% use medium- strength passwords and 30% use high-strength passwords. This distribution shows that most users show basic network security awareness. It is recommended to use password complexity standards to help strengthen the first layer of access control. However, 20% use weak or extremely weak passwords, which means that there may be serious vulnerabilities in the system’s network security, which may pose a major risk to the system’s overall information security. Password complexity requirements should be enforced, such as a minimum length of 12 characters, including uppercase and lowercase letters, numbers or special symbols. Through this type of network security protection measures, the validity of the credentials is increased, the network security protection architecture is added, and the possibility of system intrusion is reduced. In addition, this study performs risk assessment based on the results of statistical data. The purpose of doing so is to identify the system of the warehouse management system and analyze the information security risks. Risk assessment is used to ensure the reliability of the warehouse management system. Table 1 shows the risk management assessment of the system. Table 1. Risk Assessment of Warehouse Management | System Setup Analysis of Network Security Incident Detection Performance From the above research results, we know that in the experiment, the detection results of network security incidents from January to April 2025 have steadily increased from 62% to 89%, indicating that the performance of the overall system abnormality identification and early warning system has been significantly improved. The enhancement of detection can enable the warehouse management system to achieve network security optimization, but there will also be potential risks. For example, although the performance of the detection system has been improved, it does not mean that comprehensive protection can be provided. If the warehouse management system does not have an effective event response protocol, this may trigger a large number of detections, causing the system to be prone to erroneous allocation, resulting in the possibility of information leakage. Risks and threats of vulnerability scanning As mentioned above, from January to April 2025, vulnerability scanning increased from 60% to 90%, which shows that the risk list coverage of the warehouse management system has been enhanced, which means that the system administrator’s network security supervision has achieved some results. The potential risks may be related to the blind spots of the cloud interface. The response priorities made through risk assessment use continuous scanning and automatic patching programs. Regarding the strength of passwords, it is learned from the experiment that the distribution of password strength is 30% using strong passwords, 50% using passwords of equal strength, and 20% using weak or very weak passwords. This reflects the management of secure credentials, so risk assessments of network security should be evaluated regularly. WMS Digitalization and Network Security Analysis As mentioned above, the purpose of evaluating the risk hazards of the warehouse management system through risk management is to understand the level of potential risk hazards of the system’s network security, and then classify and differentiate the risk levels, as shown in Table 2. For Table 2, we designed the risk assessment measurement basis table based on Table 1 It can be seen that through risk assessment, we can understand the network security analysis of the warehouse management system, Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted. Table 2. Risk assessment measurement data table. | Conclusions This study is aimed at the study of digital logistics organizations combined with warehouse management systems. The warehouse management system is evaluated through the effectiveness of network security. The focus is on network security event detection and vulnerability scanning analysis. The protection effectiveness of network security is understood through experimental analysis results. First, we analyze the threat of abnormal detection results detected by the network security event detection system and find that they may pose a certain degree of threat to the warehouse system. From the experimental data, we find that the vulnerability scan rate from January to April increased from 62% to 89%, which shows the effectiveness of the vulnerability scan. From the experimental perspective, it can be observed that the warehouse management system can be effectively protected through high-intensity network security defense measures. From the analysis, it can be learned that more than 80% of the password settings meet medium-to-high security standards, but 20% use weak or insecure credentials, indicating that users’ recognition of the potential risks of the warehouse management system is insufficient. If the recognition of network security is insufficient, it may cause major vulnerabilities in the system’s security.Overall, the research results have achieved initial results for the cybersecurity architecture of warehouse management systems, and have improved the integration of authentication and cybersecurity awareness to a certain extent. Cybersecurity training should be conducted for employees of logistics organizations, which will help achieve effectiveness in the development of logistics organizations. Data availability statement Data sharing is not applicable to this article, and no datasets were generated or analyzed by this current research. References 1. Enache, G. I. (2023). 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