Insider threats are becoming one of the most difficult security issues for various fields-such as financial, medical, public service, and manufacturing sectors-due to their virulence to the system architecture that is dependent on computers and networks. In fact, the insider threat is a serious problem across all respects of any real-world system where people trusted with access to critical and sensitive information can abuse the access authentication to damage and compromise the information; or collaborate with others to cause failures, losses and serious harms to the system [1] .
The security of the manufacturing industry is becoming increasingly threatened by insiders according to several reports [2, 3, 4, 5, 6, 7, 8, 9] . The CERT National Insider Threat Center reports that the insider attacks were more significant than outsider attacks in the manufacturing industry [2] . According to the 2019 RSA conference survey [3] , 40% of insider threats in the industry were detected after damages had occurred or not able to be detected. Also, the manufacturing industry was noted for the most vulnerable industry against insider threats.
Insider threats are becoming more serious in the manufacturing industry as an automated manufacturing system is being deployed and developed further. Cyber-Manufacturing System (CMS)-where digital assets are being continuously exchanged among physical assets (machines, sensors, actuators on a shop floor) through various computer networks and the Internet-promises dramatic improvements in flexibility, productivity, and profitability [10] . However, such a system also ushers in additional security issues because of the enlarged attack surface by the increased level of connectivity among physical assets with sheer volume and pervasiveness of exchanged data. Particularly, when Industrial Control Systems (ICS) for manufacturing process automation is employed, security vulnerabilities by insiders increase. ICS-such as Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control System (DCS), and Process Control Systems (PCS)-have been employed in various industries to monitor and control data transmission in facilities. In general, ICS uses the Programmable Logic Controller (PLC) as a control device, which is operating physical machines based on Input/Output (I/O) data of a system. PLCs are typically located
Insider threats are becoming one of the most difficult security issues for various fields-such as financial, medical, public service, and manufacturing sectors-due to their virulence to the system architecture that is dependent on computers and networks. In fact, the insider threat is a serious problem across all respects of any real-world system where people trusted with access to critical and sensitive information can abuse the access authentication to damage and compromise the information; or collaborate with others to cause failures, losses and serious harms to the system [1] .
The security of the manufacturing industry is becoming increasingly threatened by insiders according to several reports [2, 3, 4, 5, 6, 7, 8, 9] . The CERT National Insider Threat Center reports that the insider attacks were more significant than outsider attacks in the manufacturing industry [2] . According to the 2019 RSA conference survey [3] , 40% of insider threats in the industry were detected after damages had occurred or not able to be detected. Also, the manufacturing industry was noted for the most vulnerable industry against insider threats.
Insider threats are becoming more serious in the manufacturing industry as an automated manufacturing system is being deployed and developed further. Cyber-Manufacturing System (CMS)-where digital assets are being continuously exchanged among physical assets (machines, sensors, actuators on a shop floor) through various computer networks and the Internet-promises dramatic improvements in flexibility, productivity, and profitability [10] . However, such a system also ushers in additional security issues because of the enlarged attack surface by the increased level of connectivity among physical assets with sheer volume and pervasiveness of exchanged data. Particularly, when Industrial Control Systems (ICS) for manufacturing process automation is employed, security vulnerabilities by insiders increase. ICS-such as Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control System (DCS), and Process Control Systems (PCS)-have been employed in various industries to monitor and control data transmission in facilities. In general, ICS uses the Programmable Logic Controller (PLC) as a control device, which is operating physical machines based on Input/Output (I/O) data of a system. PLCs are typically located
Insider threats are becoming one of the most difficult security issues for various fields-such as financial, medical, public service, and manufacturing sectors-due to their virulence to the system architecture that is dependent on computers and networks. In fact, the insider threat is a serious problem across all respects of any real-world system where people trusted with access to critical and sensitive information can abuse the access authentication to damage and compromise the information; or collaborate with others to cause failures, losses and serious harms to the system [1] .
The security of the manufacturing industry is becoming increasingly threatened by insiders according to several reports [2, 3, 4, 5, 6, 7, 8, 9] . The CERT National Insider Threat Center reports that the insider attacks were more significant than outsider attacks in the manufacturing industry [2] . According to the 2019 RSA conference survey [3] , 40% of insider threats in the industry were detected after damages had occurred or not able to be detected. Also, the manufacturing industry was noted for the most vulnerable industry against insider threats.
Insider threats are becoming more serious in the manufacturing industry as an automated manufacturing system is being deployed and developed further. Cyber-Manufacturing System (CMS)-where digital assets are being continuously exchanged among physical assets (machines, sensors, actuators on a shop floor) through various computer networks and the Internet-promises dramatic improvements in flexibility, productivity, and profitability [10] . However, such a system also ushers in additional security issues because of the enlarged attack surface by the increased level of connectivity among physical assets with sheer volume and pervasiveness of exchanged data. Particularly, when Industrial Control Systems (ICS) for manufacturing process automation is employed, security vulnerabilities by insiders increase. ICS-such as Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control System (DCS), and Process Control Systems (PCS)-have been employed in various industries to monitor and control data transmission in facilities. In general, ICS uses the Programmable Logic Controller (PLC) as a control device, which is operating physical machines based on Input/Output (I/O) data of a system. PLCs are typically located 48th SME North American Manufacturing Research Conference, NAMRC 48 (Cancelled due to on the shop floor to form a network environment. Furthermore, in CMS, they are integrated into the system via the Internet and computer networks. Thus, insiders have physical and digital access to the PLCs [11, 12] . As a result, insiders can intentionally or unintentionally pose serious threats to the entire manufacturing process security through PLC.
To reduce the severity of insider threats, the blockchain technology was proposed as a solution [4, 5] . The blockchain is a way of storing data in a distributed system in which the system components are connected to each other and form a peer-to-peer network. Data are permanently stored in the blockchain by stacking new data on old data, rather than updating old data by new data. For this reason, it is also called a growing list of records [13] . The blockchain has been applied to diverse industries such as financial, medical, insurance, and power grid to maintain data integrity in a verifiable and permanent way. However, attempts to utilize the blockchain to secure PLC have been few. Although numerous researches have explored the security of PLC communication, most of them have focused on intruders from outside of the system.
As the first step to manage insider threats for PLC, this paper presents a system architecture of an applied blockchain for PLC communication in CMS, where the input data for PLC is stored and retrieved via the blockchain. To validate the system, two testbeds were developed under different network environments: i) the PLC testbed ( Fig. 1) and ii) blockchain testbed (Fig. 4) . Both testbeds are based on a UDP protocol-based PLC communication environment. A single-board computer as a PLC device and open-source PLC with IEC 61131-3 standard were used to simulate a small-scaled PLC environment and connected to a personal computer representing a main control server by using UDP protocol. For the blockchain testbed, a private blockchain network was developed by using an Ethereum client (GETH v1.9.6) to enable communication among the main control server and the PLC device. To show the effectiveness of the use of the blockchain, a comparative analysis of three attack scenarios has been conducted: (i) direct modification, (ii) UDP denial of service attack, and (iii) man in the middle attack.
The contributions of the research presented in this paper are: (i) comprehensive definitions for insiders and its threats in the manufacturing industry have been established to clarify the objective of security enhancement in CMS, (ii) a conceptual system of an applied blockchain for PLC communication has been designed to improve the security against insiders, and (iii) two testbeds have been developed and used for three attack scenarios to validate the proposed system.
The rest of the paper is organized as follows. In Section 2, the background for insider threats to Cyber-Manufacturing systems and blockchain applications is provided. PLC and the blockchain technologies are discussed in Sections 3 and 4, respectively. Section 5 demonstrates the effectiveness of the proposed system by providing a comparative analysis of the three attack scenarios. Finally, the conclusion and future study are presented in Section 6.
Insider threats are considered as one of the most challenging cyber security issues because they are hardly detected or prevented by commonly employed security mechanisms. Insiders are individuals who have access and knowledge of a system and are also trusted within the security perimeter. Accordingly, insider threats refer to actions that these insiders intentionally or unintentionally misuse or abuse access/knowledge to violate the security policies of the system [33] .
The definition of insider threats is differed depending on insiders' intentions. For example, some definitions-such as "harmful acts that trusted insiders might carry out" and "the intentional misuse of computer systems by users who are authorized to access those systems and networks"-limit the scope of insider threats to intentional insiders. But other definitions-such as "threats originating from people that have been given access rights to an IS and misuse their privileges, thus violating the IS security policy of the organization" and "an individual with privileges who misuse them or whose access results in misuse"-include both intentional and unintentional insiders [34, 35, 36, 37] .
However, unintentional insider threats cannot be ignored from manufacturing system security because these can damage the system as much as intentional insider attacks. Also, it is plausible that insiders become a bridge between outside attackers thus compromising confidential system domain [6] . Furthermore, anyone who is able to access the systems-such as suppliers, outsourcing human resources, or even third-party vendors-could turn into a malicious insider. According to the 2016 California Data Breach Report, Target, the eighth-largest retailer in the United States, was hit with a credit card data breach by a third-party vendor, which results in 7.5 million customer's credit card information leakage in 2013 [7] . The attacker obtained access to Target's customer service database by using credentials stolen from the third-party vendor.
In this paper, an insider is defined as "anyone who is within the security parameter of a system, but can intentionally or unintentionally compromise the system security."
Until now, security concerns on insider threats have been on the cyber domains. However, since insiders in CMS can access both cyber and physical domains, it is necessary to enhance the security for both domains. This further makes it more difficult to develop an effective security system for the CMS. Besides, some features of insiders in CMS make them more arduous to be handled. First, it is very difficult to predict whether certain insiders pose threats in advance, due to a plethora of human motivations with limited understanding of human psychology. Second, it is hard to track attacks from insiders because they can easily hide their digital footprints by manipulating or incapacitating security mechanisms. Third, merely increasing supervised control and restrictions of the systems can yield to less flexibility, which can contribute to decreasing productivity. Lastly, the growth of collaborative business in the manufacturing industry blurs the boundary between insiders and outsiders, which becomes a varying attack vector.
The Intelligence and National Security Alliance (INSA) confirmed these difficulties in their 2018 report: only 8% of survey respondents have independent defensive preparations for insider threats, and majority of respondents answered that developing a skilled labor and the defensive preparations are elusive because it is difficult to detect and predict insider threats [8, 9] .
To understand and predict the insider attacks after developing insights into insider threats, several frameworks have been developed. A framework based on insider attacker's behaviors and symptoms involves several indicators to predict the insider threat in advance: markers, meaningful errors, preparatory behaviors, correlated usage patterns, verbal behavior and personality traits [14] . Furthermore, there is an analysis of the insider's motivations that can provide useful information to predict and detect insider threats. The motivations were examined by two factors taxonomy, which consists of six elements: intentional destruction, detrimental misuse, dangerous tinkering, naïve mistakes, aware assurance, and basic hygiene [15] . Bishop et al. [16] proposed Attribute-Based Group Access Control (ABGAC) to identify insiders by the notion of using policy discrepancies.
Applying machine learning to detect insider threats has been attempted. Community Anomaly Detection System (CADS) was proposed to detect insider threats based on the access logs of collaborative environments [17] . The system uses an unsupervised learning technique, which consists of two components: relational pattern extraction and anomaly prediction. Supervised learnings-Artificial Neural Network (ANN), Random Forest (RF), and Logistic Regression based classifier (LR)-were also utilized to predict the insider behavior by training limited ground truth [18] .
The blockchain has been adopted to several manufacturing systems, which share a similar vision of smart manufacturing systems while differs in detail, such as Cyber-Manufacturing System (CMS), Cyber-Physical System (CPS), and Cloud-Manufacturing System (CLMS). For CMS, the blockchain enabled Cyber-Manufacturing System application, called FabRec, was proposed [22] . The proposed application utilized the Ethereum Smart Contract to demonstrate the feasibility of connecting computing nodes and physical devices. Also, linking sensors and actuators to the main control device by using the blockchain has been attempted to enable a trustworthy and distributed management system for closedloop CPS [23] . CLMS also can benefit from adopting the blockchain [24, 25, 26] . The private cloud based on the blockchain was developed to ensure security in the system and to provide unique protocols and standards. However, it was asserted that the arbitrary block generation time and block occurrence frequency were lagging practical implementation to the real industry [27] . To avoid redundant delays yielded from a blockchain validation process such as Proof of Work (PoW), Song et al. [4] proposed a unique system architecture using a transaction hash as a key to access the uploaded data in the blockchain. Besides, various fields-medical, insurance, smart grid, supply chain-have adopted the blockchain in their systems to increase the security of the systems without the need of a central intermediary or third parties [28, 29, 30, 31, 32] .
PLC is a device to control physical machines in the shop floor. PLC controls the machines based on input and output data and uses Boolean values. Typically, PLC requires an operating system with the software for configuration. So it can be considered as a small computer that collects data from sensors or user input, operates the physical machines, and returns its operation result to the software or main operating system. The ladder logic is a graphical programming language that enables PLC engineers to write, read, or modify the program code easily. The steps for embedding the ladder logic code in PLC devices are: i) engineers write or modify code, ii) a software compiles the ladder logic code to binary code for PLC devices, and iii) a software uploads the code to the PLC device and embeds it to the system.
Since PLC is an important and dominant system component in the automation of manufacturing systems, it is likely that the controller would still be considered as a control device in CMS. However, PLC should meet new requirements and overcome security challenges to be adopted to the systems. Langmann et al. [19] proposed a new type of a PLC-Smart Industrial Control Services (SICS)-to fulfill the new conditions resulting from the new operation processes. The PLC enables the separated control function to the related equipment through a cloud server. SICS is powered by IEC61131-3 standard control program and can be run in two different modes: Server-Based Mixed Mode and Server-Based Mode. There is another study about extracting sensing data from PLC for CMS. The proposed method involves many approaches to identify useful data from the PLC devices. Conclusively, multithreading and HashSet algorithms improved the data extraction performance dramatically when these are applied to filter out the memory address [20] .
Security vulnerabilities of PLC devices in Supervisory Control and Data Acquisition (SCADA) systems have been analyzed. The communication between PLC and engineering station-which is in charge of configuration of the PLC-can be interfered or compromised by the attackers in three attack methods: replay attack, man-in-the-middle-attack, and stealth command modification attack [11] . There are two more attack methods-bypass logic attack and brute-force output attackoriented from legacy PLC protocols such as ModBus, Ethernet/IP, DNP 317 and Iso-TSAP. To address such attacks, five solutions have been proposed: protocol modification, protection via special filtering units, intrusion detection system, creation of demilitarized zones, and practices for securing PLC systems [21] .
PLC has been deployed for several decades without adopting any effective security defensive mechanism. Even though various security policies, restrictions, and traditional IT security such as firewalls have been adopted to prevent PLC program code from unauthorized configuration; the validation process for input data has been minimal. In 2000, Maroochy Shire-Queensland computerized waste management system-was hacked by a former employee. He had installed the malicious program in the sewage control system and attacked the system after his job application was rejected by the area's council. His laptop and a wireless radio were used to inject unauthorized commands to the system to dump a large amount of sewage into the public area in the city.
Also, PLC's high availability requirements can yield another security vulnerability. In 2010, Iran's nuclear facilities identified the Stuxnet computer worm, which was specifically targeting the Siemens control system and re-configuring PLC's programming-language-layer directly. Although it is unknown how the facilities became infected, this could happen because of the potential threats that insiders can voluntarily or involuntarily reveal accessible routes into the system to outsiders without being discovered [38] . Fig. 1 describes PLC testbed network environment that was set up to simulate communication between the main control server and PLC device with an internet network environment. A Linux OS based personal computer with i7 core and 64 GB RAM was utilized to represent the main control server that is used for execution and configuration of the PLC device. The PLC device used in the testbed is Raspberry Pi 4, which is a single-board computer with 40 General Purpose Input and Output (GPIO) pins. Raspbian operating system (OS) that is based on Debian OS was installed in Raspberry Pi to run PLC logic and remote communications.
To enable the PLC system in the devices, Open-sourced Programmable Logic Controller (OpenPLC) with IEC 61131-3 standard was installed. The OpenPLC provides simple graphical user-interfaces for the I/O monitoring, configuration, and ladder logic editor. Seen from Table 1 and Fig. 2 , OpenPLC's input address and Raspberry Pi's GPIO pin numbers were mapped, and a simple ladder logic code was created and uploaded to conduct experiments. Specific algorithms used for the testbed are presented in the above Fig. 3 . A virtual network was also established for the test environment. The main control server and PLC device are communicating by using UDP protocol that was constructed by Python with the SOCKET package. To allow interactions between Python and GPIO, GPIO packages for Python and WiringPi program for Raspbian OS were installed.
The blockchain is a way to store data in a distributed system where the system components connect to each other and form a peer-to-peer network. The main idea of the blockchain is storing the exact same data in all the components in the network and linking the old data and new data to maintain the entire data integrity [4] . Because of the connection between old and new data, the modification of the old data in the specific node affects its new data integrity. Therefore, any changes in the data could be readily detected by other components and prevented by using a consensus algorithm without inspecting the entire stored data. However, it is inefficient because the data need to be stored in all the components. The cumulative data size can grow very large with the given time, while the component's storage size is limited. Therefore, when the node is receiving, generating, and transferring data, it automatically creates a transaction hash. The transaction hash is a cryptonized identification of the data based on the data's value, timestamps, creator information etc. The transaction hash will not be directly distributed to the system components, but it will be distributed as a block form that contains many other transaction hashes. The block also has a block hash, which is an identification of the block, and it is distributed to the components. This distributed block will be chained with the previous distributed block by involving the previous block hash: thus, called the blockchain.
The blockchain's distributed data management system with the validation process can help to reduce insider threats in CMS. Given that the blockchain forces insiders to supervise each other mutually, the insider alone cannot manipulate or inject malicious data to compromise manufacturing processes without extensive computing power or assists from many other attackers. Fig. 4 shows the blockchain environment setup that is a technical countermeasure against insider threats. Ethereum Private Blockchain with Proof of Work (PoW) validation process was employed to form a blockchain network in the test environment via GETH v.1.9.6. client. GETH client is a command line user-interface for running Ethereum based blockchain network. The main control server distributes a bootnode address and enode address to the other nodes for peerto-peer connection and the blockchain participation as an initiative node. Three virtual machines join the blockchain as miner machines, which aid the blockchain's validation process. Lastly, two PLC devices are connected to upload and retrieve input data through the blockchain.
To make the PLC device as one of the nodes in the blockchain, three programs are run in the PLC device simultaneously: OpenPLC software, Python, and GETH client. OpenPLC and Python's GPIO package with WiringPi are interacting by I/O data. Uploaded input data in the private blockchain network is transferred by Python's Web3 package through GETH client to be utilized for PLC operations. As a result, PLC can have a two-way communication channel with the blockchain by employing Python as an intermediary. The channel's algorithms and architecture are presented in Fig. 5 and Fig. 6 . In this way, input data for PLC operations only can be accessed by entering a transaction hash that is returned when the input data is uploaded. Also, the input data is retrieved and temporarily existed in the memory of the PLC device when the valid transaction hash is entered, while the input data is permanently and immutably stored in the blockchain. Hence, insiders only can operate the PLC device based on preuploaded input data and cannot modify the data without attacking the blockchain. This means that PLC can now have a strong validation process for the input data. To evaluate the communication channel, the response time was measured. Retrieving data and uploading data including the validation process were tested, and one-hundred iterations of tests for each case were measured. The average retrieving data response time was 0.04 second and uploading data average time was 14.17 second. Due to the Proof of Work (PoW) validation process, uploading time is fairly longer than retrieving data, however, it will not affect the entire manufacturing process because uploading data would happen prior to actual manufacturing processing, and it requires much less frequently than retrieving data.
To validate and evaluate the security enhancement against insider threats using the blockchain, three attack scenarios have been implemented in the two testbeds. Two testbeds architecture can be found in Fig. 9 . 
Insiders can make modifications that can result in serious damage to the physical machines in the system. For example, insiders can change the sensitive manufacturing specifications by modifying input values such as heating treatment temperature, nozzle travels the speed of 3D-printer, and a spindle speed of CNC machines [10] . In the PLC testbed, it is assumed that the largest shareholder-a manufacturer in the manufacturer's factory-of the system is holding a ground truth while potential insiders could be PLC engineers, shop floor workers, or production managers [5] .
However, the blockchain can effectively prevent the data from insiders' malicious modification. The blockchain can reduce the insider threats risk by adding a validation process for input data. PLC can only proceed with legitimate input data by entering the transaction hash to retrieve corresponding data, which is already validated by the blockchain. Now, insiders need to find out the exact transaction hash value of false order and alter the embedded legitimate transaction hash to false order's transaction hash to attack the system, which is only Iteration available for the limited number of insiders. Also, since input data's transaction hash will be renewed every time when the main control server is sending the input data, it is not easy to identify the legitimate input data from the transaction hash. In another way, insiders can add new input data to the blockchain and acquire a malicious transaction hash. But insiders need to access the private blockchain, which allows only a limited number of insiders to join. Lastly, modifying the blockchain itself can be another way to directly modify input data. To do this, insiders require computing power more than 50% of the power of entire blockchain node machines or need to compromise and manipulate more than half number of entire the node machines' blockchain repository. However, such an attempt is not practical, so it is fruitless [27] .
Denial of Service (DoS) attack is the most common network attack vector in the world [39] . DoS attack disturbs information exchanges by making network resources to consume a sheer volume of invalid data. Prerequisites of DoS attack is comparably simple: being aware of Target's IP address and able to access to the target's Local Area Network. Therefore, it is very likely that insiders who already met the both prerequisites can exploit DoS attack to attack CMS.
To simulate UDP DoS attack on the PLC testbed and blockchain testbed, Kali Linux's hping3 tool was used. For the attack parameters, five packets per second with one-hundred bytes invalid data, which is very light attack intensity, was used to attack, because the tool was too strong enough to crush the program. For the experiment purpose, the main control server continuously sent out input data until the PLC device receives 100 input data, which represents 100 iterations. The attack was inflicted on the PLC device after the 50th iteration to compare with the ideal state. Finally, input data transferring time from the main control server to the PLC device was measured for the experiment result. The result can be found in Fig. 10 . Seen from above, the PLC testbed shows irregularities in time after the 50th iteration, while the blockchain testbed remains consistent. Total 28 iterations out of 50 in the PLC testbed was affected by the simulated UDP DoS attack. However, it is likely that the communication will be totally halted if the real UDP DoS attack was implemented. From the empirical experiments, UDP DoS attack with more than fifty packets per second was enough to crush the communication. For the blockchain testbed, the communication was not disturbed at all because the blockchain network is formed by multiple machines. Thus, to affect the blockchain communication, all the nodes should be attacked at the same time, which is called Distributed Denial of Service (DDoS) attack [40] . Since DDoS attacks requires tremendous volume of network resources from outside of the system, it is not covered in this research.
The main idea of Man In The Middle (MITM) attack is redirecting the victim's communication channel to attackers and manipulating or injecting malicious communication. Generally, a redirecting step needs a lot of efforts and conditions. For example, the Kaminsky Attack, which is one of the remote DNS cache poisoning attacks, is required to guess 16-bit transaction number to succeed in the attack [41] . The chance is one out of 2 !" . However, in regard to insiders, a simple modification of Address Resolution Protocol (ARP) with their access authentication enables to redirect the communication channel. Also, insiders can use MITM attack to create a race condition without poisoning DNS cache. This attack can be a more stealthy attack method because insiders can control the frequency of malicious output results. Thus, in this paper, the MITM attack for the race condition was tested.
To conduct the attack, the delays for sending and receiving input data for the main control server and PLC device in two testbeds were set by one second. To emulate the MITM attack, one virtual machine in the same LAN was added and performed the inside attacker by using SCAPY. For the test, the main control server sent out input data with Inp00, which is expected to activate Out00. Meanwhile, the attacker's virtual machine will send out input data with Inp01 whenever it detects a legitimate packet from the main control server. The test was conducted for 100 seconds, which represents 100 iterations, and the attack was implemented after the 50th iteration. The result is in Fig. 11 , where activated output 0 and 1 means Out00 and Out01, respectively. In the PLC testbed, the attack won the race condition at a rate of 50%, which can easily be modified by changing the number of sending input data. In the case of the blockchain testbed, there is no activated Out01, because the attacker cannot find or generate the transaction for Inp01. Also, since the blockchain delivers the transaction data through a peer-to-peer network, it is arduous to sneak into the network. Therefore, the blockchain technology provides an effective security enhancement against MITM attack. Although the transaction hash itself cannot be free from insider threats, the data in the transaction cannot be modified due to its distributed data management system. Moreover, the private blockchain system can limit and manage the network nodes, so it can help to trace and identify insiders effectively.
Insider threats are critical security challenges in Cyber-Manufacturing Systems (CMS) due to its trusted access authentication and knowledge of the systems. A blockchain provides a security enhancement against insiders while maintaining the systems' flexibility and connectivity between digital assets and physical assets. By storing data in a distributed way with the validation process, the blockchain provides decision-makers with higher trust in their manufacturing processes. It also enables the vision of CMS without replacing their legacy systems such as PLC.
To help understanding security challenges against insiders in CMS, an inclusive overview of insider threats is presented in this paper. also, summaries of PLC and the blockchain with the test environment setup are discussed in detail. A comparative analysis of three attack scenarios is illustrated with the two testbeds.
In spite of the promising security improvement provided, there are still unaddressed limitations and further possible improvements for future work. The blockchain has own security vulnerabilities against decentralized system specified attack vectors such as 51% attack, Long-Range Attacks, Sour Milk Attacks. Also, the current validation processes of the blockchain need to be refined to become more consistent, fast, and capable.