The advent of Industry 4.0 has transformed the global industrial environment by integrating digital technology with conventional manufacturing and production methods [1]. Industry 4.0 is intricately linked to the advent of cyber-physical systems (CPS), cloud computing, artificial intelligence (AI), big data analytics, and the Industrial Internet of Things (IIoT) [2]. These technologies collectively enable the automation of intelligent behavior, decentralization of decision-making, and real-time feedback, transforming factories into adaptive and self-optimizing smart factories. Industry 4.0 fundamentally represents an Industrial Internet of Things (IIoT) - a machine-to-machine connectivity designed and implemented to meet the stringent demands of the industry. The IIOT employs interconnected devices, embedded sensors, actuators, and intelligent machinery to compile and disseminate operational data across the value stream and across the supply chain. A MarketsandMarkets analysis indicates that the worldwide IIOT market size is projected to increase from USD 113.8 billion in 2021 to USD 197.6 billion by 2026, reflecting a CAGR of 11.4%. Demand is anticipated to rise as enterprises seek to enhance operational efficiency by modifying predictive maintenance and optimizing individual components within the constrained interval between the initial shipping phase and the designated recipient.
IIoT facilitates transformational applications across many industrial sectors. For example, the IIoT facilitates predictive analytics, utilizing IIoT data to reduce industrial downtime by up to 50% and maintenance by around 30% (McKinsey, 2022). In the energy sector, the IIoT can alleviate strain on the grid by establishing more dependable entities through performance-based data and reporting from dispersed and endemic assets [3]. The technical disruption generates significant cybersecurity challenges, particularly in managing security across a highly distributed and heterogeneous IIoT ecosystem that includes legacy systems, proprietary protocols, and geographically dispersed devices. The Industrial Internet of Things (IIoT) presents a significant attack vector that aims to generate revenue; security vulnerabilities include spoofing, man-in-the-middle attacks, data manipulation, and ransomware, among others. A 2024 analysis by IBM said that the industrial sector saw the highest incidence of cyber-attack occurrences for the second consecutive year, accounting for 23.2% of all recorded cases.
Traditional security frameworks centered on centralized and perimeter-based models are inadequate for several decentralized and dynamic IIoT scenarios, necessitating a deeper exploration of security structures capable of supporting more resilient, distributed, and tamper-resistant security frameworks. Regarding IIoT, blockchain technology is a significant suggestion for enhancing the cybersecurity framework in Industry 4.0 systems that has gained traction. Blockchain ensures data immutability, traceability, and transparency via a decentralized ledger, attributes essential for protecting IIoT settings. Furthermore, should adversaries target IoT devices, blockchain can eliminate single points of failure by dispersing trust among network nodes. This strengthens the foundational architecture of interconnected devices by offering enhanced resilience, security for communication, data integrity, and access control [4].
The convergent existence with digital technology under Industry 4.0 includes IoT capabilities along with the emergence of the Industrial Internet of Things (IIoT) thereby establishing and redefining efficiency in operations, automation and data-based decision-making [5, 6]. Whereas conventional infrastructure under a centralized model is facing connectivity and data speed challenges, IIoT infrastructure delivers nimbleness and real-time response and decision-making capabilities to a variety of systems including, plants, factories, grids, logistics, production, etc. Grand View Research indicates the IIoT market value will be approximately USD 594 billion in 2025, representing a CAGR of 23.3% expect to achieve about USD 1.7 trillion by thereafter in 2030. Though other reports provide slightly more conservative estimates, for example, Polaris projects IIoT value in 2022 at USD 313 billion with upwards projections to USD 2.58 trillion by 2032 at a CAGR of 23.5%. The IMARC report estimates the 2024 IIoT market estimate at USD 289 billion and increasing to USD 847 billion in 2033 at a stabilized CAGR of 12.7%. The most aggressive forecast the IIoT value will be USD 7 trillion in 2025 grows to over 30.9 billion devices just in 2026. Additionally, the IIoT does have an incredible impact for enhancement on the value-add business, even further than just a direct market investment. When IIoT deployed in manufacturing, it provides a range of cost savings of around 12% while reducing breakdowns by about 30% and reducing unscheduled downtime by about 70%. Ultimately IIoT analytics are estimated valorizing $7.1 trillion globally in value by 2025.
Despite its transformative potential, IIoT introduces unique cyber security vulnerabilities traditional systems weren’t designed for [7]. The sheer proliferation of sensors, machines, actuators, and controllers, often numbering in the tens of thousands per plant, creates exponentially more attack vectors. Industry estimates show that 90% of IIoT devices will be vulnerable by 2026 due to issues like misconfigurations and out-dated firmware. Industry setups are typically defined by heterogeneous architectures, mixing PLCs, SCADA, proprietary protocols, and cloud agents [8]. There is a critical absence of standard encryption, authentication, or interoperability protocols, resulting in inconsistent security practices at scale. Many industrial IoT devices run on minimal hardware, lacking cryptographic modules needed for secure identity verification. This deficiency enables spoofing, man-in-the-middle, and device impersonation exploits [9].
As IIoT networks transmit real-time telemetry and control commands across edge, cloud, and operational domains, data tampering or eavesdropping can lead to system misbehaviour, equipment damage, or safety incidents [10]. Legacy SCADA/ICS models depend on central HMIs and servers. These aggregation points are attractive targets for ransomware, distributed denial-of-service (DDoS), or supply chain attacks [11]. While IIoT demands millisecond-level response times, many current security systems only detect intrusions after weeks or even months. Industry averages show breach dwell times approaching 197 days, a lifetime in industrial terms. IIoT extends across systems maintained by third-party OEMs, suppliers, or engineers [12], enabling malware insertion or hardware trojans deep in the supply chain or through compromised insider credentials. Dynamic, global IIoT environments must align with GDPR, NIS2, the upcoming EU Cyber Resilience Act, and ISO 62443 standards. Failures here carry serious legal, financial, and reputational consequences.
Blockchain technology addresses the above challenges via its intrinsic qualities: decentralization, immutability, transparency, smart automation, and cryptographic identity, making it a compelling candidate for securing IIoT ecosystems [9, 13–15]. Permissioned blockchains (e.g., Hyperledger Fabric) eliminate central points of control. Devices validate peers in a trusted mesh rather than connecting to a zoned server. This architecture reduces single-point compromise risks and increases fault tolerance [16]. All validated transactions, sensor readings, control commands, asset movements, are cryptographically hashed into an append-only ledger. Any tampering becomes detectable, safeguarding data integrity which is foundational for automation and compliance [14, 17]. Live, shared ledgers enable real-time auditability of asset history, from assembly to shipment. This end-to-end traceability is essential in supply chains, facilitating accountability and reducing fraud or counterfeiting risks [7, 12].
Self-executing business logic (sales contracts, maintenance triggers, and payment settlements) lives on-chain as smart contracts. These guarantee automated enforcement of policies, SLAs, and security checks without human intervention [5, 6]. Blockchain-based decentralized identity systems assign unique digital IDs to IIoT endpoints. Authentication is performed peer-to-peer, eliminating trust in centralized CAs and preventing spoofing or rogue-device access [18, 19]. By automating trust, removing intermediaries, and minimizing central server dependency, blockchain-empowered systems reduce operational overhead. IIoT pilots report cost savings of 25–40%, especially in logistics and energy applications [10, 20, 21].
The combination of blockchain and IIoT has started to show real value in many industries, with a broad spectrum of enhancements from transparency to automation and resilience [22]. For example, a field implementation in Canada within the energy sector illustrated the successful use of permitted blockchain for peer-to-peer (P2P) energy trading within a microgrid. This implementation allowed for decentralized energy trading and also had the excellent outcome of a 46% reduction in peak electrical demand. In addition, microgrid participants enjoyed approximately 6% savings in utility costs a week, which demonstrated both operational and economic efficiency improvements. Another example can be found in the automotive industry, where the merging of blockchain and IIoT is revolutionizing supply chain transparency and quality control. Vehicle manufacturers are now using this integration for tracing every component in real time throughout the assembly process. For example, by recording the origin and transfer of each part on an immutable ledger, companies can now evaluate if a tampering event occurred and also ensure that components meet quality control requirements. This enhanced capability allows for traceability audits, which previously took weeks a few years ago, to be completed in real time thus improving both compliance and the operations response.
Smart energy grids are also benefiting from the blockchain-enhanced IIoT [23]. Edge devices and substations utilize blockchain to not only validate assets but also securely transact data. For example, smart meters can even automate billing as soon as use is validated on the blockchain, which speeds up the transaction process and creates trust between the utilities and consumers, positively impacting overall energy infrastructure efficiency and resiliency. Another significant use case is connected and autonomous vehicles. Blockchain technology securely updates over-the-air (OTA) software, encrypts firmware packages, and assures authorization for the vehicle’s components. These are vital methods of protecting vehicles from cyber threats like spoofing and unauthorized access given that connected vehicles are enabling a more significant potential for attack surface area. The takeaways from this section characterize how blockchain and IIoT are changing industrial operations by, among others, increasing security, automation, and transparency [24–26].
There is considerable potential for blockchain to greatly improve the security and efficiency of industrial systems as well as many security enhancers, but there are many hurdles with its application in a large-scale IIoT environment. The major concern is scalability and performance since industrial use cases often need (sub-second) latencies in order to operate foreseeably (real-time) to provide value to the user. Hybrid architectures including off-chain edge caches (downloaded quickly) can balance speed and integrity in some cases, but must also consider regulatory and privacy expectations in their deployment. Not to forget that the rules for blockchain recording will be immutable, and thus it is important to include privacy-by-design elements like off-chain storage of personally identifiable information (PII), and ways to allow GDPR compliant data redaction. Bridging legacy systems is another major hurdle.
Industrial environments are still awash in non-IP programmable logic controllers (PLCs) and SCADA systems, which means many applications cannot link to existing infrastructure without an abstraction layer, which do not seamlessly allow for modern blockchain solutions to incorporate these. As a result, middleware platforms or secure APIs must be used to integrate with systems not designed for disruption, which isn’t as straightforward. Organizational change considerations are equally important. The divide between the IT and operational technology (OT) teams is still prevalent in many businesses, with 41% of industrial operators’ indicating siloed structures in their organizations. Blockchain integration, and ensuring the right governance frameworks, along with educating staff, are even more formidable tasks [27]. Managing identity within devices throughout their lifecycle - including on boarding, certificate management and secure provisioning, utilizing solid PKI or decentralized identity systems, as while they can be secured at scale, trusted environments remain essential.
Notwithstanding the increasing potential of blockchain in facilitating Industrial IoT (IIoT) and Industry 4.0, research is still in its nascent phase. A multitude of unsolved difficulties constrains its practical implementation and scalability in industrial settings [28–30]:
- (i)
Absence of empirical validation: Most studies of blockchain in industrial applications are conceptual or simulation-based, with little evaluation of blockchain under real-world industrial operational conditions. Consequently, practical credibility and adoption of blockchain are weakened.
- (ii)
Scalability issues and latency issues: Public blockchains, for example, Bitcoin and Ethereum, both suffer from low transaction throughput and long confirmation delays, while lack of comparative studies means private or consortium blockchains continue to be an unmeasured variable in different industrial contexts.
- (iii)
Interoperability challenge: Current frameworks rarely consider adoption pathways that integrate with existing legacy SCADA systems; proprietary protocols; or heterogeneous hardware. All of which make migrating to blockchain mediated IoT architecture costly, disruptive, and risky for production-critical operations.
- (iv)
Smart contract weaknesses: Although proposed to provide authentication and access controlling in industrial contexts, the concept of smart contracts is still very much an unexplored space in safety-critical environments. Work on formal verification of smart contract logic is limited, as are studies on robust testing smart contract logic.
- (v)
Technology integration failure: Research rarely considers the co-design of blockchain with complementary technologies such as AI, edge computing, and digital twins, meaning that the siloed approach of working with each technology independently leads to limited scale, ability to build responsive systems, and sustainable design in an Industry 4.0 future.
Numerous challenges remain in employing blockchain inside IIoT and Industry 4.0, including insufficient real-world testing, scalability and latency issues, and incompatibility with existing systems. Smart contracts are little examined in safety-critical situations, and there is a lack of study on their formal verification and testing. Blockchain research frequently operates in isolation, overlooking the synergy with complementing technologies like artificial intelligence, edge computing, and digital twins.
In light of the constraints recognized in current literature, the suggested framework employs a stratified architectural design for blockchain-enabled Industrial Internet of Things systems. The architecture is designed to improve scalability, interoperability, and latency management, while ensuring security and regulated access. Each layer is delineated with distinct functional responsibilities to facilitate efficient and dependable industrial processes. In the first design phase, the overall architecture of the proposed framework is designed. This includes:
- (i)
A blockchain layer, where a permissioned blockchain (e.g., hyperledger fabric) is used to support scalability in the industrial ecosystem while allowing some controls over who can write on the blockchain.
- (ii)
IIoT layer, which includes smart devices, sensors, PLCs (programmable logic controller); and industrial gateways
- (iii)
Edge computing layer, provided for low-latency preprocessing tasks and to offload the blockchain from IIoT data to improve congestion by aggregating and filtering IIoT data prior to writing to the blockchain
- (iv)
Application layer, which is made up of analytics dashboards, control interfaces, and regulatory compliance modules.
The layered architecture provides modularity, scalability and low-latency connectivity [14, 31].
Figure 1 illustrates the architecture of the proposed blockchain-enabled IIoT framework, integrating edge devices, smart contracts, and off-chain storage. It highlights the flow of data from sensors to the blockchain via edge agents, ensuring secure, traceable, and decentralized processing.
Architecture of the proposed framework
The system incorporates a lightweight consensus mechanism (for example, RAFT or PBFT) to facilitate fast and fault-tolerant transaction processing. A Public Key Infrastructure (PKI) identity management model establishes unique digital identities assigned to each device or user. These digital identities are issued as certificates validated by the blockchain. Access control is enforced using smart contracts, which imposed Role-Based Access Control (RBAC), and it is important that the smart contracts actively assess the authentication (assess the user and device role relationship) before granting access. The overall architecture improves security, efficiency, and trust in these industrial situations [32, 33].
- (i)
Consensus Mechanism: A lightweight consensus algorithm such as RAFT or PBFT is implemented to provide fault tolerance and faster transaction processing for industrial uses.
- (ii)
Identity Management: A PKI-IdM model is designed. Each device or user will be issued a digital identity using certificates validated by the blockchain.
- (iii)
Access Control: Smart contracts are applied to enforce a Role-Based Access Control (RBAC) interpretation on smart contracts, before allowing access to services and data, it is important that the smart contracts dynamically check and confirm the associated permissions for users and devices prior to access of services/data.
- (iv)
Decentralized identity management in this way facilitates the secure and auditable interaction of devices [34].
RAFT was chosen as the consensus mechanism for the proposed blockchain network due to its lightweight nature, fault tolerance, and efficiency in permissioned contexts like IIoT. In contrast to resource-intensive consensus methods (e.g., Proof of Work), RAFT attains leader-based consensus via log replication, hence minimizing delay and resource expenditure, Table 1. This renders it exceptionally appropriate for industrial environments where devices may possess constrained processing capabilities and where rapid, predictable consensus on transactions is essential.
Comparison of RAFT, PBFT, and Other Consensus Mechanisms
| Consensus Mechanism | Fault Tolerance | Communication Overhead | Scalability | Latency | Suitability for IIoT/Industry 4.0 |
|---|---|---|---|---|---|
| RAFT (Crash Fault Tolerant) | Tolerates crash faults (non-Byzantine) | Low (leader-based log replication) | High (scales well with nodes) | Low latency | Highly suitable: lightweight, efficient, good for trusted/permissioned environments |
| PBFT (Byzantine Fault Tolerant) | Tolerates Byzantine and crash faults | High (quadratic communication with node count) | Moderate (limited by overhead at scale) | Moderate to high latency | Suitable when adversarial behavior is expected, but less efficient for large IIoT systems |
| PoW (Proof of Work) | Byzantine fault tolerant | Very high (energy- and compute intensive) | Low (resource expensive, slow) | Very high latency (seconds–minutes) | Unsuitable: resource-heavy and impractical for real-time operations |
| PoS (Proof of Stake) | Byzantine fault tolerant | Moderate (depends on stake distribution) | Moderate to high | Higher latency than RAFT, lower than PoW | Less suitable: requires stake economics, not aligned with industrial trust model |
In this phase, a comprehensive security analysis is completed employing STRIDE and MITRE ATT&CK frameworks with respect to industrial control systems. The system is threat modeled and threat vectors identified such as spoofing and replay attacks in the IIoT and blockchain layers. The countermeasure can be digital signatures, nonce, and/or validating the transaction. The immutable audit logs from a blockchain enables forensic analysis to support recovery and compliance [35, 36]
- (i)
Threat Modelling: To identify threats (e.g., spoofing, data tampering, replay attacks) in IIoT communication and blockchain interfaces.
- (ii)
Defence Mechanisms: Are the countermeasures employed (e.g., digital signatures, nonce communication, verifying transaction).
- (iii)
Audit Logs: With blockchain, an immutable log is kept, that can augment forensic audit trails for incident unresolved incident responses and regulatory compliance.
This phase serves to validate that the entire system and its components have been threat modeled against common industrial cyber-security threats [28]. Table 2 delineates significant security risks, their respective responses, and the efficacy of these approaches in protecting industrial and blockchain systems.
Security Threats, Countermeasures, and Results
| Threat (STRIDE / MITRE ATT\&CK) | Countermeasure | Result |
|---|---|---|
| Spoofing (Identity Theft / T1078 – Valid Accounts) | PKI-based digital identities, digital signatures | Ensures strong authentication and prevents unauthorized device/user impersonation |
| Replay Attacks (T1001 – Data Obfuscation / T1071 – Application Layer Protocol) | Nonce values, timestamp validation in transactions | Prevents reuse of captured messages, ensuring data freshness and integrity |
| Tampering (Data Manipulation / T1565 – Data Manipulation) | Transaction verification, hash integrity checks in blockchain | Detects and prevents alteration of IIoT or blockchain data |
| Repudiation (T1070 – Indicator Removal on Host) | Immutable blockchain audit logs | Provides non-repudiation and forensic evidence for compliance and recovery |
| Information Disclosure (T1040 – Network Sniffing) | End-to-end encryption (TLS/DTLS) | Protects sensitive industrial data during transmission |
| Denial of Service (T1499 – Endpoint DoS) | Rate limiting, edge filtering, consensus mechanism resilience | Maintains service availability under attempted overload conditions |
| Elevation of Privilege (T1548 – Abuse Elevation Control) | Role-Based Access Control (RBAC) enforced via smart contracts | Restricts unauthorized privilege escalation, ensuring secure access control |
The security assessment maps delineated risks and corresponding remedies, illustrating resistance against spoofing, manipulation, replay, and several other attacks. The solution guarantees authenticity, integrity, availability, and compliance in industrial environments by utilizing PKI, smart contracts, encryption, and immutable audit logs.
In order to assess the viability and success of the proposed system, a prototype implementation will be developed in a testbed environment (e.g., Raspberry Pi clusters, Hyperledger Fabric, Node-RED, simulated IIoT sensors).
- (i)
The following metrics will be used to determine the performance:Latency: Time from an event occurring from a sensor to the point of confirmation on the blockchain.
- (ii)
Throughput: Transactions per second the system can process.
- (iii)
Resource Usage: CPU, memory and bandwidth used on the edge devices. Security: Evaluated through penetration testing and strength against known attack vectors.
The implementation plan outlines the assembly and implementation of the proposed blockchain-based security framework within one of the proposed Industry 4.0 testbed environments under controlled conditions. The first step involves the configuration of a Hyperledger Fabric network with a set of peer nodes, ordering services and certificate authorities. Simulated IIoT devices communicate with the security framework using the MQTT standard. Edge agents are primarily responsible for performing preprocessing on data and writing transactions to the blockchain. The smart contracts created in this framework provide the guarantees of the access control list, the integrity of data and automated triggers for maintenance of the device. For off-chain storage of data the IPFS process is utilized, while all transactions are logged on-chain. The performance evaluation will be determined based on the measurement of a set of key performance indicators (KPIs) such as latency, throughput and resiliency of the system. These theoretical scenarios are designed to release a working model of the proposed security framework in a realistic industrial context [29].
The environment is set up using Hyperledger Fabric v2.4, a permissioned blockchain framework suited for enterprise applications. The network includes four peer nodes across two organizations and a single ordering service using RAFT consensus for reliability. Smart contracts (chaincode) are developed in Go for efficient execution. CouchDB is used to store the world state, enabling rich queries on blockchain data.
Framework: Hyperledger Fabric (v2.4)
Network: 4 peer nodes (2 organizations), 1 ordering service (RAFT consensus)
Chaincode Language: Go
Database: CouchDB for world state storage
The IIoT simulation utilizes Raspberry Pi 4 devices to emulate industrial sensors and PLCs. Secure communication is established using MQTT over TLS, with local processing handled by Node-RED and Python-based agents. These agents also trigger chaincode interactions with the blockchain. A Mosquitto MQTT broker facilitates message exchange, while off-chain data is stored in IPFS, with hashes recorded on-chain. Visualization and management are supported through a Grafana dashboard and the Fabric CA interface.
Devices: Raspberry Pi 4 (4 GB RAM) simulating sensors and PLCs
Protocols: MQTT over TLS for IIoT communication
Edge Layer: Node-RED and Python agents for local preprocessing and chaincode invocation
Data Broker: Mosquitto MQTT Broker running on local server
Cloud/Storage
Off-chain data: Stored in IPFS with hash stored on the blockchain
Visualization: Grafana dashboard and Fabric CA interface for device/user management
Table 3 presents the system model description, outlining key components, technologies, and configurations used in the proposed framework. It provides a clear overview of device roles, communication protocols, and blockchain settings within the IIoT testbed environment.
System Model Description
| Module | Description |
|---|---|
| Identity & Access Control | Each device and user gets a digital certificate from the CA. Smart contracts verify roles before granting data access. |
| Smart Contracts | Three chaincodes developed: (1) Predictive Maintenance, (2) Supply Chain Logger, and (3) Access Auditing. |
| Security Agents | Python scripts deployed on edge gateways that intercept data, compute hashes, and push to blockchain. |
| Monitoring Dashboard | Visual interface for network health, transaction logs, and device status. |
Setting up the Hyperledger Fabric network will be the first step through initializing it using Docker Compose. This will set up peer nodes for the blockchain network, ordering services, and Certificate Authorities (CAs). Devices and users (prosumers, consumers, etc.) on the network will need to register and enroll with the Fabric CA to create their digital identities. Next, smart contracts for access control and event logging will be installed and instantiated on the blockchain. Edge agents will be deployed to individual Raspberry Pi devices to hash their IIoT sensor data, convert MQTT messages, and push transactions to the blockchain, while retaining raw data off-chain within IPFS. The Mosquitto MQTT broker will need to be setup in advance to provide a way of securely exchanging messages that provide semi-structured data to the distributed ledger. Demo scenarios of industrial processes are simulated (like monitoring temperature, product flow, etc.). During the simulation, the system will log some metrics such as transaction throughput, CPU usage or overall performance, which could be valuable for analysis and evaluation.
Network Initialization: Start Fabric network via Docker Compose and define the Peers, Orderers, and CAs.
Certificate Enrollment: Enroll devices and users through Fabric CA, creating digital identities.
Chaincode Deployment: Install and instantiate smart contracts to control access and log activity.
Edge Agent Integration. Deploy agents on Raspberry Pi that hashes our sensor data and sends them as transactions.
MQTT and IPFS Configuration: Configure Mosquitto broker for communication as well as IPFS for off-chain storage.
Simulation Execution: Start simulated industrial processes (temperature monitoring and tracking product flow).
Logging and Evaluation: Log transaction metrics, CPU usage and continued behaviours of the system over time.
This dataset is a contemporary provenance-based dataset developed for industrial IoT environments under APT attack. It contains network logs as well as provenance traces covering phases such as lateral movement, exfiltration and persistence. Relevance of dataset: Excellent coverage of detection models and as a use case for blockchain applications such as immutable logging and tracing a threat. Gotham Dataset 2025 is a large-scale IoT network dataset has been developed capturing benign and malicious traffic exploring a variety of IIoT testbed environments.
Primary: Minimize end-to-end latency and maximize throughput (TPS) of the permissioned blockchain under IIoT loads.
Secondary: Maximize security detection performance (e.g., F1/AUC) and minimize resource overhead (CPU, memory), and MTTR (mean time to respond) for incidents.
The experimental configuration utilized a diverse array of blockchain servers, IIoT edge devices, and machine learning/analytics infrastructure, Table 4. This arrangement guarantees result repeatability and accurately represents a true Industry 4.0 testbed environment.
Hardware and Software Environment
| Category | Specification |
|---|---|
| Blockchain/Server Nodes | Intel Xeon Silver 4210 (10 cores, 2.2 GHz, 32 GB RAM, 1 TB NVMe SSD), Ubuntu 22.04 LTS, 1 Gbps Ethernet |
| Edge/IIoT Devices | Raspberry Pi 4 Model B (Quad-core Cortex-A72 @ 1.5 GHz, 4 GB RAM, 64 GB microSD), Raspberry Pi OS |
| Blockchain Platform | Hyperledger Fabric v2.4, Docker v20.x, Docker Compose v2.x, CouchDB v3.x (world state DB) |
| Messaging Protocols | MQTT (Mosquitto v2.0), CoAP (Eclipse Californium) for device communication |
| ML/Analytics Server | Intel Core i7-11700K (8 cores, 3.6 GHz, 16 GB RAM), NVIDIA GTX 1660 Ti (6 GB VRAM), Ubuntu 22.04 LTS |
| ML Frameworks | Python 3.10, TensorFlow 2.12, PyTorch 2.0, Scikit-learn 1.2, Optuna 3.0 (for hyperparameter tuning) |
| Monitoring Tools | Prometheus, Grafana, Node Exporter, Docker Stats |
| Random Seed Control | Seeds: 42, 123, 2025, 7, 99 across all experiments (NumPy, TensorFlow, PyTorch, Fabric configs) |
To evaluate the performance and effectiveness of the proposed framework, multiple experiments were conducted. Results are reported across latency, throughput, resource utilization, and security effectiveness.
The performance metrics demonstrate the system’s efficiency and suitability for industrial IIoT environments. The average transaction latency was recorded at 315 milliseconds, indicating rapid confirmation from the moment a sensor event occurred to its validation on the blockchain. Under load, the network sustained a throughput of 75 transactions per second, showcasing its capacity to handle frequent data exchanges. The RAFT consensus mechanism maintained an average block creation time of approximately 2 seconds, ensuring timely data logging. Additionally, the edge devices exhibited an average CPU usage of 42%, confirming that the lightweight agents effectively preserved system stability without overloading the hardware. Table 5 shows the performance matrices.
Performance matrices and their description
| Metric | Result | Description |
|---|---|---|
| Transaction Latency | 315 ms (avg) | Time from IIoT sensor event to blockchain confirmation. |
| Throughput | 75 TPS | Number of transactions handled by the network under load. |
| Block Creation Time | ~2 sec | Average block interval in RAFT consensus. |
| Edge CPU Usage | 42% avg | Lightweight agents kept device CPU below critical thresholds. |
There were four Raspberry Pi nodes in the testbed. This was a small-scale prototype that didn’t really show how large-scale IIoT systems work in the real world. Even though this is a limitation, the recorded metrics, an average transaction latency of 315 ms and a throughput of 75 TPS, are important because they show that the system can handle IIoT events quickly and reliably even when there aren’t enough resources. The results show that the RAFT consensus mechanism and the lightweight edge agents work well. This gives us confidence that the method can be used on bigger networks with better hardware while still keeping low latency and high throughput.
The system demonstrates strong storage and data efficiency through strategic off-chain data handling. Approximately 200 MB of sensor and process data was stored in IPFS, reducing the on-chain storage burden. After processing 1,000 transactions, the blockchain size remained compact at around 8.4 MB. Each data record was hashed using SHA-256, with an average hashing time of just 8.2 milliseconds. This approach ensures secure, scalable data management while maintaining high performance. Table 6 shows the storage and data efficiency.
Storage and data efficiency
| Parameter | Result |
|---|---|
| Off-chain data size | ~200 MB (stored in IPFS) |
| Blockchain size after 1000 txns | ~8.4 MB |
| Hashing time per record (SHA-256) | 8.2 ms |
The security evaluation confirms the system’s resilience against key threat scenarios. Data tampering was successfully detected through smart contract-triggered hash mismatches, ensuring data integrity. Replay attacks were effectively prevented by verifying nonce and timestamps in each transaction. Unauthorized access was blocked using on-chain role-based access control, which dynamically validated user and device permissions. Additionally, the system remained operational during peer node failures, thanks to RAFT consensus supporting fault tolerance and recovery. Table 7 displays the security evaluation matrices.
Security Evaluation matrices
| Threat Scenario | Outcome | Defences |
|---|---|---|
| Data Tampering | Detected | Hash mismatch alerted via smart contract |
| Replay Attack | Prevented | Nonce and timestamp verification |
| Unauthorized Access | Blocked | Role-based access enforced on-chain |
| Node Failure | Recovered | Peer node failure tolerated by RAFT consensus |
The comparative analysis highlights the advantages of the proposed blockchain framework over traditional security methods. Unlike traditional systems that suffer from a central point of failure, the blockchain approach is decentralized, enhancing fault tolerance. Tamper resistance is significantly improved through the use of an immutable ledger, while auditability is streamlined via automated, realtime logging. Device authentication is also more robust, combining PKI certificates with blockchain validation. Although the initial integration complexity is higher, the long-term benefits in security, transparency, and reliability outweigh the setup effort. Table 8 provides a comparative analysis between the proposed blockchain-based framework and traditional security systems. It highlights key differences across critical features.
Comparative Analysis of block chain and traditional system
| Feature | Traditional Security | Proposed Blockchain Framework |
|---|---|---|
| Central Point of Failure | Yes | No (decentralized) |
| Tamper Resistance | Low; unauthorized changes detected in >500 ms | High; unauthorized changes detected in <50 ms |
| Auditability | Manual logs; verification takes several minutes | Automated, real-time; verification <100 ms per transaction |
| Device Authentication | PKI / Static keys; higher impersonation risk | Certificates + Blockchain; stronger authentication |
| Integration Complexity | Medium | High (initial setup) |
Figure 2 and Figure 3 illustrates a performance comparison between the proposed blockchain-based framework and the traditional security framework across key metrics: latency, throughput, CPU usage, and storage efficiency. The chart visually demonstrates how the blockchain approach achieves lower latency, higher transaction throughput, and optimized CPU utilization through lightweight edge agents. Additionally, it shows reduced on-chain storage requirements due to the use of IPFS for off-chain data handling. This comparison emphasizes the scalability, efficiency, and robustness of the blockchain-enabled solution in contrast to conventional systems used in industrial environments.
Comparison block chain framework verses traditional frame work
Memory Usage Over Time in the Blockchain Framework vs Traditional Approach
Performance metrics comparing the proposed blockchain framework to traditional methods clearly demonstrate improvements in latency, throughput, CPU efficiency, and storage usage with the blockchain-based approach.
Blockchain Framework: Memory consumption increases with time in a linear fashion and levels out (~210 MB at 10 min) which would indicate that the growth is predictable.Traditional Approach: Memory consumption grows much faster (~260 MB at 10 min) indicating higher overhead and less efficiency overtime.Conclusion: Memory consumption from the blockchain is more efficient for long-lived IIoT/Industry 4.0 instances because of the way data is logged in the blockchain and utilizing the optimized off-chain storage.
Figure 4 illustrates that the blockchain methodology exhibits reduced latency while accommodating a greater number of devices compared to the conventional way.
Latency versus Load (line chart)
The blockchain framework responds significantly quicker across all scenarios (tampering, replay attacks, unauthorized access, and node failures). Figure 5 depicts the security incident response time of the proposed blockchain-based framework across various threat scenarios; including data tampering, replay attacks, and unauthorized access. It highlights the system’s ability to detect and respond to these incidents quickly through mechanisms such as smart contract-triggered alerts, nonce verification, and role-based access control.
Security incident response time
The visual comparison underscores the reduced response time enabled by automated blockchain processes, demonstrating enhanced real-time threat mitigation capabilities compared to traditional manual detection methods. In figure 5, Memory Resource Usage over Time (Blockchain and Non-Blockchain),Response Time for Security Incidents (Data Tampering, Replay Attack, Unauthorized Access), Blockchain Event Timeline (Device Authentication → Data Logged → Smart Contract Triggered → Audit Check).
The composite illustration, Figure 6, of memory utilization over time, response time to security breaches (Blockchain versus traditional systems), a timeline of Blockchain events, and throughput comparison for transactions per second (Blockchain versus traditional systems) enables readers to concurrently assess the performance and security advantages of the proposed blockchain framework.
Key Performance Metrics
The assessment of key performance parameters reveals that the blockchain-enabled framework surpasses conventional systems regarding scalability, security, and interoperability. The integration of edge computing markedly lowers latency and mitigates network congestion via effective data preparation. Thus, the suggested architecture guarantees improved dependability and credibility for Industry 4.0 applications.
Statistical validation was performed using paired t-tests on the performance measures to guarantee the robustness of the findings. This investigation presents quantitative proof of the substantial enhancements provided by the blockchain-based architecture compared to conventional methods. Table 9 and Table 10 present the statistical validation of experimental results using paired t-tests and ANOVA to compare blockchain, hybrid, and traditional approaches.
Statistical validation: t-tests
| Latency under Network Load | |
|---|---|
| t-statistic | p-value |
| -5.04 | 0.0039 (< 0.01) |
| Memory Usage over Time | |
| t-statistic | p-value |
| 0.29 | 0.7795 (>0.05) |
| Security Incident Response Time | |
| t-statistic | p-value |
| -13.86 | 0.0008 (< 0.001) |
Statistical validation: ANOVA
| Latency under Network Load | |
|---|---|
| F-statistic | p-value |
| 6.16 | 0.0111 (< 0.05) |
| Memory Usage over Time | |
| F-statistic | p-value |
| 0.085 | 0.918 (> 0.05) |
| Security Incident Response Time | |
| F-statistic | p-value |
| 26.44 | 0.00017 (< 0.001) |
The difference in latency between blockchain and traditional methods is statistically significant. Blockchain consistently provides lower latency. The difference in memory usage is not statistically significant. While blockchain shows smoother growth, both approaches have overlapping memory performance trends in this dataset. The difference in incident response time is highly significant. Blockchain-based security reacts much faster to tampering, replay attacks, and unauthorized access.
To further evaluate the comparative performance, ANOVA tests were used to assess the comparative performance of blockchain, hybrid, and conventional techniques. The findings indicate notable enhancements in latency and security incident response time for blockchain-based approaches, although disparities in memory efficiency remain statistically ambiguous. These findings underscore the distinct operational benefits of blockchain, with potential for enhanced resource use.
A statistically significant variation in delay exists across the three techniques. Blockchain and hybrid methodologies exhibit significantly reduced latency in comparison to conventional methods. No statistically significant disparity in memory utilization. Although Blockchain and Hybrid are more visually appealing, their statistical performance in this dataset is comparable to that of Traditional. Compelling evidence of a substantial disparity in event reaction time. Blockchain methodologies exhibit much superior speed compared to traditional systems in terms of latency and security response; nevertheless, the improvements in memory efficiency remain inconclusive. Table 11 presents the statistical validation of the experiment, detailing the test type, comparison, significance, and interpretation.
Statistical Validation of Experimental Results
| Metric | Test Type | Comparison | Test Statistic | p-value | Significance | Interpretation |
|---|---|---|---|---|---|---|
| Latency (ms) | Paired t - test | Blockchain vs Traditional | t = -5.04 | 0.0039 | Yes (p < 0.01) | Blockchain significantly lowers latency. |
| ANOVA (F-test) | All methods | F = 6.16 | 0.0111 | Yes (p < 0.05) | Significant overall difference across methods. | |
| Blockchain vs Traditional | – | 0.0157 | Yes | Blockchain significantly better than Traditional. | ||
| Memory Usage (MB) | Paired t-test | Blockchain vs Traditional | t = 0.29 | 0.7795 | No | No significant difference. |
| ANOVA (F-test) | All methods | F = 0.085 | 0.9184 | No | No significant difference across methods. | |
| Response Time (s) | Paired t-test | Blockchain vs Traditional | t = -13.86 | 0.0008 | Yes (p < 0.001) | Blockchain significantly faster response. |
| ANOVA (F-test) | All methods | F = 26.44 | 0.00017 | Yes (p < 0.001) | Strong significant difference across methods. | |
| ANOVA (F-test) | All methods | F = 26.44 | 0.00017 | Yes (p < 0.001) | Strong significant difference across methods. |
The statistical validation demonstrates that blockchain routinely surpasses traditional systems in reducing latency and accelerating security incident response, with both paired t-tests and ANOVA indicating substantial significance. The findings underscore blockchain’s capacity to provide operational efficiency in industrial operations. Nevertheless, memory use exhibits no statistically significant variation, indicating that resource optimization continues to be a persistent difficulty. The uniformity of results across several tests enhances the dependability of the conclusions. Although blockchain has distinct performance advantages, further study is necessary to improve memory efficiency and corroborate results in more extensive industrial contexts.
The increasing complexity and interconnectivity of industrial systems in the context of Industry 4.0 and the Industrial Internet of Things (IIoT) have created substantial cybersecurity challenges that traditional centralized security models find difficult to manage, owing to the dynamic, distributed, and heterogeneous characteristics of smart industrial environments. This article introduced a blockchain-based cybersecurity framework tailored for industrial systems, incorporating decentralized identity management, immutable audit logging, and access control using smart contracts. The architecture improves trust, traceability, and resilience in IIoT systems by utilizing the immutability, transparency, and decentralization of blockchain technology. Experimental findings indicated enhanced performance in critical parameters, such as transaction latency, throughput, and incident reaction time, relative to conventional systems. The design facilitates scalable, resource-efficient deployment on edge devices, allowing for real-time industrial operations, thereby affirming blockchain as a safe data management solution and a catalyst for autonomous, resilient industrial processes.
The suggested framework covers several essential security concerns; yet, there are multiple potential to enhance and expand its functionalities. Future endeavours encompass extensive implementation on actual industrial networks exceeding 1,000 nodes to assess scalability and performance under substantial load, incorporation of AI-based anomaly detection for real-time threat surveillance with autonomous smart contract responses, and formal verification of smart contracts to guarantee accuracy and security. Further instructions entail investigating cross-chain interoperability for secure data exchange among factories and supply chain collaborators, implementing privacy-preserving methodologies like zero-knowledge proofs to uphold confidentiality while guaranteeing auditability, and performing compliance assessments with standards such as ISO 62443 and regulations like GDPR. Moreover, investigations into lightweight consensus algorithms tailored for edge devices might diminish computational expenses and energy use, while comprehensive real-world field experiments will evaluate system resilience under varied operating settings. These innovations seek to enhance the security, scalability, and comprehensive compatibility of blockchain-based cybersecurity frameworks with the practical and regulatory demands of industrial IIoT networks.