Tides of Blockchain in IoT Cybersecurity
<p>IoT architectural layers.</p> "> Figure 2
<p>Blockchain transaction process.</p> "> Figure 3
<p>A diagram of an example of the combination of blockchain and IDS, visually demonstrating the benefits of intrusion detection [<a href="#B46-sensors-24-03111" class="html-bibr">46</a>].</p> "> Figure 4
<p>PRISMA flow illustrates how the final 111 papers at the reference were chosen and the 10 publications that specifically addressed combining blockchain with AI for IDS in IoT.</p> "> Figure 5
<p>Illustrating the integration of blockchain and machine learning-based intrusion detection.</p> "> Figure 6
<p>Screenshot showing the compilation of the smart contract.</p> "> Figure 7
<p>Screenshot showing the connection of the smart contract ABI.</p> "> Figure 8
<p>Screenshot showing the connection between Python and Ganache.</p> "> Figure 9
<p>Screenshot showing the blockchain interaction function.</p> ">
Abstract
:1. Introduction
- 1.
- Utilizing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) article collection approach, this study systematically gathers articles on AI, blockchain, IDS, IoT, and IIoT, shedding light on challenges, trends, and emerging review areas in IoT IDS designs for security.
- 2.
- Focusing on articles published between 2019 and 2024, this review captures recent advancements in AI and blockchain-based IDS designs, ensuring relevance and currency of insights.
- 3.
- Evaluation of various AI blockchain integration techniques prioritizes factors like fidelity, transparency, immutability, robustness, and compactness, providing a nuanced understanding of their performance.
- 4.
- This study underscores blockchain’s pivotal role in fortifying IoT/IIoT security measures by showcasing its efficacy in enhancing intrusion detection performance.
2. Background Study
2.1. Exploring Evolving Opportunities, Trends, and Striking Demands in the Internet of Things
2.2. IoT/IIoT Vulnerabilities and Attacks
- 1.
- Distributed denial-of-service and denial-of-service (DDoS/DoS) attacks deprive authorized users of network resources, primarily targeting availability requirements [4]. In such scenarios, a compromised remote terminal unit (RTU) inundates the master terminal unit (MTU) with arbitrary packets, causing network capacity depletion and hindering the accessibility of resources for legitimate users. The RTU and MTU’s communication ability is interfered with, impeding supervision and process tracking. Low Orbit Ion Cannon (LOIC), Slowloris, Raksmart, Hulk, and Tor’s Hammer are typically used attack tools [24,29,30].
- 2.
- The man-in-the-middle (MiTM) attack intercepts network traffic by infiltrating device communication paths. It is achieved by observing the network, inserting irregularities into transmissions, and relaying them to the intended recipient. Successful execution of this attack hinges on maintaining the session connection while keeping the attacker’s presence concealed, using spoofed IPs to evade detection [28,31]. SSLStrip, Evilgrade, and Ettercap are standard tools that enable MiTM attacks [24,32,33].
- 3.
- 4.
- 5.
- Viruses, Trojan horses, and worms are deployed by attackers post-MitM or masquerade attacks to infect MTUs. These malicious codes grant unauthorized access to the infected system, allowing attackers to launch further assaults or propagate throughout the network, potentially causing system instability or collapse [28].
- 6.
- Fragmentation attacks exploit weaknesses in packet reassembly processes, causing MSU/MTU failure when transmitting oversized data, leading to system collapse [28].
- 7.
- Doorknob rattling involves preparatory actions, such as limited system access attempts, to test security measures’ readiness and responsiveness before an attack [28].
- 8.
- Attacks known as reconnaissance aim to learn more about a network and its hardware characteristics. Guarding sensor readings from the operational procedure is, therefore, essential. Attacks such as response injection introduce deceptive inputs into a control system, prompting control algorithms to make incorrect decisions. In a command injection assault, fictitious control commands infiltrated the control system. Human intervention may cause an improper control action, or bogus commands may be injected and cause RTU and field device register values to be overwritten [24,28,36].
2.3. Overview of Blockchain Technology
2.3.1. Major Blockchain Security Features
- 1.
- Data immutability and integrity: Blockchain’s immutability guarantees that recorded data remain unchangeable without network consensus, making it ideal for securing critical IoT data like sensor readings, supply chain details, and device logs. This feature is crucial for maintaining data integrity, a top priority in IoT systems requiring accurate and unaltered data throughout storage and transmission [2,39].
- 2.
- Decentralization and transparency: Acting as decentralized and distributed ledgers, transactions are recorded across numerous nodes, ensuring no single entity controls the network. The decentralized architecture in IoT devices lessens the dependence on central authorities and promotes transparent and tamper-resistant transactions. It eliminates single points of failure and bolsters system resilience against cyber threats [2,40].
- 3.
- Smart contracts: These self-executing agreements coded on the blockchain automatically execute actions based on conditions, reducing reliance on intermediaries in IoT transactions [41,42]. By automating predefined tasks, such as maintenance alerts or data validation, smart contracts improve efficiency and minimize the need for intermediaries and potential vulnerabilities in IoT transactions [40].
- 4.
- Consensus mechanisms: Consensus mechanisms are sets of rules and protocols used in blockchain networks to achieve agreement among network participants regarding the validity of transactions and the state of the distributed ledger [43]. This ensures that all nodes in the network reach a consensus or joint decision about the current state of the blockchain. Various consensus mechanisms facilitate agreement and trust in decentralized networks by establishing rules for adding new transactions to the blockchain and resolving conflicts among participants [44]. Some of these mechanisms are as follows:
- a.
- b.
- c.
- Delegated proof of stake (DPoS): Uses elected nodes for transaction validation, ensuring high speed and scalability for real-time IoT applications like smart cities.
- d.
- e.
- Practical Byzantine fault tolerance (PBFT): Focuses on low latency and high throughput, making it suitable for financial IoT systems or autonomous vehicles requiring rapid consensus. These mechanisms collectively ensure data integrity, security, and trust in IoT, tailored to specific IoT application needs and constraints [42,43,44].
- 5.
- Identity management and authentication: Blockchain-based identity solutions enable secure and verifiable identity management in IoT to establish trust among themselves, ensuring that only authorized devices participate in the network [45].
- 6.
- Encryption: Transactions and data stored on the blockchain are encrypted using advanced cryptographic algorithms, ensuring that data remain private and secure, and protecting sensitive IoT data against vulnerability [45].
- 7.
- Privacy and confidentiality: Private blockchains provide controlled access to data, guaranteeing confidentiality and making them suitable for scenarios where sensitive information needs secure sharing. IoT leverages private blockchains for securely exchanging critical data, such as patient health records or industrial process data [2,45].
2.3.2. Opportunities and Challenges in Blockchain–IoT Convergence
2.3.3. Trends and Innovations in Blockchain and IoT Convergence
2.3.4. Potential Use and Applications
- 1.
- 2.
- Secure device identity and authentication: Verifies IoT device identities and prevents unauthorized access. Blockchain-based digital certificates uniquely identify devices, with smart contracts enforcing access control. Only authorized devices, validated through cryptographic measures, can interact within the network [21,56,66].
- 3.
- Decentralized access control: Reduces reliance on central authorities and eliminates single points of failure. Blockchain enables decentralized access control through smart contracts, ensuring distributed permissions management. No single entity controls the entire IoT network, enhancing resilience and security [2,21,56].
- 4.
- 5.
- 6.
- Distributed threat intelligence sharing: Collaborates on threat intelligence across IoT networks and facilitates secure sharing of threat data among devices and organizations. Malware signatures, attack patterns, and other threat intelligence can be exchanged, enhancing collective defense mechanisms [2,21,56].
- 7.
- Privacy-preserving data sharing: Enables selective data sharing while protecting privacy, employing privacy-preserving techniques like ZKPs to enable selective data disclosure. Users can share specific data without revealing sensitive information, ensuring privacy while promoting collaboration [2,21,56].
- 8.
- Smart contracts for automated security policies: Automates security policies and responses. Smart contracts execute predefined security rules autonomously. For instance, compromised devices can be automatically isolated from the network, preventing further threats and maintaining network integrity [2,51,56].
2.4. Examining Blockchain’s Progression in the Quantum Age
2.5. Analysis of Survey on Blockchain for Security Concerns of IoT/IIoT
2.6. An Overview of Related Works and Areas for Research
3. Review Methodology
- 1.
- The articles must be original works released as conference proceedings, journals, or arXiv.
- 2.
- The final discussion does not consider background and history; only papers published between 2019 and 2024 are included.
- 3.
- Only articles that discuss the problems and challenges of integrating AI-BLOCKCHAIN for cybersecurity are considered for the qualitative study.
- 4.
- To be eligible for comparison, this review paper must address blockchain and AI integration for IDS and security compared to other recent review works.
- 5.
- English must be used to write all of the papers.
- 6.
- Finally, publications with access restrictions are excluded because the writers could not access the databases.
4. Findings and Discussion
4.1. Role of Blockchain in Enhancing IDS Security
4.2. Security of IoT Devices
4.3. IoT Network Security
4.4. Blockchain Security in IoT
4.5. Blockchain Application in IoT
4.6. Case Study of AI-Blockchain Integration and Result Evaluation
4.7. Practical Implementation and Evaluation Results
4.8. Blockchain-as-a-Service (BaaS): IoT Cybersecurity Perspective
- 1.
- Data integrity and immutability: Providing a tamper-resistant and immutable ledger ensures data integrity stored within IoT networks. Due to the cryptographic links between every transaction on the blockchain and earlier transactions, it is nearly complicated to change past data without the network’s participants’ consent [90].
- 2.
- Secure data exchange: Enables safe, direct, peer-to-peer data transfer between IoT devices [46,91,92]. To reduce the danger of data modification or illegal access, smart contracts and programmable self-executing agreements on the blockchain enable automatic and safe data exchanges based on established conditions [46].
- 3.
- Decentralization and resilience: Its decentralized architecture eliminates single points of failure, enhancing the resilience of IoT networks against cyberattacks. With no central authority controlling the network, blockchain ensures that data remain accessible even in node failures or malicious attacks [46,91,92,93].
- 4.
- Identity and access management: It makes it possible for IoT devices to have strong identification and access control systems, mitigating the risk of unauthorized access and identity spoofing. By confirming the identity of network participants, improves the security of IoT devices using decentralized authentication procedures [67,107].
- 5.
- Audibility and transparency: Real-time auditing of transactions within IoT networks is made possible by the transparent nature of blockchain technology. Data exchange and operations recorded on the blockchain are traceable to their origin, enabling forensic analysis and accountability in case of security breaches [51,89].
4.9. Open Issues and Future Direction
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | artificial intelligence |
ABI | application binary interface |
API | application programming interface |
ANN | artificial neural network |
BaaS | blockchain-as-a-service |
CNN | convolutional neural network |
CRNN | convolutional and recurrent neural network |
DDoS | distributed denial-of-service |
DL | deep learning |
DoS | denial-of-service |
DPoS | delegated proof of stake |
ECC | elliptic curve cryptography |
FL | federated learning |
HMI | human–machine interface |
ICS | industrial control system |
IDE | integrated development environment |
IED | intelligent end device |
IDS | intrusion detection system |
IoT | Internet of Things |
IIoT | Industrial Internet of Things |
IPFS | interplanetary file system |
LOIC | Low Orbit Ion Cannon |
MiTM | man-in-the-middle |
ML | machine learning |
MTU | master terminal unit |
MSU | master station unit |
OTA | over-the-air |
OSI | open systems interconnection |
PBFT | practical Byzantine fault tolerance |
PLC | programmable logic controller |
PoA | proof of authority |
PoS | proof of stake |
PoW | proof of work |
PQC | post-quantum cryptographic |
PRISMA | preferred reporting items for systematic reviews and meta-analyses |
RFID | radio frequency identification |
RNN | recurrent neural network |
RSA | Rivest–Shamir–Adleman |
RSU | remote station unit |
RTU | remote terminal unit |
SCADA | supervisory control and data acquisition |
SDN | software-defined network |
SGX | Software Guard Extensions |
SVM | support vector machine |
TEE | trusted execution environment |
XAI | explainable AI |
ZKP | zero-knowledge proof |
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Author | Year | Systematic Review Methodology | IoT | Blockchain Integration | IDS | Use Case Demonstration |
---|---|---|---|---|---|---|
[75] | 2018 | √ | ||||
[53] | 2019 | √ | ||||
[74] | 2019 | √ | √ | |||
[68] | 2019 | √ | √ | |||
[54] | 2019 | √ | ||||
[76] | 2020 | |||||
[77] | 2020 | √ | √ | √ | ||
[78] | 2020 | √ | √ | |||
[55] | 2021 | √ | √ | |||
[58] | 2021 | √ | ||||
[12] | 2022 | √ | √ | √ | √ | |
[38] | 2022 | √ | √ | |||
[57] | 2022 | √ | ||||
[56] | 2022 | √ | ||||
[52] | 2022 | √ | √ | |||
[65] | 2022 | √ | √ | √ | ||
[79] | 2022 | √ | √ | |||
[21] | 2023 | √ | √ | |||
[80] | 2023 | √ | √ | √ | ||
[59] | 2024 | √ | √ | √ | √ | |
This Study | 2024 | √ | √ | √ | √ | √ |
Database Source | No. of Documents | % Freq |
---|---|---|
IEEE Xplore (Journals) | 30 | 27.03 |
IEEE Xplore (Conferences) | 10 | 9.01 |
MDPI | 18 | 16.22 |
Springer | 18 | 16.22 |
ACM | 3 | 2.70 |
arXiv Pre-print | 2 | 1.80 |
Google Scholar | 5 | 4.50 |
Hindawi | 1 | 0.9 |
Frontiers | 2 | 1.80 |
Taylor & Francis | 1 | 1.09 |
ScienceDirect (Elsevier) | 12 | 10.81 |
Other Sources (Blogs, Reports, and Websites) | 10 | 9.01 |
Total | 111 | 100.00 |
Study | Technique | Focus | Achievement | Year |
---|---|---|---|---|
[85] | Proposed a combination of blockchain and CNN for Software-defined network (SDN)-based IIoT architectures | To detect and prevent security threats in the application and network security layers of the SDN-based IIoT architectures. | Minimized the impact of attacks on SDN-based IIoT architecture layers. | 2023 |
[86] | Creation of an IDS powered by ML algorithms and blockchain to improve the privacy and security of IoT devices. | Aims to encrypt interactions between IoT devices. | Simulation results could improve privacy and security by providing a tamper-proof decentralized communication system. | 2023 |
[87] | Deep learning with blockchain orchestration for safe data transfer in IoT-enabled healthcare systems. | The approach ensures secure data transmission and integrity by exploiting the zero-knowledge proof (ZKP) scheme | Using the Ethereum smart contract to handle data security concerns with the interplanetary file system (IPFS) for off-chain storage to alleviate the problem of data storage costs. | 2023 |
[88] | A hybrid decision tree method | To integrate ML with blockchain for anomaly detection | Predict attack within the shortest time with high detection accuracy. | 2023 |
[89] | A lightweight blockchain security model driven by AI. | To guarantee the security and privacy of cloud-based IIoT systems. | Improved performance in anomaly detection when compared with other models. | 2023 |
[90] | A secure aggregation mechanism for FL based on blockchain | By ensuring secure aggregation, local device data masking stops hostile servers from compromising and reconstructing training data. | The technique minimizes resource waste and quickens the global model’s convergence rate by synchronizing clients with an antiquated model. | 2023 |
[91] | A blockchain network is used in the proposed system for a safe FL model aggregation. | To safely carry out the FL-based aggregation and produce a global model. | According to experimental results, the framework’s processing time was nearly identical to that of the original FL model. | 2023 |
[92] | Multi-signature authentication is used to confirm the integrity of the global ML model and TEE is used to safeguard each client’s local model training. | To give a verifiable ML model and guarantee the participant’s local model training security. | The training on the secure enclave resulted in a slight drop in accuracy, according to the experimental findings. Additionally, multi-signature execution time has no discernible impact on blockchain network speed. | 2023 |
[93] | A blockchain-driven edge intelligence methodology | Incorporates blockchain based on a reputation for decentralized transaction recording and verification, guaranteeing privacy and data protection. | The simulation findings validate the approach’s efficiency and robustness over state-of-the-art cyberattack detection methods. | 2022 |
[94] | A security architecture that combines SDN and blockchain technology. | To defend industrial control processes from counterfeit commands and stop misrouting attacks on OpenFlow rules in industrial IoT systems with SDN enabled. | The assessment’s findings confirm the suggested security measures’ effectiveness and efficiency. | 2019 |
Edge-IIoT Dataset | IoT Network Intrusion Dataset | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Local Model | Global Model | Local Model | Global Model | |||||||||
Clients | Accuracy (%) | Loss | Train Time (s) | Accuracy (%) | Loss | Aggregation Time (s) | Accuracy (%) | Loss | Train Time (s) | Accuracy (%) | Loss | Aggregation Time (s) |
1 | 81.72 | 0.7359 | 580 | 84.80 | 0.5895 | 501 | 94.11 | 0.2205 | 486 | 95.74 | 0.1551 | 401 |
2 | 82.59 | 0.7538 | 572 | 85.09 | 0.5892 | 555 | 93.88 | 0.1685 | 475 | 95.68 | 0.1579 | 445 |
3 | 80.69 | 0.7475 | 563 | 84.77 | 0.5873 | 564 | 93.82 | 0.2270 | 464 | 95.47 | 0.1638 | 456 |
4 | 79.60 | 0.7619 | 420 | 84.97 | 0.5871 | 599 | 94.03 | 0.2215 | 388 | 95.57 | 0.1603 | 548 |
5 | 82.79 | 0.7326 | 389 | 84.80 | 0.5990 | 530 | 93.84 | 0.2311 | 400 | 95.94 | 0.1568 | 509 |
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Ahakonye, L.A.C.; Nwakanma, C.I.; Kim, D.-S. Tides of Blockchain in IoT Cybersecurity. Sensors 2024, 24, 3111. https://doi.org/10.3390/s24103111
Ahakonye LAC, Nwakanma CI, Kim D-S. Tides of Blockchain in IoT Cybersecurity. Sensors. 2024; 24(10):3111. https://doi.org/10.3390/s24103111
Chicago/Turabian StyleAhakonye, Love Allen Chijioke, Cosmas Ifeanyi Nwakanma, and Dong-Seong Kim. 2024. "Tides of Blockchain in IoT Cybersecurity" Sensors 24, no. 10: 3111. https://doi.org/10.3390/s24103111
APA StyleAhakonye, L. A. C., Nwakanma, C. I., & Kim, D.-S. (2024). Tides of Blockchain in IoT Cybersecurity. Sensors, 24(10), 3111. https://doi.org/10.3390/s24103111