Submitted:
01 July 2024
Posted:
01 July 2024
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Abstract
Keywords:
1. Introduction
2. Emerging Authentication Technologies for Zero Trust on the IoT
- 1)
- Every data source and computing service is regarded as a corporate asset that requires protection.
- 2)
- All communications are considered insecure, regardless of the network location specified in the access request. No entity seeking access is automatically trusted.
- 3)
- Resource access is granted on a session-by-session basis.
- 4)
- Device characteristics, along with behavioral and environmental factors, are considered in access decisions.
- 5)
- The principle of least privilege is applied.
- 6)
- Access is granted intermittently, not automatically.
- 7)
- Enhancements to security in communications, network infrastructure, and assets are continuously applied.
3. The Performance Evaluation Criteria for Authentication Zero Trust in the IoT
4. Future Direction and Suggestions
5. Conclusion
References
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Chanapha Bast graduated with a Ph.D. in Information Management from National Dong Hua University, Taiwan, in 2020. She earned her Master of Information Technology and Computer in 2007 from King Mongkut’s University of Technology Thonburi in Bangkok, Thailand. Currently, she serves as a lecturer in the Business Computer Department at the Management Science Faculty of Udon Thani Rajabhat University, Thailand. Her primary research interests include IoT, security, privacy, cybersecurity, network security, and cloud computing. Additionally, she has conducted significant research in blockchain technology. Her contributions have been widely published in various peer-reviewed journals and conference proceedings, underscoring her expertise and commitment to advancing knowledge in these critical areas. |
Kuo-Hui Yeh (SM’16) serves as a professor at the Institute of Artificial Intelligence Innovation, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. Prior to this appointment, he was a professor in the Department of Information Management at National Dong Hwa University, Hualien, Taiwan, from February 2012 to January 2024. Dr. Yeh earned his M.S. and Ph.D. degrees in Information Management from the National Taiwan University of Science and Technology, Taipei, Taiwan, in 2005 and 2010, respectively. He has contributed over 150 articles to esteemed journals and conferences, covering a wide array of research interests such as IoT security, Blockchain, NFC/RFID security, authentication, digital signatures, data privacy and network security. Furthermore, Dr. Yeh plays a pivotal role in the academic community, serving as an Associate Editor (or Editorial Board Member) for several journals, including the Journal of Information Security and Applications (JISA), Human-centric Computing and Information Sciences (HCIS), Symmetry, Journal of Internet Technology (JIT) and CMC-Computers, Materials & Continua. In the professional realm, Dr. Yeh is recognized as a Senior Member of IEEE and holds memberships with (ISC)², ISA, ISACA, CAA, and CCISA. His professional qualifications include certifications like CISSP, CISM, Security+, ISO 27001/27701/42001 Lead Auditor, IEC 62443-2-1 Lead Auditor, and ISA/IEC 62443 Cybersecurity Expert, covering fundamentals, risk assessment, design, and maintenance specialties. |



| Author(s) | Implementation | Authentication Technology | Advantages/ Challenges |
|---|---|---|---|
| [3] (2023) |
Using factors as PIN password, verification, facial recognition. | Biometric Authentication |
• Strong authentication based on unique biological or behavioral traits. • Enhances user for memorizing passwords or token. • Potential privacy concerns. • Increasing device costs. |
| [9] (2021) |
Identifying device fingerprints authentication for users and providing entry-point security, identity the subject, and accessing the device. | ||
| [26] (2019) |
Specifying biometric scanner collects unique biological data from users and matches the stored data. | ||
| [5] (2020) |
Using for verification and calculating the discrete algorithm problem. | Cryptographic Authentication |
• Offers mathematically security. • Supports secure key exchange. • Non-repudiation, data integrity, and cryptographic primitives. • Computationally intensive for constrained IoT devices. • Requires secure communication channels for key exchange. |
| [10] (2024) |
Taking uniqueness and persistence of physiological characteristics and user identity. | ||
| [21] (2022) |
Protecting the system by manufacturers. | ||
| [25] (2023) |
Securing protocols for data storage and transmission and using to the confidentiality, integrity, and availability. | ||
| [26] (2019) |
Using to morph actual messages during communication in an insecure network. | ||
| [27] (2023) |
Offering higher confidence over authenticator and verifying an authentication key. | ||
| [28] (2019) |
Using as a key requirement within the scheme to fulfil the cryptographic checksum needed. | ||
| [6] (2024) |
Ensuring only authorized users, devices, and applications can access networks. | Federated Identity and Access Management (FIdAM) |
• Enables seamless authentication across multiple IoT. • Reduce complexity • Supports various authentication methods. • Required trust establishment. |
| [11] (2023) |
Providing the requested security services, access control, and management of configuration updates. | ||
| [29] (2023) |
Adding security for all resource authentication and authorization and strictly enforced. | ||
| Author(s) | Implementation | Authentication Technology |
Advantages/ Challenges |
| [1] (2020) |
Proposing an anonymous access system and computing the divided identity block data. | Blockchain-based Authentication |
• Provide a decentralized. • Enables secure and editable identity management. • Supports distributed trust. • Scalability limitations. • Requires design and implementation to ensure privacy and security. |
| [10] (2024) |
Presenting blockchain technology for zero trust networks and comparing techniques used by different platforms. Presenting a possible approach for trusted transactions. | ||
| [11] (2023) |
Securing data storage, sensitive data and distributing data across multiple nodes. | ||
| [25] (2024) |
Protecting sensitive information and combining blockchain and trust assessment. | ||
| [27] (2023) |
Presenting blockchain technology with zero trust. | ||
| [28] (2019) |
Presenting the potential to increase privacy and security in blockchain applications. | ||
| [9] (2021) |
Designing characteristics that are used in one piece of hardware. | PUFs, or physically unclonable functions | • Unique physical properties. • Offers tamper resistance and counterfeiting capacities. |
| [20] (2024) |
Linking between malware on Android devices and zero trust security. |
| Feature | Mutual Authentication |
Cryptographic Authentication |
Multi-Factor Authentication (MFA) |
Blockchain-based Authentication |
Ref. |
|---|---|---|---|---|---|
| Security | High | Moderate | High | High | [25,30,31] |
| Processing | Low, Moderate | Low | Low, Moderate | Moderate, High | [32,33] |
| Scalability | Moderate | High | High | Scalability limitations | [28,29,34,35] |
| Resource | Moderate | Low | Low, Moderate | Moderate, High | [30,36] |
| Complexity | Moderate | Low | Moderate | High | [21,29,34] |
| Suitability | Moderate | High | Moderate | Limited | [2,28,31,37] |
| Evaluation Features | Benefits | Ref. |
|---|---|---|
| IoT Device Identification | • Identify all IoT devices connected to the networks. • Vulnerabilities security. |
[5,24,32,38] |
| Network Segmentation | • Analyze network segmentation. | [7,10,39] |
| Access Management | • Determine authentication methods. • Vulnerability to unauthorized access. |
[11,29] |
| Data Encryption | • Assess the encryption protocols. • Use data transmission within IoT environment. |
[5,14] |
| Threat Detection | • Evaluate mechanisms and analyze effectiveness. | [20,30] |
| Penetration Testing | • Conduct hacking and simulated attacks to assess the authentication mechanisms. • Test against various attacks (spoofing, man in the middle). • Identify and exploit potential vulnerabilities. |
[2,10,29,31,40,41] |
| Formal Verification | • Use techniques and mathematical models to confirm the security characteristics of cryptography and authentication. • Ensure established security guarantees. |
[5,12,39] |
| Simulation and emulation | • Simulate the IoT and authentication to evaluate performance and scalability. • Identify resource constraints and interoperability. |
[29,33] |
| Compliance Testing | • Assess authenticity to security standards. • Ensure compliance with legal data protection and data privacy. |
[10,23,27] |
|
Evaluation/ implementation |
[11] | [12] | [15] | [20] | [24] | [34] | [36] | [38] | [42] | [43] | [44] |
| Stringent access controls | ✗ | ✗ | ✗ | ✓ | ✓ | ✗ | ✗ | ✓ | ✓ | ✓ | ✗ |
| Micro-segmentation strategies | ✓ | ✗ | ✓ | ✗ | ✓ | ✗ | ✓ | ✓ | ✓ | ✗ | ✓ |
| Threat detection systems | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ |
| Data Encryption | ✗ | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Zero Trust policies across the IoT environment | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✓ |
| Integrating IoT security | ✗ | ✓ | ✗ | ✓ | ✓ | ✗ | ✓ | ✓ | ✗ | ✗ | ✗ |
| Developing IoT environments under Zero Trust principles | ✓ | ✗ | ✓ | ✓ | ✗ | ✗ | ✗ | ✓ | ✗ | ✓ | ✓ |
| Future Directions | Suggestions |
|---|---|
| Continuous Adaptive Risk and Trust Assessment (CARTA) | CARTA frameworks are endlessly adapted based on context, behavior, and risk factors for IoT devices [50,51,52,53,54]. |
| Blockchain-based Authentication | Develop blockchain technologies for decentralized, tamper-proof authentication and access control in IoT ecosystems. This technology addresses scalability limitations on a large scale [55,56]. |
| Quantum-resistant cryptography | Investigate quantum-resistant cryptographic algorithms as the protocols to secure IoT authentication against and prepare for potential vulnerabilities from future quantum computing threats[57]. |
| Federated Identity and Access Management (FedIAM) | Implement FedIAM solutions to enable secure and seamless authentication across multiple IoT service providers. This reference implements a standardized authentication protocol to facilitate it across IoT platforms[51,52]. |
| Integration of AI and Machine Learning (ML) | Develop AI/ML techniques for real-time anomaly detection and adaptive authentication mechanisms in IoT environments. To analyze user and device patterns in real-time, dynamically adjust access control policies based on risk assessment[54,58]. |
| Privacy-preserving Authentication | Explore authentication schemes that protect user privacy and sensitive data in the IoT environment. Develop for user control and usage associated with authentication[53,59]. |
| Secure Firmware and Hardware Roots of Trust | Integrate secure hardware and trusted execution environments for IoT device authentication and integrity[60,61]. |
| Future Directions | Suggestions |
| Standardization of Authentication Protocols for Interoperability | Participate in standards and ensure interoperability among different IoT authentication solutions. Develop open-source libraries and references for standardized authentication protocols[51,56]. |
| User-centric Authentication for Improve Usability | Discover user-centric authentication, user preferences, user-friendly interfaces, and workflows such as biometrics and behavioral patterns for secure and convenient IoT access control in an IoT environment[52]. |
| Emerging Technologies | Investigate the security implications of emerging technologies and networks on authentication processes within the IoT environment[57,59,62]. |
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