OVERVIEW OF MODERN AUTHENTICATION METHODS FOR MICROCONTROLLERS
DOI:
https://doi.org/10.28925/2663-4023.2024.25.200214Keywords:
cyber security; vulnerabilities; microcontrollers; authentication of microcontrollersAbstract
The paper is devoted to the study of modern authentication methods for microcontrollers, which play a crucial role in today's technological landscape. Microcontrollers serve as the foundation for most embedded devices used in various sectors, including consumer electronics, automotive systems, industrial equipment, and medical devices. They perform essential functions related to the control, monitoring, and management of numerous processes and systems. Given the widespread adoption of microcontrollers in critical infrastructures, ensuring their security has become a top priority. Authentication of microcontrollers is vital for preventing unauthorized access and cyberattacks, which could lead to serious consequences such as data breaches, system control, or failures in critical services. The paper examines the significance of microcontroller security in modern technologies and explores the potential risks arising from the use of unsecured microcontrollers. It also analyzes the state-of-the-art authentication methods used to protect microcontrollers. Special attention is given to comparing different approaches to authentication, which include both traditional and novel methods based on cryptography, physically unclonable functions (PUF), and biometrics. For each method, the paper outlines its advantages, disadvantages, and application areas, along with an assessment of their effectiveness in various security scenarios. Furthermore, the paper presents the results of practical implementation of some authentication methods in real-world examples, which demonstrate their viability and effectiveness in securing modern systems. The authors also suggest future research directions in this field, particularly the development of new authentication methods that combine high reliability with ease of implementation in the context of rapidly evolving technologies and cyber threats.
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References
Kleber, S. (2015). Secure Execution Architecture based on PUF-driven Instruction Level Code Encryption: preprint. Cryptology ePrint Archive.
Khalil, D. (2019). A Comparison Survey Study on RFID Based Anti-Counterfeiting Systems. J. Sens. Actuator Netw. 8(3), 37. https://doi.org/10.3390/jsan8030037
Karimian, N. (2019). DRAMNet: Authentication based on Physical Unique Features of DRAM Using Deep Convolutional Neural Networks. https://doi.org/10.48550/arXiv.1902.09094
Yang, D. (2019). Security and Accuracy of Fingerprint-Based Biometrics: A Review. Symmetry, 11(2), 141. https://doi.org/10.3390/sym11020141
Shamsoshoara, A., Korenda, A., Fatemeh, A., & Sherali, Z. (2020). A survey on physical unclonable function (PUF)-based security solutions for Internet of Things. Comp. Netw. 183. https://doi.org/10.1016/j.comnet.2020.107593
Nadimi Goki, P., Civelli, S., Parente, E. (2023). Optical identification using physical unclonable functions. https://doi.org/10.48550/arXiv.2305.02141
Shamala, L., Zayaraz, D., Vivekanandan, D. (2021). Lightweight Cryptography Algorithms for Internet of Things enabled Networks: An Overview. Journal of Physics: Conference Series, 1717. https://doi.org/10.1088/1742-6596/1717/1/012072
van de Meent, T. A. (2022). Comparative Study on Lightweight Authentication Protocols in IoT context. https://essay.utwente.nl/89452/1/van_de_Meent_BA_EEMCS.pdf.
Papathanasaki, M. (2022). Modern Authentication Methods: A Comprehensive Survey. AI, Computer Science and Robotics Technology. https://doi.org/10.5772/acrt.08
Yang, A. (2022). A Lightweight and Practical Anonymous Authentication Protocol Based on Bit-Self-Test PUF. Electronics, 11(5), 772. https://doi.org/10.3390/electronics11050772
Meixner, A. (2023). Fingerprinting Chips For Traceability. https://semiengineering.com/fingerprinting-chips-for-traceability/
Mahadeen, M. (2023). Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data. Sustainability, 15(13), 10456. https://doi.org/10.3390/su151310456
Gupta, C. (2024). A Lightweight and Secure PUF-Based Authentication and Key-exchange Protocol for IoT Devices. https://doi.org/10.21203/rs.3.rs-3850019/v1
Xi, D. (2024). Device Identity Recognition Based on an Adaptive Environment for Intrinsic Security Fingerprints. Electronics, 13(3), 656. https://doi.org/10.3390/electronics13030656
Nie, S., Liu, L., & Du, Y., (2017). Free-fall: Hacking tesla from wireless to can bus.
Gassend, B., Clarke, D., van Dijk, M., & Devadas, S., (2002). Silicon physical random functions, Proceedings of the 9th ACM Conference on Computer and Communications Security, 148–160. https://doi.org/10.1145/586110.586132
Anagnostopoulos, N. A., Katzenbeisser, S., Chandy, J., & Tehranipoor, F., (2018). An overview of dram-based security primitives. Cryptography, 2(2).
Tehranipoor, F., Karimian, N., Yan, W., & Chandy, J. A., (2017). Dram-based intrinsic physically unclonable functions for system-level security and authentication. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25, 1085–1097.
Tehranipoor, F., Yan, W., & Chandy, J. A. (2016). Robust hardware true random number generators using dram remanence effects. IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 79–84.
Schaller, A., Xiong, W., Anagnostopoulos, N. A., Saleem, M. U., Gabmeyer, S., Skoric, B., Katzenbeisser, S., & Szefer, J., (2018). Decay-based dram pufs in commodity devices. IEEE Transactions on Dependable and Secure Computing.
Talukder, B. M. S. B., Ray, B., Tehranipoor, M., Forte, D., & Rahman, M. T. (2018). LDPUF: exploiting DRAM latency variations to generate robust device signatures. arXiv preprint. https://doi.org/10.48550/arXiv.1808.02584
Kim, J. S., Patel, M., Hassan, H., & Mutlu, O. (2018). The dram latency puf: Quickly evaluating physical unclonable functions by exploiting the latency-reliability tradeoff in modern commodity dram devices. IEEE International Symposium on High Performance Computer Architecture (HPCA), 194–207.
Anagnostopoulos, N. A., Arul, T., Fan, Y., Hatzfeld, C., Schaller, A., Xiong, W., Jain, M., Saleem, M. U., Lotichius, J., Gabmeyer, S., Szefer, J., & Katzenbeisser, S. (2018). Intrinsic run-time row hammer pufs: Leveraging the row hammer effect for run-time cryptography and improved security. Cryptography, 2(3).
Ruhrmair, U., Sehnke, F., Zolter, J. S., Dror, G., Devadas, S., & Schmidhuber, J. (2010). Modeling attacks on physical unclonable functions. Proceedings of the 17th ACM Conference on Computer and Communications Security, CCS ‘10, 237–249.
Rhrmair, U., Slter, J., Sehnke, F., Xu, X., Mahmoud, A., Stoyanova, V., Dror, G., Schmidhuber, J., Burleson, W., & Devadas, S., (2013). Puf modeling attacks on simulated and silicon data. IEEE Transactions on Information Forensics and Security, 8, 1876–1891.
Ganji, F., Tajik, S., Faßler, F., & Seifert, J.-P. (2016). Strong machine learning attack against pufs with no mathematical model. Cryptographic Hardware and Embedded Systems – CHES 2016, 391–411.
Herder, C., Yu, M., Koushanfar, F., & Devadas, S. (2014). Physical unclonable functions and applications: A tutorial. Proceedings of the IEEE, 102, 1126–1141.
Yu, M.-D. M., M’Raihi, D., Sowell, R., & Devadas, S. (2011). Lightweight and secure puf key storage using limits of machine learning. Cryptographic Hardware and Embedded Systems – CHES 2011, 358–373.
Paral, Z., & Devadas, S., (2011). Reliable and efficient puf-based key generation using pattern matching. IEEE International Symposium on Hardware-Oriented Security and Trust, 128–133.
Addabbo, T., Fort, A., Marco, M. D., Pancioni, L., Vignoli, V., (2013). Physically unclonable functions derived from cellular neural networks. IEEE Transactions on Circuits and Systems I: Regular Papers, 60, 3205–3214.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Tehranipoor, F., Karimian, N., Yan, W., & Chandy, J. A. (2017). Investigation of dram pufs reliability under device accelerated aging effects. IEEE International Symposium on Circuits and Systems (ISCAS), 1–4.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929–1958.
Kushner, H. J., & Yin, G. G. (2003). Stochastic Approximation and Recursive Algorithms and Applications. Stochastic Modelling and Applied Probability. Springer Science & Business Media, 35.
Kingma, D. P., Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint. https://doi.org/10.48550/arXiv.1412.6980
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, 1, 1097–1105.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint. https://doi.org/10.48550/arXiv.1409.1556
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