| Title | Mc-PUF: Memory-based and Machine Learning Resilient Strong PUF for Device Authentication in Internet of Things |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Williams, Phillip, Idriss, Haytham, Bayoumi, Magdy |
| Conference Name | 2021 IEEE International Conference on Cyber Security and Resilience (CSR) |
| Keywords | authentication, Constrained Devices, Germanium, machine learning, Mc-PUF, Measurement, modeling attack, physical unclonable function, Predictive models, pubcrawl, Random access memory, Resiliency, resilient, Resilient Security Architectures, security |
| Abstract | Physically Unclonable Functions (PUFs) are hardware-based security primitives that utilize manufacturing process variations to realize binary keys (Weak PUFs) or binary functions (Strong PUFs). This primitive is desirable for key generation and authentication in constrained devices, due to its low power and low area overhead. However, in recent years many research papers are focused on the vulnerability of PUFs to modeling attacks. This attack is possible because the PUFs challenge and response exchanges are usually transmitted over communication channel without encryption. Thus, an attacker can collect challenge-response pairs and use it as input into a learning algorithm, to create a model that can predict responses given new challenges. In this paper we introduce a serial and a parallel novel 64-bits memory-based controlled PUF (Mc-PUF) architecture for device authentication that has high uniqueness and randomness, reliable, and resilient against modeling attacks. These architectures generate a response by utilizing bits extracted from the fingerprint of a synchronous random-access memory (SRAM) PUF with a control logic. The synthesis of the serial architecture yielded an area of 1.136K GE, while the parallel architecture was 3.013K GE. The best prediction accuracy obtained from the modeling attack was 50%, which prevents an attacker from accurately predicting responses to future challenges. We also showcase the scalability of the design through XOR-ing several Mc-PUFs, further improving upon its security and performance. The remainder of the paper presents the proposed architectures, along with their hardware implementations, area and power consumption, and security resilience against modeling attacks. The 3-XOR Mc-PUF had the greatest overhead, but it produced the best randomness, uniqueness, and resilience against modeling attacks. |
| DOI | 10.1109/CSR51186.2021.9527930 |
| Citation Key | williams_mc-puf_2021 |
Mc-PUF: Memory-based and Machine Learning Resilient Strong PUF for Device Authentication in Internet of Things