# FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication
## 2 Related Work
[21] introduced a framework that leverages the combination of RF-PUF and random forest classification for authenticating IoT nodes. Furthermore, [PERSON] et al. [42] presented a deep neural network-based framework for real-time authentication of wireless nodes using PUFs and RF fingerprinting. The framework detects process variation-induced effects on RF properties of wireless transmitters using in situ machine learning at the receiver end. Also, [PERSON] et al. [43] introduced a secure authentication method that utilizes the RF-PUF for both static and quasi-static channels. The proposed method aims to ensure reliable and efficient authentication by exploiting the inherent randomness of the RF-PUF. The aforementioned approaches have shown to be effective in accurately identifying IoT nodes. However, these approaches are susceptible to modeling attacks and do not account for the impact of noise on biometric data, which can cause authentication failure. Addressing the issue of noise in biometric data is critical to enhancing the security of authentication systems. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Ref.** & **Key Feature** & **Advantage** & **Disadvantage** \\ \hline
[25] & PUF-based authentication & Enables mutual authentication between IoT devices & Relies on an intermediary server to store CRPs and generate secure tokens \\ \hline
[26] & PUF-based authentication & No stored explicit CRPs in the verifier database & Introduces additional complexity due to encrypted CRPs \\ \hline
[27] & PUF-based authentication & Develops a security solution to address counterfeit products & Utilizes Rabin public-key encryption \\ \hline
[28] & PUF-based authentication & No partial/full CRPs are stored and require an intermediary server \\ \hline
[29] & Authentication using PUFs and blockchain & Utilizes blockchain smart contracts & Computationally heavy due to the use of Diffie–Hellman protocol \\ \hline
[30] & Multifactor authentication using PUFs & Suitable for telehealth system for mobile and IoT edge devices & Relies on user passwords during the authentication \\ \hline
[31] & PUF-based authentication and key generation & Use simple cryptographic primitives & Vulnerable to machine learning attacks. \\ \hline
[32] & PUF-based authentication & No explicit CRP storage in the verifier database & Overhead of cryptographic operations (geometric threshold secret sharing) \\ \hline
[33] & PUF-based authentication & Applies challenge obfusc and uses a subset of response bits & Susceptible to impersonation attacks due to CRP disclosure \\ \hline
[34] & PUF-based authentication & Obfuscates challenge bit-strings to counter modeling attacks & Use of hash functions introduces a significant computational overhead \\ \hline
[35] & PUF-based authentication & Relies on secret sharing to minimize exposure of raw PUF responses & Susceptible to impersonation attacks due to increased CRP disclosure \\ \hline
[13] & PUF-based mutual authentication & Mitigates modeling attacks using unique binary operations & Attacker can access challenge bits; dependant on secrecy of binary operations \\ \hline
[36] & Three-factor authentication & Combines passwords, biometrics, and PUFs without storing node IDs & Introduces a significant computational overhead and requires the involvement of a server for mutual authentication \\ \hline
[37] & PUF-based authentication & Integrates secret sharing with PUF for authentication & Introduces computational overhead and requires the involvement of a server for mutual authentication \\ \hline
[38; 39; 40] & IoT authentication using RF fingerprinting & Utilizes unique characteristics of wireless signals and avoids complex cryptography & Vulnerable to adversarial attacks \\ \hline
[21; 42; 43] & Two-factor authentication & Leverages RFFs and PUF for authenticating wireless nodes & Susceptible to modeling attacks; no mitigation of the noise impact on biometric data \\ \hline \end{tabular}
\end{table}