# FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication
## 4 System Model and Approach Overview
### Approach Overview  
Our proposed protocol supports mutual authentication for IoT nodes. Two hardware primitives, namely, RFF and PUFs, are employed in the authentication process. Recall that  
Figure 2: The fuzzy extractor diagram based on the syndrome (\(N=63\) bits).  
the biometric data are subjected to high noise levels, which subsequently could result in the corruption of the authentication token and rejecting connection requests from legitimate nodes. Therefore, we consider mitigating the noise in the utilized biometric to enhance the authentication efficiency by using the fuzzy extractor. Mainly, FeRHA aims to achieve the following design objectives:  
1. Develop a novel lightweight PUF-based mechanism for authenticating IoT devices. Rather than applying encryption, our mechanism pursues two-factor authentication by combining the benefits of PUFs and RF fingerprinting.
2. Given the dynamic nature of IoT systems, intermediaries should be avoided, and authentication should be conducted in a decentralized manner.
3. The system should resist attempts by adversaries to eavesdrop and gain knowledge of many CRPs of the embedded PUF to model it using machine learning techniques.
4. To ensure accurate authentication, it is essential to mitigate the noise in biometric data.  
The involvement of PUFs enables the concealment of device secrets and mitigates the threat of device hacking. To satisfy the above design goals, FeRHA adopts a distributed authentication strategy in which the network operation is not dependent on a trusted server. Only device enrollment could benefit from engaging a server. In contrast to existing PUF-based solutions, FeRHA does not retain CRPs of a node during the enrollment phase, nor does it incorporate a cryptosystem [26; 61]. To elaborate, during the enrollment phase, an IoT node obtains a number of challenge bit-strings and their associated authentication tokens for every other node in the network. Specifically, a node, \(\delta_{y}\), will obtain a set, \(\gamma_{x\to y}\),\(\forall x\
eq y\), of challenges and tokens for each node, \(\delta_{x}\), as shown in Figure 3. Thus, in the event that \(\gamma_{x\to y}\) is compromised by an eavesdropper, any attempts to replicate the PUF of \(\delta_{x}\) would fail. Additionally, the verifier, \(\delta_{y}\), will factor in the physical layer features of the \(\delta_{x}\)'s transmitter. In this regard, IQ is considered as the fundamental attribute for \(RFF\) extraction, although the selection of any other feature can be made, based on the network environment. Then, the prover, \(\delta_{x}\), will utilize the fuzzy extractor to generate the helper string, \(\mathcal{H}\), for PUFs and RFF, s.t. \(\Pi(R_{i}^{x})=\mathcal{H}_{R_{i}^{x}}\), \(\forall i\in\gamma_{i\to j}\), and \(\Pi(RFF_{x})=\mathcal{H}_{RFF_{i}}\); where \(\Pi(.)\) denotes the fuzzy extractor function, and \(\mathcal{H}_{R_{i}^{x}}\) and \(\mathcal{H}_{RFF_{x}}\) are the helper strings for \(R_{i}^{x}\) and \(RFF_{x}\), respectively. The FeRHA protocol is discussed in detail in the next section.  
Figure 3: Illustrative diagram of FeRHA during the enrollment phase.