## 4 Hardware Security
### Physical Unclonable Functions (PUFs)
#### 4.1.2 Application of PUFs  
Physical unclonable functions (PUFs) are used for authentication and secret key storage without needing secure EEPROMs and other expensive hardware. [PERSON] et al. have proposed a Lattice PUF against ML attacks that leverages the Learning With Errors (LWE) cryptographic problem. The designers proposed to build a pseudo-random number generator that integrates a Physically Obfuscated Key (POK) with a LWE decryption function and a linear-feedback shift register (LFSR) [234]. ML capabilities are utilized in the screening of stable challenges to strong PUFs. Initially, randomly generated challenges tested for stability are chosen as the input and output of the ML model for extracting a stable challenge dataset [235]. [PERSON] et al. have proposed a lightweight feedback-based anti-ML-attack Physically Unclonable Function (FLAM-PUF) that integrates an arbiter PUF, a Galois LFSR, and basic logic gates [236]. The design employs a 1-bit feedback mechanism to disrupt the training data, increasing complexity and randomness in the CRP set. This obfuscation reduces the CRP correlation and strengthens resistance to ML attacks by introducing non-linear relationships. The researchers reported a 50% prediction accuracy against various ML algorithms, including Support Vector Machines (SVMs), Logistic Regression, and Deep Neural Networks (DNNs). A comparable design approach utilizing an LFSR and an Arbiter PUF (APUF) is introduced in [237]. A delay difference quantization strategy for Arbiter PUF (DDQ-APUF) is proposed in [238], which employs multiple configurable delay units (\(\Delta\)) along two symmetrical signal transmission paths. The design measures and quantifies the delay difference between these two paths. A configurable delay is introduced along the signal path and gradually increases until the output response of the APUF flips. This quantified delay difference is then used as the PUF response, providing robustness against environmental variations. This design follows the Strict Avalanche Criterion (SAC), ensuring that even a minor alteration in the challenge inputs results in significant and random response changes. [PERSON] et al. have proposed a dynamically configured hybrid (DCH) PUF by combining the Self-XOR (SX) PUF with a Modified Feed-Forward (MFF) \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**PUF Types** & **Merits** & **Demerits** \\ \hline \multirow{6}{*}{} & _Large CRP Space_: A strong PUF generates a large pool of CRPs, hence suitable for authentication tasks, which might lead to frequent authentication between edge devices and servers or peers without repeating challenges [232], thus eliminating the need to store a secret key, as the PUF itself serves as the authentication mechanism. & _Higher Resource Requirements_: Strong PUFs often utilize complex circuits, such as delay lines in APUFs, that require additional area and power needs further straining the limited resources of edge devices. & _Susceptibility to Modeling Attacks_: The large number of CRPs from strong PUFs are normally exposed during authentication stages, making them vulnerable to adversaries gaining access to CRPs and able to model the PUF’s behavior using ML, compromising its security [233]. Susceptibility to CRP leakage via communication channels or direct interfacing requires additional protection like tamper detection, which may be costly. & _Power and Complexity Trade-Off_: A huge number of CRPs consume more energy and require more sophisticated hardware, which may not align with the constraints of low-end edge devices. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._PUF. An LFSR is used as a configuration generator, independent of the input challenge. DCH PUF has proven its resilience against diverse ML attacks, including Deep Neural Networks (DNNs), Logistic Regression (LR), and covariance matrix adaptation evolution strategy (CMA-ES) [239]. [PERSON] et al.