Hardware Security For and Beyond CMOS Technology: An Overview on Fundamentals, Applications, and Challenges <s> 4.2.5 <s> We discuss numerical modeling attacks on several proposed strong physical unclonable functions (PUFs). Given a set of challenge-response pairs (CRPs) of a Strong PUF, the goal of our attacks is to construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. If successful, this algorithm can subsequently impersonate the Strong PUF, and can be cloned and distributed arbitrarily. It breaks the security of any applications that rest on the Strong PUF's unpredictability and physical unclonability. Our method is less relevant for other PUF types such as Weak PUFs. The Strong PUFs that we could attack successfully include standard Arbiter PUFs of essentially arbitrary sizes, and XOR Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs up to certain sizes and complexities. We also investigate the hardness of certain Ring Oscillator PUF architectures in typical Strong PUF applications. Our attacks are based upon various machine learning techniques, including a specially tailored variant of logistic regression and evolution strategies. Our results are mostly obtained on CRPs from numerical simulations that use established digital models of the respective PUFs. For a subset of the considered PUFs-namely standard Arbiter PUFs and XOR Arbiter PUFs-we also lead proofs of concept on silicon data from both FPGAs and ASICs. Over four million silicon CRPs are used in this process. The performance on silicon CRPs is very close to simulated CRPs, confirming a conjecture from earlier versions of this work. Our findings lead to new design requirements for secure electrical Strong PUFs, and will be useful to PUF designers and attackers alike. <s> BIB001 </s> Hardware Security For and Beyond CMOS Technology: An Overview on Fundamentals, Applications, and Challenges <s> 4.2.5 <s> Physically Unclonable Functions (PUFs) combat counterfeit ICs by identifying each chip using inherent process variation. PUFs must produce sufficiently many bits, but replicating the same PUF design requires care since process variation and its spatial correlation may change in the next 10 years. Additional challenges arise in system-on-chip and heterogeneous 3D integration of diverse PUFs. Responding to these challenges, we introduce methods for combining PUFs, with provisions for sampling process variation throughout the IC. When multiple sources of entropy are available, our optimization algorithms select sources to maximize joint entropy and minimize physical overhead. Empirical validation uses SPICE simulations for a 45nm technology node. <s> BIB002 </s> Hardware Security For and Beyond CMOS Technology: An Overview on Fundamentals, Applications, and Challenges <s> 4.2.5 <s> Severe security threats and alerts associated with the use of smart devices have drawn increasing public attentions since the inception of Internet of Things (IoT) in late 1990s. IoT devices pose a unique and challenging scenario for hardware security because of their ubiquity and area-power cost constraints. Traditional software techniques and established cryptographic methods are either inadequate or impractical due to the computational capacity and permanent storage required to process and maintain the privacy of the secret key. In this light, Physical Unclonable Function (PUF), a burgeoning technology rooted in 2002, comes in handy as an inexpensive and yet effective security primitive to overcome the forgery tagging problem by its radically different way of generating and processing secret keys in security hardware. PUFs are hardware structures or functions designed to utilize the physical disorder of random nanoscale phenomena for the derivation of keys without having to keep any security-critical information explicitly in hardware. As we usher PUF into its 15th anniversary in 2017, it is timely to review the advancements of PUF over the past decade. Specifically, this survey addresses three fundamental questions which are at all times relevant in the security arms race. These questions are: how secure can a PUF be? what differences have PUFs brought to security applications and how do these differences impact existing security protocols? how is hardware implementation research influenced by the opportunities of nanotechnologies and new discoveries of disorder-based physical phenomena? It is hoped that this retrospective study of problems and solutions encountered in the journey of developing PUF to its current state will foreshadow the challenges and opportunities for future sustainable activities in the field. <s> BIB003 </s> Hardware Security For and Beyond CMOS Technology: An Overview on Fundamentals, Applications, and Challenges <s> 4.2.5 <s> The promise of strong physical unclonable functions (PUF) is to utilize the manufacturing variations of circuit elements to produce an independent and unpredictable response to any input challenge vector. Attacks on PUFs that predict the responses to input challenge vectors offer an interesting research problem. An attacking approach based on the optimization theory and side-channel information is proposed where we estimate the manufacturing variations of the circuit elements and predict the PUF’s responses to challenge vectors whose actual responses are not known. We apply this attacking approach on some popular PUF designs, including the Arbiter PUFs, the Memristor Crossbar PUFs, and the XOR Arbiter PUFs. Simulations show a substantial reduction in attack complexity compared with previously proposed machine-learning (ML)-based attacks: we achieve an average reduction of 66% in attack time compared with the ML approach. Despite some overhead, our approach is also applicable when the PUF responses are noisy. <s> BIB004
Confidentiality and Integrity of Hardware: PUFs. The integration of multiple chips into 3D/2.5D stacks seems beneficial for the notion of PUFs, as the individual chips are subject to independent process variations. Thus, one can build up PUFs using multiple, independent sources of entropy. In BIB002  , two such schemes have been proposed, which further leverage the process variations of TSVs. While promising in principle, these studies did not consider state-of-the-art machine learning attacks like BIB003  BIB001 BIB004 ; their actual resilience remains yet to be demonstrated.