Hybrid Side-Channel/Machine-Learning Attacks on PUFs: A New Threat?

Machine Learning (ML) is a well-studied strategy in modeling Physical Unclonable Functions (PUFs) but reaches its limits while applied on instances of high complexity. To address this issue, side-channel attacks have recently been combined with modeling techniques to make attacks more efficient [25][26]. In this work, we present an overview and survey of these so-called "hybrid modeling and side-channel attacks" on PUFs, as well as of classical side channel techniques for PUFs. A taxonomy is proposed based on the characteristics of different side-channel attacks. The practical reach of some published side-channel attacks is discussed. Both challenges and opportunities for PUF attackers are introduced. Countermeasures against some certain side-channel attacks are also analyzed. To better understand the side-channel attacks on PUFs, three different methodologies of implementing side-channel attacks are compared. At the end of this paper, we bring forward some open problems for this research area.