Search for: All records

Internet of Things (IoT) have broad and deep penetration into our society, and many of them are resource-constrained, calling for lightweight security protocols. Physical unclonable functions (PUFs) leverage physical variations of circuits to produce responses unique for individual devices, and hence are not reproducible even by their manufacturers. Implementable with simplistic circuits and operable with low energy, PUFs are promising candidates as security primitives for resource-constrained IoT devices. Arbiter PUF (APUF) and its variants are lightweight in resource requirements but suffer from vulnerability to machine learning attacks. To defend APUF variants against machine learning attacks, in this paper we investigate a challenge input interface, which incurs low overhead. Analytical and experimental studies were carried out, showing substantial improvement of resistance against machine learning attacks when a PUF is equipped with the interface, rendering interfaced APUF variants promising candidates for security critical applications. 

Security is of importance for communication networks, and many network nodes, like sensors and IoT devices, are resource-constrained. Physical Unclonable Functions (PUFs) leverage physical variations of the integrated circuits to produce responses unique to individual circuits and have the potential for delivering security for low-cost networks. But before a PUF can be adopted for security applications, all security vulnerabilities must be discovered. Recently, a new PUF known as Interpose PUF (IPUF) was proposed, which was tested to be secure against reliability-based modeling attacks and machine learning attacks when the attacked IPUF is of small size. A recent study showed IPUFs succumbed to a divide-and-conquer attack, and the attack method requires the position of the interpose bit known to the attacker, a condition that can be easily obfuscated by using a random interpose position. Thus, large IPUFs may still remain secure against all known modeling attacks if the interpose position is unknown to attackers. In this paper, we present a new modeling attack method of IPUFs using multilayer neural networks, and the attack method requires no knowledge of the interpose position. Our attack was tested on simulated IPUFs and silicon IPUFs implemented on FPGAs, and the results showed that many IPUFs which were resilient against existing attacks cannot withstand our new attack method, revealing a new vulnerability of IPUFs by re-defining the boundary between secure and insecure regions in the IPUF parameter space.