In this paper we demonstrate the first real-world cloning attack on a commercial PUF-based RFID tag. The examined commercial PUFs can be attacked by measuring only 4 protocol executions, which takes less than 200 ms. Using a RFID smartcard emulator, it is then possible to impersonate, i.e., "clone" the PUF. While attacking the 4-way PUF used by these tags can be done using traditional machine learning attacks, we show that the tags can still be attacked if they are configured as presumably secure XOR PUFs. We achieved this by using a new reliability-based machine learning attack that uses a divide-and-conquer approach for attacking the XOR PUFs. This new divide-and-conquer approach results in only a linear increase in needed number of challenge and responses for increasing numbers of XORs. This is in stark contrast to the state-of-the-art machine learning attacks on XOR PUFs that are shown to have an exponential increase in challenge and responses. Hence, it is now possible to attack XOR PUF constructs that were previously believed to be secure against machine learning attacks. Since XOR Arbiter PUFs are one of the most popular and promising electrical strong PUF designs, our reliability-based machine learning attack raises doubts that secure and lightweight electrical strong PUFs can be realized in practice.

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Physical unclonable functions (PUFs) have emerged as a promising solution for securing resource-constrained embedded devices such as RFID tokens. PUFs use the inherent physical differences of every chip to either securely authenticate the chip or generate cryptographic keys without the need of nonvolatile memory. However, PUFs have shown to be vulnerable to model building attacks if the attacker has access to challenge and response pairs. In these model building attacks, machine learning is used to determine the internal parameters of the PUF to build an accurate software model. Nevertheless, PUFs are still a promising building block and several protocols and designs have been proposed that are believed to be resistant against machine learning attacks. In this paper, we take a closer look at two such protocols, one based on reverse fuzzy extractors and one based on pattern matching. We show that it is possible to attack these protocols using machine learning despite the fact that an attacker does not have access to direct challenge and response pairs. The introduced attacks demonstrate that even highly obfuscated responses can be used to attack PUF protocols. Hence, this paper shows that even protocols in which it would be computationally infeasible to compute enough challenge and response pairs for a direct machine learning attack can be attacked using machine learning.Index Terms-Evolution strategies (ES), machine learning, physical unclonable functions (PUFs), reverse fuzzy extractor.

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Intellectual property (IP) right violations are an increasing problem for hardware designers. Illegal copies of IP cores can cause multi-million dollar damages and are thus considered a serious threat. One possible solution to this problem can be digital watermarking schemes for integrated circuits. We propose a new watermarking technique that employs sidechannels as building blocks and can easily and reliably be detected by methods adapted from side-channel analysis. The main idea is to embed a unique signal into a side-channel of the device that serves as a watermark. This enables circuit designers to check integrated circuits for unauthorized use of their watermarked cores. The watermark is hidden below the noise floor of the side channel and is thus hidden from third parties. Furthermore, the proposed schemes can be implemented with very few gates and are thus even harder to detect and to remove. The proposed watermarks can also be realized in a programmable fashion to leak a digital signature.