Point of Sale (PoS) machines have become extremely popular recently. In many economies, most transactions occur using them. Although PoS technology is evolving, PINs are still heavily used. In this paper, we perform a large-scale study to understand how difficult it is to uncover user PINs at PoS, even when the users cover the pad with their hands. Our study involves 142 participants, two types of PoS, and around 13,800 PINs. We develop machine learning techniques to infer PoS PINs by using hidden cameras. Our results show that uncovering PINs in PoS is more complex than in other cases where a user PIN is used, e.g., ATMs, because of the small pad area of PoS. Nevertheless, we could achieve more than 50% Top-3 accuracy for 4-digit PINs and 45% Top-3 accuracy for 5-digit PINs, even when the PIN is covered by the user's hand. We comment on the impact of the camera's position and PoS on the successful inference of the user's PINs. We also comment on the hardness of inferring PINs depending on the physical distance of digits and recommend what are good practices to generate PINs and cover PoS to make PIN inference difficult.

To avoid exploitation of known vulnerabilities, it is standard security practice to not disclose any model information regarding the antennas used in cellular infrastructure. However, in this work, we show that end-user devices receive enough information to infer, with high accuracy, the model-family of antennas. We demonstrate how low-cost hardware and software setups can fingerprint the cellular infrastructure of whole regions within a few minutes by only listening to cellular broadcast messages. To show the effectiveness and hence risk of such fingerprinting, we collected an extensive dataset of broadcast messages from three different countries. We then trained a machine-learning model to classify broadcast messages based on the model-family they belong to. Our results reveal a worryingly high average accuracy of 97% for model-family classification. We further discuss how inferring the model-family with such high accuracy can lead to a class of identification attacks on cellular infrastructure and we subsequently suggest countermeasures to mitigate the fingerprint effectiveness.

Automated Teller Machines (ATMs) represent the most used system for withdrawing cash. The European Central Bank reported more than 11 billion cash withdrawals and loading/unloading transactions on the European ATMs in 2019. Although ATMs have undergone various technological evolutions, Personal Identification Numbers (PINs) are still the most common authentication method for these devices. Unfortunately, the PIN mechanism is vulnerable to shoulder-surfing attacks performed via hidden cameras installed near the ATM to catch the PIN pad. To overcome this problem, people get used to covering the typing hand with the other hand. While such users probably believe this behavior is safe enough to protect against mentioned attacks, there is no clear assessment of this countermeasure in the scientific literature. This paper proposes a novel attack to reconstruct PINs entered by victims covering the typing hand with the other hand. We consider the setting where the attacker can access an ATM PIN pad of the same brand/model as the target one. Afterward, the attacker uses that model to infer the digits pressed by the victim while entering the PIN. Our attack owes its success to a carefully selected deep learning architecture that can infer the PIN from the typing hand position and movements. We run a detailed experimental analysis including 58 users. With our approach, we can guess 30% of the 5-digit PINs within three attempts - the ones usually allowed by ATM before blocking the card. We also conducted a survey with 78 users that managed to reach an accuracy of only 7.92% on average for the same setting. Finally, we evaluate a shielding countermeasure that proved to be rather inefficient unless the whole keypad is shielded.