Personal Identification Numbers (PINs) are the most common user authentication method for in-person banking transactions at ATMs. The US Federal Reserve reported that, in 2018, PINs secured 31.4 billion transactions in the US, with an overall worth of US$ 1.19 trillion. One well-known attack type involves the use of cameras to spy on the ATM PIN pad during PIN entry. Countermeasures include covering the PIN pad with a shield or with the other hand while typing. Although this protects PINs from visual attacks, acoustic emanations from the PIN pad itself open the door for another attack type. In this paper, we show the feasibility of an acoustic side-channel attack (called PinDrop ) to reconstruct PINs by profiling acoustic signatures of individual keys of a PIN pad. We demonstrate the practicality of PinDrop via two sets of data collection experiments involving two commercially available metal PIN pad models and 58 participants who entered a total of 5,800 5-digit PINs. We simulated two realistic attack scenarios: (1) a microphone placed near the ATM (0.3 m away) and (2) a real-time attacker (with a microphone) standing in the queue at a common courtesy distance of 2 m. In the former case, we show that PinDrop recovers 96% of 4-digit, and up to 94% of 5-digits, PINs. Whereas, at 2 m away, it recovers up to 57% of 4-digit, and up to 39% of 5-digit PINs in three attempts. We believe that these results are both significant and worrisome.@en

This paper introduces DISPERSE, a distributed scalable architecture for delivery of content and services that provides resilience against node failure through location-independent storage and replication of content. Current content delivery networks (CDNs) have, at least to some degree, a centralized structure thus susceptible to a single point of failure. DISPERSE addresses this limitation by implementing a fully de-centralized structure. DISPERSE is a two-layer architecture: The first layer (front-end layer) exposes services (e.g., Web, SFTP) to clients; the second layer (back-end layer) provides reliable distributed storage of content and application state. Content in DISPERSE's back-end layer is stored and exchanged as Named Data Network (NDN) content objects. This allows DISPERSE to implement fine-grained, location-independent, fully decentralized content replication mechanisms. We validate the performance of DISPERSE under two node failure scenarios. In the first scenario, content can be stored in any DISPERSE node, and all nodes are equally likely to fail. In this scenario, we use non-linear optimization techniques to determine the optimal number of content copies under availability and latency constraints. In the second scenario, different nodes fail with different probabilities, and content is stored in nodes according to its value, node failure probability, and resource availability. This scenario is addressed as an instance of the minimum cost flow problem. Our results show that DISPERSE reduces the failure of content retrieval by five orders of magnitude compared to common CDN implementations, without significantly increasing content retrieval delay. Further, numerical results show that DISPERSE improves content availability by a factor of 1.3 imes-2.3 imes when deploying the minimum cost flow algorithm.@en

Personal Identification Numbers (PINs) are widely used as the primary authentication method for Automated Teller Machines (ATMs) and Point of Sale (PoS). ATM and PoS typically mitigate attacks including shoulder-surfing by displaying dots on their screen rather than PIN digits, and by obstructing the view of the keypad. In this paper, we explore several sources of information leakage from common ATM and PoS installations that the adversary can leverage to reduce the number of attempts necessary to guess a PIN. Specifically, we evaluate how the adversary can leverage audio feedback generated by a standard ATM keypad to infer accurate inter-keystroke timing information, and how these timings can be used to improve attacks based on the observation of the user’s typing behavior, partial PIN information, and attacks based on thermal cameras. Our results show that inter-keystroke timings can be extracted from audio feedback far more accurately than from previously explored sources (e.g., videos). In our experiments, this increase in accuracy translated to a meaningful increase in guessing performance. Further, various combinations of these sources of information allowed us to guess between 44% and 89% of the PINs within 5 attempts. Finally, we observed that based on the type of information available to the adversary, and contrary to common knowledge, uniform PIN selection is not necessarily the best strategy. We consider these results relevant and important, as they highlight a real threat to any authentication system that relies on PINs.@en