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Personal Identification Numbers (PIN) are widely used as authentication method for systems such as Automated Teller Machines (ATMs) and Point of Sale (PoS). Input devices (PIN pads) usually give the user a feedback sound when a key is pressed. In this paper, we propose an attack based on the extraction of inter-keystroke timing from the feedback sound when users type their PINs. Our attack is able to reach an accuracy of 98% with a mean error of 0.13 +/-6.66 milliseconds. We demonstrate that inter-keystroke timing significantly improves the guessing probability of certain subsets of PINs. We believe this represents a security problem that has to be taken into account for secure PIN generation. Furthermore, we identified several attack scenarios where the adversary can exploit inter-keystroke timing and additional information about the user or the PIN, such as typing behavior. Our results show that combining the inter-keystroke timing with other information drastically reduces attempts to guess a PIN, outperforming random guessing. With our attack, we are able to guess 72% of the 4-digit PINs within 3 attempts. We believe this poses a serious security problem for systems that use PIN-based authentication.

This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work [4]. In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper.

State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., "password123456") and leet speak (e.g., "password" becomes "p4s5w0rd"). Although these rules work well in practice, expanding them to model further passwords is a laborious task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were able to match 51%-73% more passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode., Comment: This is an extended version of the paper which appeared in NeurIPS 2018 Workshop on Security in Machine Learning (SecML'18), see https://github.com/secml2018/secml2018.github.io/raw/master/PASSGAN_SECML2018.pdf

We introduce Hand Movement, Orientation, and Grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data was collected under two conditions: sitting and walking. We achieved authentication EERs as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at 16Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions, and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).

Biometric key generation techniques are used to reliably generate cryptographic material from biometric signals. Existing constructions require users to perform a particular activity (e.g., type or say a password, or provide a handwritten signature), and are therefore not suitable for generating keys continuously. In this paper we present a new technique for biometric key generation from free-text keystroke dynamics. This is the first technique suitable for continuous key generation. Our approach is based on a scaled parity code for key generation (and subsequent key reconstruction), and can be augmented with the use of population data to improve security and reduce key reconstruction error. In particular, we rely on linear discriminant analysis (LDA) to obtain a better representation of discriminable biometric signals. To update the LDA matrix without disclosing user's biometric information, we design a provably secure privacy-preserving protocol (PP-LDA) based on homomorphic encryption. Our biometric key generation with PP-LDA was evaluated on a dataset of 486 users. We report equal error rate around 5% when using LDA, and below 7% without LDA.

In the last decade, there has been a growing realization that the current Internet Protocol is reaching the limits of its senescence. This has prompted several research efforts that aim to design potential next-generation Internet architectures. Named Data Networking (NDN), an instantiation of the content-centric approach to networking, is one such effort. In contrast with IP, NDN routers maintain a significant amount of user-driven state. In this paper we investigate how to use this state for covert ephemeral communication (CEC). CEC allows two or more parties to covertly exchange ephemeral messages, i.e., messages that become unavailable after a certain amount of time. Our techniques rely only on network-layer, rather than application-layer, services. This makes our protocols robust, and communication difficult to uncover. We show that users can build high-bandwidth CECs exploiting features unique to NDN: in-network caches, routers' forwarding state and name matching rules. We assess feasibility and performance of proposed cover channels using a local setup and the official NDN testbed.

Content-Centric Networking (CCN) is an emerging networking paradigm being considered as a possible replacement for the current IP-based host-centric Internet infrastructure. In CCN, named content becomes a first-class entity. CCN focuses on content distribution, which dominates current Internet traffic and is arguably not well served by IP. Named-Data Networking (NDN) is an example of CCN. NDN is also an active research project under the NSF Future Internet Architectures (FIA) program. FIA emphasizes security and privacy from the outset and by design. To be a viable Internet architecture, NDN must be resilient against current and emerging threats. This paper focuses on distributed denial-of-service (DDoS) attacks; in particular we address interest flooding, an attack that exploits key architectural features of NDN. We show that an adversary with limited resources can implement such attack, having a significant impact on network performance. We then introduce Poseidon: a framework for detecting and mitigating interest flooding attacks. Finally, we report on results of extensive simulations assessing proposed countermeasure., Comment: The IEEE Conference on Local Computer Networks (LCN 2013)

Instrumented environments, such as modern building automation systems (BAS), are becoming commonplace and are increasingly interconnected with (and sometimes by) enterprise networks and the Internet. Regardless of the underlying communication platform, secure control of devices in such environments is a challenging task. The current trend is to move from proprietary communication media and protocols to IP over Ethernet. While the move to IP represents progress, new and different Internet architectures might be better-suited for instrumented environments. In this paper, we consider security of instrumented environments in the context of Content-Centric Networking (CCN). In particular, we focus on building automation over Named-Data Networking (NDN), a prominent instance of CCN. After identifying security requirements in a specific BAS sub-domain (lighting control), we construct a concrete NDN-based security architecture, analyze its properties and report on preliminary implementation and experimental results. We believe in securing a communication paradigm well outside of its claimed forte of content distribution. At the same time, we provide a viable (secure and efficient) communication platform for a class of instrumented environments exemplified by lighting control., Comment: arXiv admin note: text overlap with arXiv:1208.0952

With the growing realization that current Internet protocols are reaching the limits of their senescence, a number of on-going research efforts aim to design potential next-generation Internet architectures. Although they vary in maturity and scope, in order to avoid past pitfalls, these efforts seek to treat security and privacy as fundamental requirements. Resilience to Denial-of-Service (DoS) attacks that plague today's Internet is a major issue for any new architecture and deserves full attention. In this paper, we focus on DoS in a specific candidate next-generation Internet architecture called Named-Data Networking (NDN) -- an instantiation of Information-Centric Networking approach. By stressing content dissemination, NDN appears to be attractive and viable approach to many types of current and emerging communication models. It also incorporates some basic security features that mitigate certain attacks. However, NDN's resilience to DoS attacks has not been analyzed to-date. This paper represents the first step towards assessment and possible mitigation of DoS in NDN. After identifying and analyzing several new types of attacks, it investigates their variations, effects and counter-measures. This paper also sheds some light on the long-standing debate about relative virtues of self-certifying, as opposed to human-readable, names.

Content-Centric Networking (CCN) is a concept being considered as a potential future alternative to, or replacement for, today's Internet IP-style packet-switched host-centric networking. One factor making CCN attractive is its focus on content distribution, which dominates current Internet traffic and which is arguably not well-served by IP. Named Data Networking (NDN) is a prominent example of CCN. It is also one of several on-going research efforts aiming to design and develop a full-blown candidate future Internet architecture. Although NDN's primary motivation is content distribution, it is envisioned to support other types of traffic, such as conferencing (audio, video) as well as more historical applications, such as remote login. However, it is unclear how suitable NDN is for applications that are not obviously content-centric. In this paper, we explore NDN in the context of a class of applications that involve low- latency bidirectional communication. Specifically, we propose a few architectural amendments to NDN that provide significantly better throughput and lower latency for this class of applications. The proposed approach is validated via both simulations and testbed experiments., Comment: 9 pages, 5 figures

Content-centric networking -- also known as information-centric networking (ICN) -- shifts emphasis from hosts and interfaces (as in today's Internet) to data. Named data becomes addressable and routable, while locations that currently store that data become irrelevant to applications. Named Data Networking (NDN) is a large collaborative research effort that exemplifies the content-centric approach to networking. NDN has some innate privacy-friendly features, such as lack of source and destination addresses on packets. However, as discussed in this paper, NDN architecture prompts some privacy concerns mainly stemming from the semantic richness of names. We examine privacy-relevant characteristics of NDN and present an initial attempt to achieve communication privacy. Specifically, we design an NDN add-on tool, called ANDaNA, that borrows a number of features from Tor. As we demonstrate via experiments, it provides comparable anonymity with lower relative overhead., Comment: NDSS 2012 - Proceedings of the Network and Distributed System Security Symposium, San Diego, California, USA

Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information sharing and retrieval. One interesting open problem in this context involves two parties that need to assess the similarity of their datasets, but are reluctant to disclose their actual content. This paper presents an efficient and provably-secure construction supporting the privacy-preserving evaluation of sample set similarity, where similarity is measured as the Jaccard index. We present two protocols: the first securely computes the (Jaccard) similarity of two sets, and the second approximates it, using MinHash techniques, with lower complexities. We show that our novel protocols are attractive in many compelling applications, including document/multimedia similarity, biometric authentication, and genetic tests. In the process, we demonstrate that our constructions are appreciably more efficient than prior work., Comment: A preliminary version of this paper was published in the Proceedings of the 7th ESORICS International Workshop on Digital Privacy Management (DPM 2012). This is the full version, appearing in the Journal of Computer Security

Recent advances in DNA sequencing technologies have put ubiquitous availability of fully sequenced human genomes within reach. It is no longer hard to imagine the day when everyone will have the means to obtain and store one's own DNA sequence. Widespread and affordable availability of fully sequenced genomes immediately opens up important opportunities in a number of health-related fields. In particular, common genomic applications and tests performed in vitro today will soon be conducted computationally, using digitized genomes. New applications will be developed as genome-enabled medicine becomes increasingly preventive and personalized. However, this progress also prompts significant privacy challenges associated with potential loss, theft, or misuse of genomic data. In this paper, we begin to address genomic privacy by focusing on three important applications: Paternity Tests, Personalized Medicine, and Genetic Compatibility Tests. After carefully analyzing these applications and their privacy requirements, we propose a set of efficient techniques based on private set operations. This allows us to implement in in silico some operations that are currently performed via in vitro methods, in a secure fashion. Experimental results demonstrate that proposed techniques are both feasible and practical today., Comment: 18th ACM Conference on Computer and Communications Security (CCS 2011)