189 research outputs found
DoubleEcho: Mitigating Context-Manipulation Attacks in Copresence Verification
Copresence verification based on context can improve usability and strengthen
security of many authentication and access control systems. By sensing and
comparing their surroundings, two or more devices can tell whether they are
copresent and use this information to make access control decisions. To the
best of our knowledge, all context-based copresence verification mechanisms to
date are susceptible to context-manipulation attacks. In such attacks, a
distributed adversary replicates the same context at the (different) locations
of the victim devices, and induces them to believe that they are copresent. In
this paper we propose DoubleEcho, a context-based copresence verification
technique that leverages acoustic Room Impulse Response (RIR) to mitigate
context-manipulation attacks. In DoubleEcho, one device emits a wide-band
audible chirp and all participating devices record reflections of the chirp
from the surrounding environment. Since RIR is, by its very nature, dependent
on the physical surroundings, it constitutes a unique location signature that
is hard for an adversary to replicate. We evaluate DoubleEcho by collecting RIR
data with various mobile devices and in a range of different locations. We show
that DoubleEcho mitigates context-manipulation attacks whereas all other
approaches to date are entirely vulnerable to such attacks. DoubleEcho detects
copresence (or lack thereof) in roughly 2 seconds and works on commodity
devices
Dynamic Reciprocal Authentication Protocol for Mobile Cloud Computing
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.A combination of mobile and cloud computing delivers many advantages such as mobility, resources, and accessibility through seamless data transmission via the Internet anywhere at any time. However, data transmission through vulnerable channels poses security threats such as man-in-the-middle, playback, impersonation, and asynchronization attacks. To address these threats, we define an explicit security model that can precisely measure the practical capabilities of an adversary. A systematic methodology consisting of 16 evaluation criteria is used for comparative evaluation, thereby leading other approaches to be evaluated through a common scale. Finally, we propose a dynamic reciprocal authentication protocol to secure data transmission in mobile cloud computing (MCC). In particular, our proposed protocol develops a secure reciprocal authentication method, which is free of Diffie–Hellman limitations, and has immunity against basic or sophisticated known attacks. The protocol utilizes multifactor authentication of usernames, passwords, and a one-time password (OTP). The OTP is automatically generated and regularly updated for every connection. The proposed protocol is implemented and tested using Java to demonstrate its efficiency in authenticating communications and securing data transmitted in the MCC environment. Results of the evaluation process indicate that compared with the existing works, the proposed protocol possesses obvious capabilities in security and in communication and computation costs
Privacy-centered authentication: a new framework and analysis
© 2023 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The usage of authentication schemes is increasing in our daily life with the ubiquitous spreading Internet services. The verification of user’s identity is still predominantly password-based, despite of being susceptible to various attacks and openly hated by users. Bonneau et al. presented a framework, based on Usability, Deployability, and Security criteria (UDS), to evaluate authentication schemes and find a replacement for passwords. Although the UDS framework is a mature and comprehensive evaluation framework and has been extended by other authors, it does not analyse privacy aspects in the usage of authentication schemes. In the present work, we extend the UDS framework with a privacy category to allow a more comprehensive evaluation, becoming an UDSP framework. We provide a thorough, rigorous assessment of sample authentication schemes, including analyse novel behavioural biometrics. Our work also discusses implementation aspects regarding the new privacy dimension and sketches the prospect of future authentication schemes.Javier Parra-Arnau is the recipient of a “Ramón y Cajal” fellowship (ref. RYC2021–034256-I) funded by the Spanish Ministry of Science and Innovation and the European Union – “NextGenerationEU”/PRTR (Plan de Recuperación, Transformación y Resiliencia). This work was also supported by the Spanish Government under the project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” PID2020–113795RB-C31, funded by MCIN/AEI/10.13039/501100011033, and through the project “MOBILYTICS” (TED2021–129782B-I00), funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR.Peer ReviewedPostprint (published version
Dynamic Slicing by On-demand Re-execution
In this paper, we propose a novel approach that aims to offer an alternative
to the prevalent paradigm to dynamic slicing construction. Dynamic slicing
requires dynamic data and control dependencies that arise in an execution.
During a single execution, memory reference information is recorded and then
traversed to extract dependencies. Execute-once approaches and tools are
challenged even by executions of moderate size of simple and short programs. We
propose to shift practical time complexity from execution size to slice size.
In particular, our approach executes the program multiple times while tracking
targeted information at each execution. We present a concrete algorithm that
follows an on-demand re-execution paradigm that uses a novel concept of
frontier dependency to incrementally build a dynamic slice. To focus dependency
tracking, the algorithm relies on static analysis. We show results of an
evaluation on the SV-COMP benchmark and Antrl4 unit tests that provide evidence
that on-demand re-execution can provide performance gains particularly when
slice size is small and execution size is large
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