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CLAS: A Novel Communications Latency Based Authentication Scheme

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journal contribution
submitted on 2024-09-18, 10:53 and posted on 2024-09-18, 10:53 authored by Zuochao Dou, Issa Khalil, Abdallah Khreishah

We design and implement a novel communications latency based authentication scheme, dubbed CLAS, that strengthens the security of state-of-the-art web authentication approaches by leveraging the round trip network communications latency (RTL) between clients and authenticators. In addition to the traditional credentials, CLAS profiles RTL values of clients and uses them to defend against password compromise. The key challenges are (i) to prevent RTL manipulation, (ii) to alleviate network instabilities, and (iii) to address mobile clients. CLAS addresses the first challenge by introducing a novel network architecture, which makes it extremely difficult for attackers to simulate legitimate RTL values. The second challenge is addressed by outlier removal and multiple temporal profiling, while the last challenge is addressed by augmenting CLAS with out-of-band-channels or other authentication schemes. CLAS restricts login to profiled locations while demanding additional information for nonprofiled ones, which highly reduces the attack surface even when the legitimate credentials are compromised. Additionally, unlike many state-of-the-art authentication mechanisms, CLAS is resilient to phishing, pharming, man-in-the-middle, and social engineering attacks. Furthermore, CLAS is transparent to users and incurs negligible overhead. The experimental results show that CLAS can achieve very low false positive and false negative rates.

Other Information

Published in: Security and Communication Networks
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1155/2017/4286903

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Hindawi

Publication Year

  • 2017

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU