Search

A modified version of the Bellare and Rogaway (1993) adversarial model is encoded using Asynchronous Product Automata (APA). A model checker tool, Simple Homomorphism Verification Tool (SHVT), is then used to perform state-space analysis on the Automata in the setting of planning problem. The three-party identity-based secret public key protocol (3P-ID-SPK) protocol of Lim and Paterson (2006), which claims to provide explicit key authentication, is used as a case study. We then refute its heuristic security argument by revealing a previously unpublished flaw in the protocol using SHVT. We then show how our approach can automatically repair the protocol. This is, to the best of our knowledge, the first work that integrates an adversarial model from the computational complexity paradigm with an automated tool from the computer security paradigm to analyse protocols in an artificial intelligence problem setting – planning problem – and, more importantly, to repair protocols. [ABSTRACT FROM AUTHOR]

We revisit the password-based group key exchange protocol due to Lee et al. (2004), which carries a claimed proof of security in the Bresson et al. model under the intractability of the Decisional Diffie–Hellman problem (DDH) and Computational Diffie–Hellman (CDH) problem. We reveal a previously unpublished flaw in the protocol and its proof, whereby we demonstrate that the protocol violates the definition of security in the model. To provide a better insight into the protocol and proof failures, we present a fixed protocol. We hope our analysis will enable similar mistakes to be avoided in the future. We also revisit protocol 4 of Song and Kim (2000), and reveal a previously unpublished flaw in the protocol (i.e., a reflection attack). [ABSTRACT FROM AUTHOR]

Biometric-based authentication methods have been widely used, for example on portable devices (e.g., Android and iOS devices). However, there are several known limitations in existing authentication methods based on biometrics (e.g., those using facial, iris, and fingerprint). For example, in a healthcare context, a user may be physically incapable of completing the authentication due to his/her medical conditions. Hence, as a complementary authentication mechanism, there have been attempts to also utilize electrocardiogram (ECG). In this work, we propose an ECG authentication system that leverages deep learning. Specifically, to achieve generalization ability, complementary ensemble empirical decomposition (CEEMD) is introduced in our design. Moreover, a 1-D Multi-scale Convolutional Neural Network (1-D MCNN) is implemented to achieve accurate authentication. To evaluate the usability of our proposed approach, we have performed extensive experiments on eight databases, and the findings show that our proposed approach achieves good performance even on abnormal databases and can be adapted for different application environments. In addition, our adopted data from eight public databases requires theoretical statistical treatment for practical applications in real authentication scenarios. [ABSTRACT FROM AUTHOR]