Biblio

It is standard practice that the secret key derived from an execution of a Password Authenticated Key Exchange (PAKE) protocol is used to authenticate and encrypt some data payload using a Symmetric Key Protocol (SKP). Unfortunately, most PAKEs of practical interest are studied using so-called game-based models, which – unlike simulation models – do not guarantee secure composition per se. However, Brzuska et al. (CCS 2011) have shown that a middle ground is possible in the case of authenticated key exchange that relies on Public-Key Infrastructure (PKI): the game-based models do provide secure composition guarantees when the class of higher-level applications is restricted to SKPs. The question that we pose in this paper is whether or not a similar result can be exhibited for PAKE. Our work answers this question positively. More specifically, we show that PAKE protocols secure according to the game-based Real-or-Random (RoR) definition with the weak forward secrecy of Abdalla et al. (S&P 2015) allow for safe composition with arbitrary, higher-level SKPs. Since there is evidence that most PAKEs secure in the Find-then-Guess (FtG) model are in fact secure according to RoR definition, we can conclude that nearly all provably secure PAKEs enjoy a certain degree of composition, one that at least covers the case of implementing secure channels.

In many domains, engineers build simulation models (e.g., Simulink) before developing code to simulate the behavior of complex systems (e.g., Cyber-Physical Systems). Those models are commonly heavy to simulate which makes it difficult to execute the entire test suite. Furthermore, it is often difficult to measure white-box coverage of test cases when employing such models. In addition, the historical data related to failures might not be available. This paper proposes a cost-effective approach for test case selection that relies on black-box data related to inputs and outputs of the system. The approach defines in total five effectiveness measures and one cost measure followed by deriving in total 15 objective combinations and integrating them within Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). We empirically evaluated our approach with all these 15 combinations using four case studies by employing mutation testing to assess the fault revealing capability. The results demonstrated that our approach managed to improve Random Search by 26% on average in terms of the Hypervolume quality indicator.