LAUSR

Multifactor authentication (MFA) is a growing trend for the accurate identification of the legitimate users through different modalities such as biometrics, nonbiometric, and cognitive behavior metric. In this paper, we have developed an adaptive MFA that considers the effects of different user devices, media, environments, and the frequency of authentication to detect the legitimate user. For this purpose, initially, we have evaluated the trustworthiness values of all the authentication modalities in different user devices and media using a nonlinear programming problem with probabilistic constraints. Finally, an evolutionary strategy, using fuzzy “IF–THEN” rule and genetic algorithm has been developed for the adaptive selection of authentication modalities. We have done a numerical simulation to prove the effectiveness and efficiency of the proposed method. Moreover, we have developed a prototype client–server-based application and have done a detailed user study to justify its better usability than the existing counterparts.