Dozens of two-factor authentication schemes have been proposed to secure real-time data access in industrial wireless sensor networks (WSNs). However, more often than not, the protocol designers advocate the merits of their scheme, but do not reveal (or unconsciously overlooking) the aspects on which their scheme performs poorly. Such lack of an objective, comprehensive measurement leads to the unsatisfactory "break-fix-break-fix" cycle in this research area. In this paper, we make an attempt towards breaking this undesirable cycle by (1) proposing a systematical evaluation framework for schemes to be assessed objectively; (2) revisiting two foremost schemes proposed by Wu et al. (2017) and Srinivas et al. (2017) to reveal the challenges and difficulties in designing a sound scheme; and (3) conducting a large-scale evaluation of 44 representative schemes under our evaluation framework, thereby providing the missing measurements for two-factor schemes in industrial WSNs.

Multi-factor authentication (MFA) has been widely used to safeguard high-value assets. Unlike single-factor authentication (e.g., password-only login), t-factor authentication ( tFA) requires a user always to carry and present t specified factors so as to strengthen the security of login. Nevertheless, this may restrict user experience in limiting the flexibility of factor usage, e.g., the user may prefer to choose any factors at hand for login authentication. To bring back usability and flexibility without loss of security, we introduce a new notion of authentication, called (t,n) threshold MFA, that allows a user to actively choose t factors out of n based on preference. We further define the “most-rigorous” multi-factor security model for the new notion, allowing attackers to control public channels, launch active/passive attacks, and compromise/corrupt any subset of parties as well as factors. We state that the model can capture the most practical security needs in the literature. We design a threshold MFA key exchange (T-MFAKE) protocol built on the top of a threshold oblivious pseudorandom function and an authenticated key exchange protocol. Our protocol achieves the “highest-attainable” security against all attacking attempts in the context of parties/factors being compromised/corrupted. As for efficiency, our design only requires 4+t exponentiations, 2 multi-exponentiations and 2 communication rounds. Compared with existing tFA schemes, even the degenerated (t,t) version of our protocol achieves the strongest security (stronger than most schemes) and higher efficiency on computational and communication. We instantiate our design on real-world platform to highlight its practicability and efficiency.

Nowadays wireless sensor networks (WSNs) have drawn great attention from both industrial world and academic community. To facilitate real‐time data access for external users from the sensor nodes directly, password‐based authentication has become the prevalent authentication mechanism in the past decades. In this work, we investigate three foremost protocols in the area of password‐based user authentication scheme for WSNs. Firstly, we analyze an efficient and anonymous protocol and demonstrate that though this protocol is equipped with a formal proof, it actually has several security loopholes been overlooked, such that it cannot resist against smart card loss attack and violate forward secrecy. Secondly, we scrutinize a lightweight protocol and point out that it cannot achieve the claimed security goal of forward secrecy, as well as suffering from user anonymity violation attack and offline password guessing attack. Thirdly, we find that an anonymous scheme fails to preserve two critical properties of forward secrecy and user friendliness. In addition, by adopting the “perfect forward secrecy (PFS)” principle, we provide several effective countermeasures to remedy the identified weaknesses. To test the necessity and effectiveness of our suggestions, we conduct a comparison of 10 representative schemes in terms of the underlying cryptographic primitives used for realizing forward secrecy.

Password-only authentication is one of the most popular secure mechanisms for real-world online applications. But it easily suffers from a practical threat - password leakage, incurred by external and internal attackers. The external attacker may compromise the password file stored on the authentication server, and the insider may deliberately steal the passwords or inadvertently leak the passwords. So far, there are two main techniques to address the leakage: Augmented password-authentication key exchange (aPAKE) against insiders and honeyword technique for external attackers. But none of them can resist both attacks. To fill the gap, we propose the notion of honey PAKE (HPAKE) that allows the authentication server to detect the password leakage and achieve the security beyond the traditional bound of aPAKE. Further, we build an HPAKE construction on the top of the honeyword mechanism, honey encryption, and OPAQUE which is a standardized aPAKE. We formally analyze the security of our design, achieving the insider resistance and the password breach detection. We implement our design and deploy it in the real environment. The experimental results show that our protocol only costs 71.27 ms for one complete run, within 20.67 ms on computation and 50.6 ms on communication. This means our design is secure and practical for real-world applications.

Face spoofing is a serious threat to the security of face recognition and face anti-spoofing (FAS) has become one of the popular research directions recently. The pipeline of modern face anti-spoofing methods is to train deep neural networks to distinguish live and spoof faces using various auxiliary information. Depth maps are the most commonly used auxiliary features due to their computational simplicity and ease of discrimination. However, existing methods do not generalize well in complex environments and against unknown attacks. In this paper, we propose a novel FAS method using generative adversarial network (GAN). We train a GAN for generating depth maps of faces. The network uses the architecture of Wasserstein GAN and adds an attention module to learn the contribution of different regions to the deep graph. The classifier is trained using a latent variable containing depth information in the network. Experiments demonstrate that our approach can better generalize complex external conditions such as illumination and background. Among all results, we gain the best result of 1.17% (EER) on CASIA-FASD and 21.57% (HTER) on cross testing between Replay-Attack and CASIA-FASD.