Passive User Authentication Utilizing Behavioral Biometrics for IIoT Systems

Abstract

Passive authentication is of great importance for security guarantee in industrial Internet of Things (IIoT) systems. Based on the behavioral biometrics from sequential operation actions in IIoT, this article proposes a nonintrusive and passive authentication framework for continuous user authentication against the impersonation attack. We first provide experimental results to demonstrate the discriminability and stability for the intrinsic features of sequential operation actions, and then leverage the Kalman filtering and wavelet techniques for noise elimination and the singular value decomposition method for the dimensionality reduction of feature space. We further exploit the one-class classification technique to formulate the authentication decision process as a hidden Markov model (HMM). Extensive experiments are conducted to illustrate the authentication performance of the passive authentication framework in terms of the false acceptance rate, false rejection rate, and equal-error rate. We also investigate the related authentication efficiency issues in terms of the usability to the operation-action sequence length, the scalability to the number of features and user space, and the sensitivity to the operation action features.