Optimized Identity Authentication via Channel State Information for Two-Factor User Verification in Information Systems

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 8; p. 2465
• Main Authors: Tian, Chuangeng, Li, Fanjia, Liu, Xiaomeng, Li, Juanjuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 14.04.2025; MDPI
• Subjects: Accuracy; activity segmentation; Algorithms; Analysis; Biometrics; Biometry; channel state information; Facial recognition technology; identity recognition; Information systems; Keyboards; keystroke dynamics; Methods; Noise control; Privacy; Propagation; Signal processing; Support vector machines; two-factor authentication; channel state information; keystroke dynamics; activity segmentation; two-factor authentication; identity recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25082465

Traditional user authentication mechanisms in information systems, such as passwords and biometrics, remain vulnerable to forgery, theft, and privacy breaches. To address these limitations, this study proposes a two-factor authentication framework that integrates Channel State Information (CSI) with conventional methods to enhance security and reliability. The proposed approach leverages unique CSI variations induced by user-specific keystroke dynamics to extract discriminative biometric features. A robust signal processing pipeline is implemented, combining Hampel filtering, Butterworth low-pass filtering, and wavelet transform threshold denoising to eliminate noise and outliers from raw CSI data. Feature extraction is further optimized through a dual-threshold moving window detection algorithm for precise activity segmentation, a subcarrier selection method to filter redundant or unstable channels, and principal component analysis (PCA) to reduce feature dimensionality while retaining 90% of critical information. For classification, a kernel support vector machine (SVM) model is trained using a randomized hyperparameter search algorithm. The SVM classifies the CSI feature patterns obtained from user-specific keystroke dynamics, which are processed by Hampel filtering, Butterworth low-pass filtering, wavelet transform threshold denoising, a dual-threshold moving window detection algorithm, a subcarrier selection method, and PCA, to achieve optimal performance. The experimental results show that the user recognition accuracy of this algorithm is 2–3% better than current algorithms.