IDENTIFICATION AND AUTHENTICATION SYSTEMS IN THE CONTEXT OF MODERN INFORMATION SECURITY THREATS

Abstract

The monograph aims to provide a detailed analysis of current biometric authentication technologies using electrocardiographic (ECG) signals and voice as two fundamentally different but complementary modalities. The relevance of the work is due to the ongoing and evolving crisis of password-based security mechanisms (annual leaks affecting billions of user accounts, and generative speech synthesis technologies moving towards levels where the human voice can be imitated in a matter of minutes). The combination of ECG (an innate marker of life that is almost impossible to fake) with voice (which provides simple contactless verification at a distance) forms a logical and specialized premise for a new generation of biometric structures. The first part is devoted to the basic theory of biometric technologies: nine principles (uniqueness, stability, measurability, universality, resistance to counterfeiting, security, accuracy, standardization and acceptability) for building systems; a comparative analysis of nine physiological and behavioral modalities; nine functional modules of a typical biometric system; architectural solutions from autonomous to cloud-based and adaptive with Zero Trust support; a system of quality assessment metrics – FAR/FRR/EER/DET/minDCF curves – and approaches to ensuring demographic equity. The second part is devoted exclusively to ECG-based biometrics. The unique characteristics of the electrocardiographic signal, arising from anatomical, electrophysiological and neurohumoral factors, are further explained in physiological terms. All processing stages are described – from signal acquisition (with contact and non-contact methods), noise filtering (adaptive LMS filter or 1D U-Net based on neural networks), cardiac cycle segmentation using the Pan–Tompkins algorithm, autoencoder architectures, formation of morphological, temporal and spectral features for average heart beat analysis and dimensionality reduction based on PCA/LDA – up to classification and decision making. Comparison of two-class SVM, k-NN, decision trees and neural networks in different configurations was performed using four datasets (MIT-BIH, PTB, Fantasia, Lviv Biometric Data Set). The highest identification accuracy (98.90%, FAR 0.18·10⁻³, FRR 7.32·10⁻³) was achieved by a neural network with a hidden layer architecture (256, 128). It was experimented with deep embedding architectures (1D-ResNet, Transformer) as well as metric loss functions (Triplet Loss, ArcFace) and tested whether it is possible to deploy it on edge devices. The third section contains the full voice biometric chain: classical MFCC, LPC and PNCC descriptors, speech preprocessing methods (spectral subtraction, Wiener filtering, VAD and neural network optimization using Wave-U-Net), and finally four modern neural network architectures for speaker embedding-ECAPA-TDNN/WavLM/TitaNet/PyAnnote. In the framework of the experimental study, it is shown that TitaNet achieves the highest verification accuracy (0.96), and ECAPA-TDNN – 0.95 and scales better with increasing number of users; while WavLM, under difficult conditions, demonstrates the highest sensitivity to channel variations. The fourth section covers several practical implementation topics: international legal regulation (GDPR, AI Act, BIPA, ISO/IEC 24745 and 30107 standards), ethical aspects of the use of biometric data, architectural solutions for industrial implementation (design of a template database with approximate search algorithms: HNSW and IVF-PQ; cancelable biometric methods; fault tolerance; integration of BioAPI and FIDO2), implementation scenarios in the e-ID industry: banking, telecommunications, medicine, public administration and corporate security; the topic of the user interface - from ease of use to accessibility and economic analysis of costs against return on investment. Strategic development opportunities are identified: multimodal integration of ECG and voice at the level of feature fusion, scoring and decision-making; adaptive systems based on federated learning with differentiated privacy against threats from generative AI; transfer to decentralized identification based on Zero-Knowledge Proof. The scientific novelty of the work is determined by a new integrated concept of biometric authentication with a combination of ECG and voice; a comparative study of modern embedding architectures not only with an assessment of accuracy, but also with a determination of the scalability of models and resistance to spoofing attacks, based on the most modern generative models; as well as a quantitative measurement of how serious a threat these models pose to voice biometrics systems. The practical significance depends on the ability to directly apply the results to build biometric systems in finance, telecommunications, medicine and other areas of social activity. The monograph is designed for researchers, graduate students and students of specialties in information security, digital signal processing and machine learning, as well as for practicing specialists in the field of design and implementation of biometric authentication systems.