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Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques

  • Satyam Das , Risa Deshwal , M. Anbarasi and Shanmugam G. Siva
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Cybersecurity Unlocked
This chapter is in the book Cybersecurity Unlocked

Abstract

Voice biometrics-based dynamic encryption is a key generation technology that is emerging within the scope of the subject’s digital footprints. In this study, we present three unique approaches to constrain the process of cryptographic key generation from a spoken phrase by a user. Our baseline technique operates on unprocessed audio files, from which it retrieves MFCCs and subsequently undergoes fuzzy extraction, quantization, and finally hashing using SHA-256 to dynamically generate keys. The second method, “enhanced averaging,” utilizes multiple clean reference recordings and improves quantization by computing a mean value, granulating the number of recordings. The third method, Hamming enhancement, divides the string of quantization results into equal-sized blocks and applies Hamming (7,4) error correction to each block, enabling single-bit error correction. Robust simulation results as well as several contrasting evaluations show that traditional approaches bear greater consistency in noiseless environments; however, our approaches outperform the rest in cases with low acceptance threshold tuning and prove to enhance overall regenerative accuracy alongside computational speeds. This multifaceted, highly adaptive posture with an absence of dormant keys allows greater performance against spoofing attacks and makes the method more applicable to mobile banking and IoT devices.

Abstract

Voice biometrics-based dynamic encryption is a key generation technology that is emerging within the scope of the subject’s digital footprints. In this study, we present three unique approaches to constrain the process of cryptographic key generation from a spoken phrase by a user. Our baseline technique operates on unprocessed audio files, from which it retrieves MFCCs and subsequently undergoes fuzzy extraction, quantization, and finally hashing using SHA-256 to dynamically generate keys. The second method, “enhanced averaging,” utilizes multiple clean reference recordings and improves quantization by computing a mean value, granulating the number of recordings. The third method, Hamming enhancement, divides the string of quantization results into equal-sized blocks and applies Hamming (7,4) error correction to each block, enabling single-bit error correction. Robust simulation results as well as several contrasting evaluations show that traditional approaches bear greater consistency in noiseless environments; however, our approaches outperform the rest in cases with low acceptance threshold tuning and prove to enhance overall regenerative accuracy alongside computational speeds. This multifaceted, highly adaptive posture with an absence of dormant keys allows greater performance against spoofing attacks and makes the method more applicable to mobile banking and IoT devices.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
  4. Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
  5. Chapter 3 Data Science for Threat Detection and Analysis 59
  6. Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
  7. Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
  8. Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
  9. Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
  10. Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
  11. Chapter 9 Light POW for Smart Grid Communication 201
  12. Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
  13. Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
  14. Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
  15. Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
  16. Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
  17. Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
  18. Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
  19. Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
  20. Chapter 18 Firewall and IDS in Cybersecurity 439
  21. Index
https://doi.org/10.1515/9783111712895-016

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
  4. Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
  5. Chapter 3 Data Science for Threat Detection and Analysis 59
  6. Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
  7. Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
  8. Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
  9. Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
  10. Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
  11. Chapter 9 Light POW for Smart Grid Communication 201
  12. Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
  13. Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
  14. Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
  15. Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
  16. Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
  17. Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
  18. Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
  19. Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
  20. Chapter 18 Firewall and IDS in Cybersecurity 439
  21. Index
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