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Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks

  • J. Angel Ida Chellam , R. Kowsalya , R.S. Ramya and S. Senthilkumar
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Cybersecurity Unlocked
This chapter is in the book Cybersecurity Unlocked

Abstract

The advent of quantum computing threatens to undermine current encryption methods significantly, especially those employed in mobile data transmission networks. Quantum algorithms can solve the computational challenges behind present public-key encryption methods like RSA (Rivest-Shamir-Adleman) and elliptic curve cryptography at a speedy rate. With mobile communication networks handling more and more sensitive information, the availability of secure authentication methods is increasingly important. This project proposes to investigate how mobile data communication systems can leverage post-quantum cryptographic (PQC) techniques to enhance authentication protocols. The research compares some NIST-standardized post-quantum cryptography candidates, i.e., lattice-based, code-based, hash-based, and multivariate polynomial cryptography, focusing on their usage in mobile environments. We establish a guideline for constructing PQC-improved authentication protocols, focusing on their quantum-resistance, computational cost, and appropriateness for resource-constrained mobile devices. Our analysis proves that the regulations successfully protect information’s secrecy, integrity, and authenticity in the post-quantum era. This has led to the development of future-proof, safe, and resilient mobile communication networks. PQC augmented authentication systems offer good resistance against quantum attacks while supporting acceptable performance on mobile devices, as evidenced by previous research. Mobile communication systems can achieve long-term security through PQC, thus safeguarding user data and critical infrastructure against potential adversaries that might employ quantum computing in the future.

Abstract

The advent of quantum computing threatens to undermine current encryption methods significantly, especially those employed in mobile data transmission networks. Quantum algorithms can solve the computational challenges behind present public-key encryption methods like RSA (Rivest-Shamir-Adleman) and elliptic curve cryptography at a speedy rate. With mobile communication networks handling more and more sensitive information, the availability of secure authentication methods is increasingly important. This project proposes to investigate how mobile data communication systems can leverage post-quantum cryptographic (PQC) techniques to enhance authentication protocols. The research compares some NIST-standardized post-quantum cryptography candidates, i.e., lattice-based, code-based, hash-based, and multivariate polynomial cryptography, focusing on their usage in mobile environments. We establish a guideline for constructing PQC-improved authentication protocols, focusing on their quantum-resistance, computational cost, and appropriateness for resource-constrained mobile devices. Our analysis proves that the regulations successfully protect information’s secrecy, integrity, and authenticity in the post-quantum era. This has led to the development of future-proof, safe, and resilient mobile communication networks. PQC augmented authentication systems offer good resistance against quantum attacks while supporting acceptable performance on mobile devices, as evidenced by previous research. Mobile communication systems can achieve long-term security through PQC, thus safeguarding user data and critical infrastructure against potential adversaries that might employ quantum computing in the future.

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/10.1515/9783111712895-011

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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