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Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques

  • Radhakrishnan Maivizhi and Palanichamy Yogesh
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Cybersecurity Unlocked
This chapter is in the book Cybersecurity Unlocked

Abstract

With the recent technological advancement of ICT industry and proliferation of networked systems including the Internet, digital transformation is rapidly underway, leading to a dramatic transition in cyberspace. The increased use of the Internet for transferring digital data from one system to another attracts cybercriminals to launch a variety of cyberattacks with new technologies. In addition, the exponential growth of data and large amount of traffic make the cyberspace more vulnerable to prolonged and automated cyberattacks. Furthermore, the emergence of new kind of attacks increases the complexity of attacks which consequently increases the security vulnerabilities making harder to defend against cyberattacks. To safeguard the cyberspace from unauthorized access and attacks, cybersecurity techniques which detect cyberattacks and react against them have been developed. However, the existing security systems are no longer sufficient for protecting the cyberspace as the cybercriminals are very intelligent in evading the conventional security mechanisms with new technologies. Considering these drawbacks, the cybersecurity techniques must be more intelligent, flexible, and robust in order to detect various cyberattacks and cybercrimes. This chapter discusses two emerging technologies, namely artificial intelligence (AI) and machine learning (ML) for enhancing cybersecurity. These techniques combat cyberattacks and protect the cyberspace effectively. The use of AI techniques automatically detects the threats and handles them effectively, whereas ML techniques recognize the patterns of suspicious activities and identify the threats. These techniques have abilities to analyze huge amount of data, detect anomalies and handle threats, and therefore offer promising solutions to enhance cybersecurity in real time. As AI and ML are the most inherent techniques in cybersecurity, this chapter explores the potential of different AI and ML approaches for enhancing cybersecurity.

Abstract

With the recent technological advancement of ICT industry and proliferation of networked systems including the Internet, digital transformation is rapidly underway, leading to a dramatic transition in cyberspace. The increased use of the Internet for transferring digital data from one system to another attracts cybercriminals to launch a variety of cyberattacks with new technologies. In addition, the exponential growth of data and large amount of traffic make the cyberspace more vulnerable to prolonged and automated cyberattacks. Furthermore, the emergence of new kind of attacks increases the complexity of attacks which consequently increases the security vulnerabilities making harder to defend against cyberattacks. To safeguard the cyberspace from unauthorized access and attacks, cybersecurity techniques which detect cyberattacks and react against them have been developed. However, the existing security systems are no longer sufficient for protecting the cyberspace as the cybercriminals are very intelligent in evading the conventional security mechanisms with new technologies. Considering these drawbacks, the cybersecurity techniques must be more intelligent, flexible, and robust in order to detect various cyberattacks and cybercrimes. This chapter discusses two emerging technologies, namely artificial intelligence (AI) and machine learning (ML) for enhancing cybersecurity. These techniques combat cyberattacks and protect the cyberspace effectively. The use of AI techniques automatically detects the threats and handles them effectively, whereas ML techniques recognize the patterns of suspicious activities and identify the threats. These techniques have abilities to analyze huge amount of data, detect anomalies and handle threats, and therefore offer promising solutions to enhance cybersecurity in real time. As AI and ML are the most inherent techniques in cybersecurity, this chapter explores the potential of different AI and ML approaches for enhancing cybersecurity.

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/10.1515/9783111712895-017

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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