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Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity

  • S. Hemalatha , R. Thangarajan , K. Sakthivel and K. Karthikeyan
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Cybersecurity Unlocked
This chapter is in the book Cybersecurity Unlocked

Abstract

The way risks are identified, evaluated, and reduced has completely changed as a result of the incorporation of artificial intelligence (AI) and machine learning (ML) into cybersecurity. As cyberattacks grow in complexity and volume, conventional security techniques frequently fall behind. By utilizing clever algorithms that can process enormous volumes of data in real time, AI and ML provide a proactive approach to cybersecurity. These technologies enhance threat detection by finding trends, irregularities, and weaknesses that traditional approaches might miss. Behavioral analysis and anomaly detection powered by ML enable systems to track user and network activity and identify any questionable behavior and reducing response times to potential breaches. Additionally, AI-driven automation streamlines incident response, prioritizing threats and suggesting remediation strategies with minimal human intervention. Despite their transformative potential, these technologies come with obstacles including adversarial assaults, algorithmic biases, false positives, and moral dilemmas with regard to data privacy and appropriate AI use. To properly utilize AI and ML in cybersecurity, these problems must be resolved. The chapter continues with an examination of new developments that could improve the field’s progress, such as explainable AI, federated learning, and the function of quantum computing. With an emphasis on existing applications, difficulties, and potential future directions, this chapter offers a thorough analysis of AI and ML in cybersecurity.

Abstract

The way risks are identified, evaluated, and reduced has completely changed as a result of the incorporation of artificial intelligence (AI) and machine learning (ML) into cybersecurity. As cyberattacks grow in complexity and volume, conventional security techniques frequently fall behind. By utilizing clever algorithms that can process enormous volumes of data in real time, AI and ML provide a proactive approach to cybersecurity. These technologies enhance threat detection by finding trends, irregularities, and weaknesses that traditional approaches might miss. Behavioral analysis and anomaly detection powered by ML enable systems to track user and network activity and identify any questionable behavior and reducing response times to potential breaches. Additionally, AI-driven automation streamlines incident response, prioritizing threats and suggesting remediation strategies with minimal human intervention. Despite their transformative potential, these technologies come with obstacles including adversarial assaults, algorithmic biases, false positives, and moral dilemmas with regard to data privacy and appropriate AI use. To properly utilize AI and ML in cybersecurity, these problems must be resolved. The chapter continues with an examination of new developments that could improve the field’s progress, such as explainable AI, federated learning, and the function of quantum computing. With an emphasis on existing applications, difficulties, and potential future directions, this chapter offers a thorough analysis of AI and ML in 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/10.1515/9783111712895-013

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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