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Chapter 3 Data Science for Threat Detection and Analysis

  • Bryan Bovas Mathew , Vignesh Krishna Vajha and Ruchi Jakhmola Mani
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Cybersecurity Unlocked
This chapter is in the book Cybersecurity Unlocked

Abstract

This chapter explores the applications of data science techniques that enhance cybersecurity. It provides a theoretical analysis based on existing literature, focusing on how tools like machine learning, deep learning, and natural language processing help contribute to threat detection, analysis, and prevention. By examining user behavior and device interactions, the chapter highlights important machine learning and deep learning techniques that are used to identify these interactions, helping us to classify them as benign behavior or malicious behavior. Typically, these techniques are used in identifying DDoS attacks, malware, and insider threats. This chapter also emphasizes the need for real-time threat detection systems and integrated systems with highly accurate models for predictions. Additionally, we delve into challenges that arise when trying to implement data science techniques in this field while also explaining what limitations the model themselves faces. Furthermore, we learn about future trends and how integrating and developing better techniques and technologies can help strengthen our defenses while allowing for real time response. On doing so we are able to shift from reactive measures to proactive measures.

Abstract

This chapter explores the applications of data science techniques that enhance cybersecurity. It provides a theoretical analysis based on existing literature, focusing on how tools like machine learning, deep learning, and natural language processing help contribute to threat detection, analysis, and prevention. By examining user behavior and device interactions, the chapter highlights important machine learning and deep learning techniques that are used to identify these interactions, helping us to classify them as benign behavior or malicious behavior. Typically, these techniques are used in identifying DDoS attacks, malware, and insider threats. This chapter also emphasizes the need for real-time threat detection systems and integrated systems with highly accurate models for predictions. Additionally, we delve into challenges that arise when trying to implement data science techniques in this field while also explaining what limitations the model themselves faces. Furthermore, we learn about future trends and how integrating and developing better techniques and technologies can help strengthen our defenses while allowing for real time response. On doing so we are able to shift from reactive measures to proactive measures.

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/10.1515/9783111712895-003

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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