Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection
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Dr. R. Pushpa Lakshmi
Abstract
Globally, organizations faced an average of 1,876 cyberattacks per week in 2024, marking a 75% increase from 2023. Ransom demands surged by 40%, reaching nearly $2 million per incident, while data breaches exposed over 15 billion records – a 38% rise. Cybercriminals increasingly use artificial intelligence (AI) to automate attacks and craft sophisticated phishing emails, amplifying the threat landscape. These trends highlight the urgent need for strong and adaptive cybersecurity defences. Machine learning (ML) and deep learning (DL) have emerged as key technologies, enabling dynamic and intelligent security systems. This study reviews ML- and DL-based cybersecurity approaches, focusing on their application in threat intelligence, phishing and malware detection, fraud detection, and intrusion prevention systems. DL models are also used in behavioral biometrics, multimedia security, and cyber threat hunting. By reducing false positives and enhancing detection speed, ML and DL improve the accuracy and efficiency of modern cybersecurity frameworks.
Abstract
Globally, organizations faced an average of 1,876 cyberattacks per week in 2024, marking a 75% increase from 2023. Ransom demands surged by 40%, reaching nearly $2 million per incident, while data breaches exposed over 15 billion records – a 38% rise. Cybercriminals increasingly use artificial intelligence (AI) to automate attacks and craft sophisticated phishing emails, amplifying the threat landscape. These trends highlight the urgent need for strong and adaptive cybersecurity defences. Machine learning (ML) and deep learning (DL) have emerged as key technologies, enabling dynamic and intelligent security systems. This study reviews ML- and DL-based cybersecurity approaches, focusing on their application in threat intelligence, phishing and malware detection, fraud detection, and intrusion prevention systems. DL models are also used in behavioral biometrics, multimedia security, and cyber threat hunting. By reducing false positives and enhancing detection speed, ML and DL improve the accuracy and efficiency of modern cybersecurity frameworks.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
- Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
- Chapter 3 Data Science for Threat Detection and Analysis 59
- Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
- Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
- Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
- Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
- Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
- Chapter 9 Light POW for Smart Grid Communication 201
- Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
- Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
- Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
- Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
- Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
- Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
- Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
- Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
- Chapter 18 Firewall and IDS in Cybersecurity 439
- Index
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
- Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
- Chapter 3 Data Science for Threat Detection and Analysis 59
- Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
- Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
- Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
- Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
- Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
- Chapter 9 Light POW for Smart Grid Communication 201
- Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
- Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
- Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
- Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
- Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
- Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
- Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
- Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
- Chapter 18 Firewall and IDS in Cybersecurity 439
- Index
