Foundations of Hybrid Machine Learning Techniques in Cybersecurity for Robust Data Protection
Karthiga R, A. Geethapriya, M. D. Boomija · 2025
The ever-evolving landscape of cybersecurity demands advanced and adaptive solutions to effectively combat sophisticated cyber threats. Hybrid machine learning (ML) techniques have emerged as a powerful approach to enhancing the robustness of Intrusion Detection Systems (IDS) and threat mitigation strategies. By combining the strengths of multiple learning algorithms, hybrid ML models offer superior detection capabilities, adaptability, and accuracy compared to traditional methods. This chapter explores the foundations of hybrid ML techniques in cybersecurity, focusing on their application in intrusion detection and threat mitigation. Key algorithms, including ensemble learning, support vector machines, and deep learning, are discussed in detail, alongside their integration to address challenges such as class imbalance, model accuracy, and evolving cyber threats. Case studies highlight the real-world effectiveness of these models in diverse cybersecurity domains, demonstrating their potential to enhance threat detection, reduce false positives, and provide dynamic responses to emerging threats. The chapter also delves into advanced methods for overcoming data imbalance, emphasizing the role of cost-sensitive learning and synthetic data generation. By providing a comprehensive overview of hybrid ML applications, this chapter underscores the transformative potential of these techniques in shaping the future of cybersecurity.