Application of Transfer Learning in Multi-Domain Hybrid Cybersecurity Solutions
D Shobana · 2025
The increasing sophistication and frequency of cyber-attacks necessitate advanced solutions for cybersecurity that can adapt to new and evolving threats across multiple domains. Transfer learning, a machine learning technique that leverages knowledge from one domain to enhance learning in another, holds significant promise in improving the effectiveness of multi-domain cybersecurity systems. This chapter explores the application of transfer learning in the context of cybersecurity, with a specific focus on its role in addressing domain adaptation challenges, detecting emerging threats, and improving the robustness of security systems across diverse environments. Key methodologies, such as semi-supervised and unsupervised transfer learning, are discussed, along with their practical applications in real-world cybersecurity scenarios. The chapter examines the use of clustering techniques to enhance knowledge transfer, allowing for better detection and classification of novel attack patterns in previously unseen domains. Its potential, the integration of transfer learning into cybersecurity is accompanied by challenges, particularly with regard to privacy, security concerns, and the adaptation of models to dynamic threat landscapes. This chapter provides insights into these challenges, offering potential solutions for overcoming them and optimizing the use of transfer learning in cybersecurity. The findings presented aim to bridge existing gaps in research, promoting the development of adaptive, efficient, and secure cybersecurity systems capable of responding to the evolving threat landscape.