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.
The evolution of cyber threats has necessitated the continuous development of advanced cybersecurity systems capable of adapting to new and emerging risks [1]. Cybercriminals are becoming increasingly sophisticated, employing advanced attack techniques such as zero-day exploits, ransomware, and insider threats, which often go undetected by traditional security systems [2]. In this context, machine learning (ML) and artificial intelligence (AI) have become critical tools for enhancing cybersecurity defenses [3]. Conventional ML models, which typically rely on large labeled datasets for training, face significant limitations in dynamic environments where threats evolve rapidly, and labeled data is scarce or unavailable [4]. Transfer learning, a technique that allows knowledge from one domain to be transferred to another, has emerged as a promising solution for addressing these challenges [5]. By enabling cybersecurity systems to learn from existing models and apply that knowledge to new, unseen domains, transfer learning facilitates enhanced detection and response capabilities across various environments [6].
Transfer learning, particularly in multi-domain cybersecurity, offers the ability to improve the performance and adaptability of machine learning models by leveraging knowledge gained from one domain to boost the model’s efficiency in another [7]. Multi-domain cybersecurity environments often encompass a variety of industries, systems, and data sources, each with unique attack vectors and security challenges [8]. Transfer learning allows cybersecurity models to generalize across these different environments, enhancing the overall threat detection process [9]. The key advantage of this approach is that it reduces the need for extensive labeled data in every new domain, making it more efficient and effective in rapidly adapting to new attack scenarios [10]. This capability significantly improves the robustness and scalability of cybersecurity solutions, which is crucial for addressing the increasingly complex and dynamic nature of cyber threats [11].
