Transfer Learning for Rapid Deployment of Predictive Models in Dynamic Security Environments
Senthil J, D Shobana, B Vishnu Prabh · 2025
Transfer learning has emerged as a transformative approach in the rapid deployment of predictive models within dynamic security environments, offering significant advantages in adapting pre-trained models to novel, domain-specific tasks. The effectiveness of transfer learning models was challenged by several factors, including domain shift, adversarial attacks, data privacy concerns, and the need for real-time adaptability. This chapter provides an in-depth exploration of the key considerations and methodologies for evaluating transfer learning models in security contexts. It highlights critical aspects such as benchmarking model performance under domain shift, the ethical balancing of accuracy and privacy, and the integration of adversarial defenses. Additionally, the chapter discusses metrics for assessing generalization and adaptability across diverse security tasks, as well as the scalability and flexibility of transfer learning models in incorporating real-time data streams. By focusing on these multifaceted challenges, this work contributes to the growing body of knowledge aimed at enhancing the robustness, security, and efficiency of transfer learning models in dynamic and evolving security environments. Key areas such as domain shift, adversarial defenses, model generalization, real-time adaptation, data privacy, and transfer learning scalability are critically examined, providing a comprehensive framework for future research and development in this field.