Transformer Models and Attention Mechanisms for Intelligent Cyber Threat Intelligence Extraction

The rapid evolution of cyber threats necessitates the development of advanced detection systems capable of adapting to dynamic attack patterns. This chapter explores the integration of transformer models and attention mechanisms in the realm of cybersecurity, particularly for
anomaly detection and cyber threat intelligence extraction. Transformer models, renowned for their ability to capture long-range dependencies and contextual relationships within sequential data, are increasingly leveraged in cybersecurity to identify complex, previously unseen threats.
Attention mechanisms enhance these models by allowing them to focus on critical features in large,noisy datasets, improving the interpretability and decision-making capabilities of automated systems. However, challenges persist in the application of these models, including the handling of real-time data streams, the interpretability of attention maps, and the management of false positives
and false negatives. The chapter delves into the implications of these challenges and proposes strategies for overcoming them, such as integrating explainable AI (XAI) methods and refining feature attribution techniques. By examining the evolving landscape of cyber threats and presenting case studies, this work highlights the crucial role of explainability in enhancing human- model interaction, trust, and decision-making. The chapter provides a comprehensive analysis of the strengths, limitations, and future directions for transformer-based anomaly detection systems, offering valuable insights for both researchers and cybersecurity practitioners.